diff --git a/.DS_Store b/.DS_Store index f9317dbf..d47fc5f1 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/blog/atmosphere/index.html b/blog/atmosphere/index.html index 9904fa7b..3135702c 100644 --- a/blog/atmosphere/index.html +++ b/blog/atmosphere/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Atmosphere

    🚧 Under Development 🚧

    • This project is supported by Schmidt Sciences, LLC.

    Atmosphere

    🚧 Under Development 🚧

    Atmosphere

    Parameterization of moist process in the atmosphere
    People involved: Paul O’Gorman, Griffin Mooers, Pierre Gentine

    Much of the uncertainty in climate-model projections for surface precipitation and winds comes from the need to parameterize subgrid processes such as moist convection. We are developing and implementing new parameterizations for these processes using machine learning trained on high-resolution simulations. We aim to develop parameterizations that are robust, stable and physically consistent. See Yuval and O’Gorman 2020 and Yuval, O’Gorman and Hill 2021 for examples of these research projects.

    Figure: Structure of a machine-learning parameterization of subgrid moist processes in the atmosphere. The structure is chosen so that the parameterization conserves energy and water.

    Parameterization of the boundary layer
    People involved: Alexander Connolly, Pierre Gentine

    Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/climate/index.html b/blog/climate/index.html index df2a2ad1..9f538afe 100644 --- a/blog/climate/index.html +++ b/blog/climate/index.html @@ -8,6 +8,7 @@
  • Resources
  • News

    • Climate Model Development

    🚧 Under Development 🚧

    • This project is supported by Schmidt Sciences, LLC.

    Climate Model Development

    Comprehensive climate models typically include atmosphere, ocean, sea ice, and land components and the coupling between them. These models can also incorporate land ice, atmospheric chemistry and terrestrial and marine biogeochemistry, enabling carbon cycle simulations. Early climate models were developed in the 1970s and have increased in complexity over the years, with more process interactions, more sophisticated parameterizations of subgridscale processes, and higher spatial resolution being incorporated over time. These models have been skillful at predicting anthropogenic climate change, and even early models accurately simulated aspects of the spatial pattern of warming. However, there is still considerable uncertainty associated with model structure, and climate models which incorporate different parameterizations can differ greatly in many characteristics of future projected change. Because of this, it is imperative that there are continued developments and improvements of these modeling systems. Bringing new approaches, such as Machine Learning, to this challenge has the potential to rapidly accelerate progress.

    Studies across M²LInES are using scientific and interpretable Machine Learning to gain new insight on parameterization development across the atmosphere, ocean, and sea ice systems. Development and testing of these parameterizations is underway in component model configurations and work is planned to incorporate these into a number of climate models. This includes, among others, improved parameterizations of:

    • The simulated conductive heat fluxes through sea ice (Zampieri et al, 2024 +),
    • Ocean mixing processes,
    • Moist convection in the atmosphere
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/climateprocess/index.html b/blog/climateprocess/index.html index b4aaf377..1466d730 100644 --- a/blog/climateprocess/index.html +++ b/blog/climateprocess/index.html @@ -8,9 +8,16 @@
  • Resources
  • News

    • Key Climate Processes

    Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. -M²LInES will be focusing on understanding these key climate processes using two types of data:

    1. Data from high-resolution simulation and observations.
    2. Data resulting from model errors, also called data assimilation increments.

    Below is a representation of the physical processes that will be studied by the team:

    title

    Learn more: +

    Key Climate Processes

    Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level.

    M²LInES will be focusing on understanding these key climate processes using two types of data:

    High-resolution simulation and observations

    • Atmospheric convection and clouds (O’Gorman, Mooers, Yuval) (see Atmosphere +)
    • Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) (see Atmosphere +)
    • Ocean mesoscale buoyancy fluxes (Balwada, Everard) (see Ocean +)
    • Ocean submesoscale processes (Le Sommer, Barge) (see Ocean +)
    • Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) (see Ocean +)
    • Vertical mixing (Adcroft, Reichl, Sane) (see Ocean +)
    • Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri) (see Sea-Ice +)

    Data assimilation increments

    Data assimilation increments +or DA are data resulting from model errors. We will be working on DA from 3 distinct parts of climate models:

    • Atmospheric (Berner, Chapman)
    • Ocean (Adcroft, Lu, Du)
    • Sea-Ice (Adcroft, Bushuk, Gregory)

    Below is a representation of the physical processes that will be studied by the team:

    title

    Learn more: Come discover Pangeo Forge and how it can help us solve complex problems in climate and weather research here -with Ryan Abernathey, head of big data at M²LInES.

    • This project is supported by Schmidt Sciences, LLC.
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/coupledphysics/index.html b/blog/coupledphysics/index.html index 8495e183..2c9b1644 100644 --- a/blog/coupledphysics/index.html +++ b/blog/coupledphysics/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Coupled Physics

    Developing new physics-aware machine learning tools

    🚧 Under Development 🚧

    • This project is supported by Schmidt Sciences, LLC.

    Coupled Physics

    The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of:

    • Atmosphere
    • Land
    • Land Ice
    • Ocean
    • River Runoff
    • Sea Ice
    • Wave

    In addition to improving each component independently, it is also important to better understand and model the coupling of components.

    Take the air-sea interaction as an example. The large-scale dynamics of coupled physics in air-sea interactions involve the exchange of energy, moisture, and momentum between the atmosphere and the ocean. These interactions play a crucial role in regulating climate patterns, driving ocean circulation, and influencing weather systems globally.

    Heat Exchange:

    • The transfer of heat from the ocean to the atmosphere affects temperature gradients and drives atmospheric circulation.
    • For example, the Southern Ocean’s heat uptake plays a critical role in absorbing heat from the atmosphere, influencing global climate patterns, and helping to regulate the Earth’s climate system.

    Momentum Transfer:

    • Wind stress over large oceanic areas generates surface currents and contributes to the overall momentum exchange between the ocean and atmosphere.
    • This process impacts circulation patterns and storm development.

    Coupled Feedback Mechanisms:

    • Large-scale air-sea interactions are central to climate feedback, such as the El Niño-Southern Oscillation (ENSO).
    • ENSO represents complex feedback between the tropical Pacific Ocean and the atmosphere, which significantly influences global climate patterns.

    Climate Regulation and Teleconnections:

    • These interactions contribute to teleconnections, where climate changes in one region can affect distant regions.
    • Large-scale coupling helps balance heat and energy across latitudes, playing a critical role in Earth’s climate regulation.

    In M²LInES, there are projects using observational data to better model these air-sea fluxes, in collaboration with scientists from the NSF STC LEAP. Several other projects also focus on the study of large-scale processes, which enhance our understanding of climate variability and change. This ultimately leads to improvements in climate model accuracy and better predictions for the earth system as a whole.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/index.html b/blog/index.html index 29838d21..c8e0cffd 100644 --- a/blog/index.html +++ b/blog/index.html @@ -8,7 +8,7 @@
  • Resources
  • News

    • M²LInES Blog

    Coming soon!

    Atmosphere

    🚧 Under Development 🚧

    Climate Model Development

    🚧 Under Development 🚧

    Coupled Physics

    Developing new physics-aware machine learning tools

    🚧 Under Development 🚧

    Data Assimilation

    Climate models & observations

    Climate models are powerful tools to simulate and predict the Earth’s climate system, but...

    Key Climate Processes

    Despite drastic improvements in climate model development, current simulations have difficulty capturing the...

    Machine Learning

    Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and...

    Ocean

    Ocean modeling and turbulence

    Figure: The ocean circulation is composed of many coherent features distributed over a...

    Project Goals and Vision

    Project Goal

    We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable...

    Sea Ice

    🚧 Under Development 🚧

    Developing new physics-aware machine learning tools

    Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop...

    High-resolution simulations and observational data

    We will diagnose small-scale physical processes such as:

    • Atmospheric convection and clouds (O’Gorman, Yuval)...

    Subscribe to our newsletter

    * indicates required

    M²LInES Blog

    Coming soon!

    Atmosphere

    🚧 Under Development 🚧

    Atmosphere

    Parameterization of moist process in the atmosphere
    People involved: Paul O’Gorman,...

    Climate Model Development

    Comprehensive climate models typically include atmosphere, ocean, sea ice, and land components and the coupling between...

    Coupled Physics

    The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in...

    Data Assimilation

    Climate models & observations

    Climate models are powerful tools to simulate and predict the Earth’s climate system, but...

    Key Climate Processes

    Despite drastic improvements in climate model development, current simulations have difficulty capturing the...

    Machine Learning

    Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and...

    Ocean

    Ocean modeling and turbulence

    Figure: The ocean circulation is composed of many coherent features distributed over a...

    Project Goals and Vision

    Project Goal

    We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable...

    Sea Ice

    Sea Ice

    Figure: Snapshot of Arctic sea ice thickness (Greenland, Barents and Kara Seas) from a CM4 simulation...

    Developing new physics-aware machine learning tools

    Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop...

    High-resolution simulations and observational data

    We will diagnose small-scale physical processes such as:

    • Atmospheric convection and clouds (O’Gorman, Yuval)...

    Subscribe to our newsletter

    * indicates required
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/index.xml b/blog/index.xml index 4509e434..7ec915a7 100644 --- a/blog/index.xml +++ b/blog/index.xml @@ -1,7 +1,10 @@ -Blog on M²LInEShttps://m2lines.github.io/blog/Recent content in Blog on M²LInESHugo -- gohugo.ioen-usAtmospherehttps://m2lines.github.io/blog/atmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/atmosphere/🚧 Under Development 🚧Climate Model Developmenthttps://m2lines.github.io/blog/climate/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climate/🚧 Under Development 🚧Coupled Physicshttps://m2lines.github.io/blog/coupledphysics/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/coupledphysics/Developing new physics-aware machine learning tools 🚧 Under Development 🚧Data Assimilationhttps://m2lines.github.io/blog/dataassimilation/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/dataassimilation/Climate models & observations Climate models are powerful tools to simulate and predict the Earth’s climate system, but they aren’t without flaws. Due to missing physics, imperfect parameterizations, and inaccuracies in the underlying numerics, climate models often show structural errors. On the other hand we have direct observations of the climate system. However, observations come with their own set of limitations. They have limited space and time coverage and also contain errors due to measurement noise.Key Climate Processeshttps://m2lines.github.io/blog/climateprocess/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climateprocess/Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. M²LInES will be focusing on understanding these key climate processes using two types of data: -Data from high-resolution simulation and observations. Data resulting from model errors, also called data assimilation increments.Machine Learninghttps://m2lines.github.io/blog/machinelearning/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/machinelearning/Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and function of the human brain. These algorithms are capable of learning from data, identifying patterns, and making predictions or decisions without being explicitly programmed. Neural networks consist of interconnected nodes organized into layers, including input, hidden, and output layers. During training, they adjust the strength of connections between nodes, called weights, based on examples from a dataset, allowing them to learn complex patterns and relationships in the data.Oceanhttps://m2lines.github.io/blog/ocean/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/ocean/Ocean modeling and turbulence Figure: The ocean circulation is composed of many coherent features distributed over a wide range of scales. Snapshot of surface relative vorticity (swirliness) from a global 1/10 degree simulation with MOM6, showing the presence of ocean eddies, sharp boundary currents, tropical instability waves, shelf circulations, etc. -The ocean is a turbulent fluid, in which motions on a wide range of scales – from centimeters to thousands of kilometers – interact and exchange energy.Project Goals and Visionhttps://m2lines.github.io/blog/projectgoals/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/projectgoals/Project Goal We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes. -Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations.Sea Icehttps://m2lines.github.io/blog/seaice/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/seaice/🚧 Under Development 🚧Developing new physics-aware machine learning toolshttps://m2lines.github.io/blog/research2/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research2/Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop Machine Learning algorithms that are physics- and scale-aware to accelerate climate science discovery . +Blog on M²LInEShttps://m2lines.github.io/blog/Recent content in Blog on M²LInESHugo -- gohugo.ioen-usAtmospherehttps://m2lines.github.io/blog/atmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/atmosphere/🚧 Under Development 🚧 +Atmosphere Parameterization of moist process in the atmosphere People involved: Paul O’Gorman, Griffin Mooers, Pierre Gentine Much of the uncertainty in climate-model projections for surface precipitation and winds comes from the need to parameterize subgrid processes such as moist convection. We are developing and implementing new parameterizations for these processes using machine learning trained on high-resolution simulations. We aim to develop parameterizations that are robust, stable and physically consistent.Climate Model Developmenthttps://m2lines.github.io/blog/climate/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climate/Comprehensive climate models typically include atmosphere, ocean, sea ice, and land components and the coupling between them. These models can also incorporate land ice, atmospheric chemistry and terrestrial and marine biogeochemistry, enabling carbon cycle simulations. Early climate models were developed in the 1970s and have increased in complexity over the years, with more process interactions, more sophisticated parameterizations of subgridscale processes, and higher spatial resolution being incorporated over time. These models have been skillful at predicting anthropogenic climate change, and even early models accurately simulated aspects of the spatial pattern of warming.Coupled Physicshttps://m2lines.github.io/blog/coupledphysics/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/coupledphysics/The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of: +Atmosphere Land Land Ice Ocean River Runoff Sea Ice Wave In addition to improving each component independently, it is also important to better understand and model the coupling of components.Data Assimilationhttps://m2lines.github.io/blog/dataassimilation/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/dataassimilation/Climate models & observations Climate models are powerful tools to simulate and predict the Earth’s climate system, but they aren’t without flaws. Due to missing physics, imperfect parameterizations, and inaccuracies in the underlying numerics, climate models often show structural errors. On the other hand we have direct observations of the climate system. However, observations come with their own set of limitations. They have limited space and time coverage and also contain errors due to measurement noise.Key Climate Processeshttps://m2lines.github.io/blog/climateprocess/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climateprocess/Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. +M²LInES will be focusing on understanding these key climate processes using two types of data: +High-resolution simulation and observations Atmospheric convection and clouds (O’Gorman, Mooers, Yuval) (see Atmosphere ) Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) (see Atmosphere ) Ocean mesoscale buoyancy fluxes (Balwada, Everard) (see Ocean ) Ocean submesoscale processes (Le Sommer, Barge) (see Ocean ) Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) (see Ocean ) Vertical mixing (Adcroft, Reichl, Sane) (see Ocean ) Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri) (see Sea-Ice ) Data assimilation increments Data assimilation increments or DA are data resulting from model errors.Machine Learninghttps://m2lines.github.io/blog/machinelearning/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/machinelearning/Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and function of the human brain. These algorithms are capable of learning from data, identifying patterns, and making predictions or decisions without being explicitly programmed. Neural networks consist of interconnected nodes organized into layers, including input, hidden, and output layers. During training, they adjust the strength of connections between nodes, called weights, based on examples from a dataset, allowing them to learn complex patterns and relationships in the data.Oceanhttps://m2lines.github.io/blog/ocean/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/ocean/Ocean modeling and turbulence Figure: The ocean circulation is composed of many coherent features distributed over a wide range of scales. Snapshot of surface relative vorticity (swirliness) from a global 1/10 degree simulation with MOM6, showing the presence of ocean eddies, sharp boundary currents, tropical instability waves, shelf circulations, etc. +The ocean is a turbulent fluid, in which motions on a wide range of scales – from centimeters to thousands of kilometers – interact and exchange energy.Project Goals and Visionhttps://m2lines.github.io/blog/projectgoals/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/projectgoals/Project Goal We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes. Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations.Sea Icehttps://m2lines.github.io/blog/seaice/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/seaice/Sea Ice Figure: Snapshot of Arctic sea ice thickness (Greenland, Barents and Kara Seas) from a CM4 simulation (⅛-degree ocean and sea ice). The Fram strait (between Greenland and Svalbard) exports approximately 10% of the total Northern Hemisphere sea ice cover out of the Arctic basin annually. +Sea ice is the thin layer of frozen ocean that exists at the high latitudes, forming when ocean temperatures are sufficiently cooled by the atmosphere.Developing new physics-aware machine learning toolshttps://m2lines.github.io/blog/research2/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research2/Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop Machine Learning algorithms that are physics- and scale-aware to accelerate climate science discovery . Our transformative methodology will rely on a range of ML algorithms (e.g., convolutional neural networks, equation-discovery), including with strict physical constraints, or with embeded physics for improved interpretability, and generalizations. Those ML models will be used to deepen our understanding of subgrid processes, and will be implemented as subgrid parameterizations or as bias corrections, in coarser resolution ocean (NEMO and MOM6) and coupled climate models (IPSLCM, CM4, CESM).High-resolution simulations and observational datahttps://m2lines.github.io/blog/research4/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research4/We will diagnose small-scale physical processes such as: Atmospheric convection and clouds (O’Gorman, Yuval) Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) Ocean mesoscale buoyancy fluxes (Abernathey, Balwada) Ocean submesoscale processes (Le Sommer, Gorbunova) Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) Vertical mixing (Adcroft, Reichl, Sane) Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri) \ No newline at end of file diff --git a/blog/projectgoals/index.html b/blog/projectgoals/index.html index 4a6a4441..38a88fce 100644 --- a/blog/projectgoals/index.html +++ b/blog/projectgoals/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Project Goals and Vision

    Project Goal

    We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes.

    Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models’ numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.

    To tackle these biases and reduce the potential sources of error, M²LInES’ strategy is to leverage advances in machine learning & “interrogate” the data to

    1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
    2. Produce error corrections derived from observational products for climate model components.

    By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.

    Our vision

    1. ​​Generate new scientific knowledge in climate science from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?
    2. Accelerate end-to-end, from development to delivery, for a new generation of climate models; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.
    3. Drive a change of direction in the field by building models and tools centered around data-driven methods for the community to advance climate science discovery.
    4. Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling.
    • This project is supported by Schmidt Sciences, LLC.

    Project Goals and Vision

    Project Goal

    We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes.

    Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models’ numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.

    To tackle these biases and reduce the potential sources of error, M²LInES’ strategy is to leverage advances in machine learning & “interrogate” the data to:

    1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
    2. Produce error corrections derived from observational products for climate model components.

    By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.

    Our vision

    💡 Generate new scientific knowledge in climate science from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?

    💻 Accelerate end-to-end, from development to delivery, for a new generation of climate models; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.

    ⚙️ Drive a change of direction in the field by building models and tools centered around data-driven methods for the community to advance climate science discovery.

    👩‍🏫 Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/blog/seaice/index.html b/blog/seaice/index.html index 4439afef..44a93f41 100644 --- a/blog/seaice/index.html +++ b/blog/seaice/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Sea Ice

    🚧 Under Development 🚧

    • This project is supported by Schmidt Sciences, LLC.

    Sea Ice

    Sea Ice

    Figure: Snapshot of Arctic sea ice thickness (Greenland, Barents and Kara Seas) from a CM4 simulation (⅛-degree ocean and sea ice). The Fram strait (between Greenland and Svalbard) exports approximately 10% of the total Northern Hemisphere sea ice cover out of the Arctic basin annually.

    Sea ice is the thin layer of frozen ocean that exists at the high latitudes, forming when ocean temperatures are sufficiently cooled by the atmosphere. It plays a major role in the Earth’s climate and ecosystems, reflecting incoming solar radiation to keep surface temperatures cool, regulating large-scale ocean currents through the re-circulation of salt and nutrients, providing an integral platform for connecting Arctic communities, and also a natural habitat for endemic species. Sea ice loss due to anthropogenic climate change is posing a threat to the balance of these various human and natural systems. Over the past decade for example, the average summer Arctic sea ice cover was more than 60% lower than in the 1980s.

    The importance of sea ice in the climate system was illustrated in the seminal works of Manabe & Stouffer in 1980, who showed that, due to positive ice-albedo feedbacks, the dominant climate response to quadrupling CO₂ is the seasonal loss of the Arctic sea ice cover. Even before this however there had been concerted efforts to understand and model the physical processes that control sea ice evolution – efforts which would subsequently lay the foundations for nearly all sea ice models over the next 4-5 decades! For example, all of the latest-generation (CMIP6) climate models still use the ice thickness distribution formulation devised by Thorndike in 1975, as well as some variation of the viscous-plastic sea ice rheology scheme developed by Hibler in 1979. Since the 1970s however, there have been several important sea ice physics developments, including: an energy-conserving model for sea ice thermodynamics (Bitz & Lipscombe, 1999), the incorporation of multiple scattering sea ice radiative transfer (Briegleb and Light 2007; Holland et al 2012), the formulation for sea ice surface melt ponds (Flocco et al 2012; Hunke et al 2013), and also recent work on sea ice rheology to better represent damage mechanics (Maxwell Elasto-Brittle rheology; Dansereau et al., 2016).

    Although sea ice models are continuously improving, there is still work to do. We can see this in the figure below, which shows the sea ice extent climatology of the GFDL SPEAR (1-degree) climate model in both the Arctic and Antarctic. Here we see that SPEAR generally has a good representation of the total Arctic sea ice cover. On the other hand, SPEAR’s Antarctic biases are more severe, with too little sea ice across the summer–fall seasons, and then too much sea ice in winter.

    Figure: Sea ice mean state (1979-2010) of the GFDL SPEAR large ensemble (historical simulation). Thin grey lines represent individual ensemble members and the black line is the ensemble mean. Shown for (top) Arctic, (bottom) Antarctic.

    Biases in the sea ice mean state are due to a myriad of factors, and are often difficult to isolate. Biases may originate from missing physics within the sea ice model itself (e.g., absence of a sea ice ridging scheme), or from errors in sea ice model parameters (e.g., sea ice albedo or snow thermal conductivity). Furthermore sea ice is strongly coupled to the atmosphere and ocean, hence biases in either one of these components can imprint on the sea ice.

    M²LInES is working to improve sea ice model biases by developing new data-driven sea ice model parameterization schemes. Recent highlights include work from M²LInES members Lorenzo Zampieri and Marika Holland, who used in-situ data from the recent MOSAiC expedition ( Multidisciplinary drifting Observatory for the Study of Arctic Climate) to derive a new parametric correction to sea ice and snow conductive heat fluxes within the CICE sea ice model. This work showed that simulations which do not account for local-scale sea ice and snow heterogeneity can under-estimate conductive heat fluxes through sea ice by up to 10% (see this work in GRL).

    Additional work by Will Gregory and Mitch Bushuk has shown that data assimilation can be used to extract the systematic component of sea ice model error within sub-grid model state variables, and subsequently that neural networks can learn this error very effectively (see work in JAMES). In follow-up work they showed that this neural network can be used to bias-correct sea ice conditions during online global ice-ocean simulations (see work in GRL), and outlined how this framework of data assimilation + machine learning can also be used to improve online generalization of machine learning models.
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/images/M2LInES_infographics2412.png b/images/M2LInES_infographics2412.png new file mode 100644 index 00000000..69b62861 Binary files /dev/null and b/images/M2LInES_infographics2412.png differ diff --git a/images/news/2025Chapman.png b/images/news/2025Chapman.png new file mode 100644 index 00000000..4908021e Binary files /dev/null and b/images/news/2025Chapman.png differ diff --git a/images/news/2411-Shi.png b/images/news/2411-Shi.png new file mode 100644 index 00000000..77cf9897 Binary files /dev/null and b/images/news/2411-Shi.png differ diff --git a/images/news/2411-Zampieri.png b/images/news/2411-Zampieri.png new file mode 100644 index 00000000..80891b78 Binary files /dev/null and b/images/news/2411-Zampieri.png differ diff --git a/images/publications/2025-Chapman2.png b/images/publications/2025-Chapman2.png new file mode 100644 index 00000000..8b419722 Binary files /dev/null and b/images/publications/2025-Chapman2.png differ diff --git a/images/publications/2312-Bodner.png b/images/publications/2312-Bodner.png new file mode 100644 index 00000000..eca609d7 Binary files /dev/null and b/images/publications/2312-Bodner.png differ diff --git a/images/publications/2410-Perezhogin.png b/images/publications/2410-Perezhogin.png new file mode 100644 index 00000000..da51ac2e Binary files /dev/null and b/images/publications/2410-Perezhogin.png differ diff --git a/images/research/Moistprocess.png b/images/research/Moistprocess.png new file mode 100644 index 00000000..297dac3c Binary files /dev/null and b/images/research/Moistprocess.png differ diff --git a/images/research/SPEAR.png b/images/research/SPEAR.png new file mode 100644 index 00000000..f9b4d83c Binary files /dev/null and b/images/research/SPEAR.png differ diff --git a/images/silas-baisch-ceITO2rlDgc-unsplash.jpg b/images/silas-baisch-ceITO2rlDgc-unsplash.jpg new file mode 100644 index 00000000..fe8513d2 Binary files /dev/null and b/images/silas-baisch-ceITO2rlDgc-unsplash.jpg differ diff --git a/images/team/AntoineNasser.jpg b/images/team/AntoineNasser.jpg new file mode 100644 index 00000000..06635713 Binary files /dev/null and b/images/team/AntoineNasser.jpg differ diff --git a/images/team/XavierLevine.jpeg b/images/team/XavierLevine.jpeg new file mode 100644 index 00000000..dcd1cd70 Binary files /dev/null and b/images/team/XavierLevine.jpeg differ diff --git a/index.html b/index.html index 8cd24022..c9d795c4 100644 --- a/index.html +++ b/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • M²LInES - Multiscale Machine Learning In Coupled Earth System Modeling

    M²LInES (pronounced M-square-lines) is an international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning to capture unaccounted physical processes at the air-sea-ice interface.

    About us

    Learn more about our project.

    More about us

    Our Research

    Deepening our understanding of key climate processes, such as ocean mixing, clouds, convection, using data

    Developing new physics-aware multiscale machine learning tools for accelerating climate science discovery

    Improving coupled climate models from leading modeling centers (NCAR, GFDL, and IPSL).
    More about our research
    infographic

    Our Team

    Our international team of dedicated researchers has expertise in Climate Science, Machine Learning and Numerical Modeling.

    Meet the team

    Subscribe to our newsletter

    Sign up for our newsletter and receive our latest updates.

    subscribe
    • This project is supported by Schmidt Sciences, LLC.

    M²LInES - Multiscale Machine Learning In Coupled Earth System Modeling

    M²LInES (pronounced M-square-lines) is an international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning to capture unaccounted physical processes at the air-sea-ice interface.

    About us

    Learn more about our project.

    More about us

    Our Research

    Deepening our understanding of key climate processes, such as ocean mixing, clouds, convection, using data

    Developing new physics-aware multiscale machine learning tools for accelerating climate science discovery

    Improving coupled climate models from leading modeling centers (NCAR, GFDL, and IPSL).
    More about our research
    infographic

    Our Team

    Our international team of dedicated researchers has expertise in Climate Science, Machine Learning and Numerical Modeling.

    Meet the team

    Subscribe to our newsletter

    Sign up for our newsletter and receive our latest updates.

    subscribe
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/index.xml b/index.xml index f7016329..7ca80065 100644 --- a/index.xml +++ b/index.xml @@ -1,10 +1,13 @@ -Home on M²LInEShttps://m2lines.github.io/Recent content in Home on M²LInESHugo -- gohugo.ioen-usWed, 02 Oct 2024 09:29:16 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYU, Courant Institute + Center for Data ScienceAtmospherehttps://m2lines.github.io/blog/atmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/atmosphere/🚧 Under Development 🚧Climate Model Developmenthttps://m2lines.github.io/blog/climate/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climate/🚧 Under Development 🚧Coupled Physicshttps://m2lines.github.io/blog/coupledphysics/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/coupledphysics/Developing new physics-aware machine learning tools 🚧 Under Development 🚧Data Assimilationhttps://m2lines.github.io/blog/dataassimilation/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/dataassimilation/Climate models & observations Climate models are powerful tools to simulate and predict the Earth’s climate system, but they aren’t without flaws. Due to missing physics, imperfect parameterizations, and inaccuracies in the underlying numerics, climate models often show structural errors. On the other hand we have direct observations of the climate system. However, observations come with their own set of limitations. They have limited space and time coverage and also contain errors due to measurement noise.Key Climate Processeshttps://m2lines.github.io/blog/climateprocess/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climateprocess/Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. M²LInES will be focusing on understanding these key climate processes using two types of data: -Data from high-resolution simulation and observations. Data resulting from model errors, also called data assimilation increments.Machine Learninghttps://m2lines.github.io/blog/machinelearning/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/machinelearning/Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and function of the human brain. These algorithms are capable of learning from data, identifying patterns, and making predictions or decisions without being explicitly programmed. Neural networks consist of interconnected nodes organized into layers, including input, hidden, and output layers. During training, they adjust the strength of connections between nodes, called weights, based on examples from a dataset, allowing them to learn complex patterns and relationships in the data.Oceanhttps://m2lines.github.io/blog/ocean/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/ocean/Ocean modeling and turbulence Figure: The ocean circulation is composed of many coherent features distributed over a wide range of scales. Snapshot of surface relative vorticity (swirliness) from a global 1/10 degree simulation with MOM6, showing the presence of ocean eddies, sharp boundary currents, tropical instability waves, shelf circulations, etc. -The ocean is a turbulent fluid, in which motions on a wide range of scales – from centimeters to thousands of kilometers – interact and exchange energy.Project Goals and Visionhttps://m2lines.github.io/blog/projectgoals/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/projectgoals/Project Goal We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes. -Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations.Sea Icehttps://m2lines.github.io/blog/seaice/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/seaice/🚧 Under Development 🚧Johanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYU, Courant InstituteDeveloping new physics-aware machine learning toolshttps://m2lines.github.io/blog/research2/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research2/Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop Machine Learning algorithms that are physics- and scale-aware to accelerate climate science discovery . +Home on M²LInEShttps://m2lines.github.io/Recent content in Home on M²LInESHugo -- gohugo.ioen-usSun, 01 Dec 2024 09:29:16 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYUAtmospherehttps://m2lines.github.io/blog/atmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/atmosphere/🚧 Under Development 🚧 +Atmosphere Parameterization of moist process in the atmosphere People involved: Paul O’Gorman, Griffin Mooers, Pierre Gentine Much of the uncertainty in climate-model projections for surface precipitation and winds comes from the need to parameterize subgrid processes such as moist convection. We are developing and implementing new parameterizations for these processes using machine learning trained on high-resolution simulations. We aim to develop parameterizations that are robust, stable and physically consistent.Climate Model Developmenthttps://m2lines.github.io/blog/climate/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climate/Comprehensive climate models typically include atmosphere, ocean, sea ice, and land components and the coupling between them. These models can also incorporate land ice, atmospheric chemistry and terrestrial and marine biogeochemistry, enabling carbon cycle simulations. Early climate models were developed in the 1970s and have increased in complexity over the years, with more process interactions, more sophisticated parameterizations of subgridscale processes, and higher spatial resolution being incorporated over time. These models have been skillful at predicting anthropogenic climate change, and even early models accurately simulated aspects of the spatial pattern of warming.Coupled Physicshttps://m2lines.github.io/blog/coupledphysics/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/coupledphysics/The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of: +Atmosphere Land Land Ice Ocean River Runoff Sea Ice Wave In addition to improving each component independently, it is also important to better understand and model the coupling of components.Data Assimilationhttps://m2lines.github.io/blog/dataassimilation/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/dataassimilation/Climate models & observations Climate models are powerful tools to simulate and predict the Earth’s climate system, but they aren’t without flaws. Due to missing physics, imperfect parameterizations, and inaccuracies in the underlying numerics, climate models often show structural errors. On the other hand we have direct observations of the climate system. However, observations come with their own set of limitations. They have limited space and time coverage and also contain errors due to measurement noise.Key Climate Processeshttps://m2lines.github.io/blog/climateprocess/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/climateprocess/Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. +M²LInES will be focusing on understanding these key climate processes using two types of data: +High-resolution simulation and observations Atmospheric convection and clouds (O’Gorman, Mooers, Yuval) (see Atmosphere ) Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) (see Atmosphere ) Ocean mesoscale buoyancy fluxes (Balwada, Everard) (see Ocean ) Ocean submesoscale processes (Le Sommer, Barge) (see Ocean ) Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) (see Ocean ) Vertical mixing (Adcroft, Reichl, Sane) (see Ocean ) Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri) (see Sea-Ice ) Data assimilation increments Data assimilation increments or DA are data resulting from model errors.Machine Learninghttps://m2lines.github.io/blog/machinelearning/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/machinelearning/Machine learning methodologies, particularly neural networks, are computer algorithms inspired by the structure and function of the human brain. These algorithms are capable of learning from data, identifying patterns, and making predictions or decisions without being explicitly programmed. Neural networks consist of interconnected nodes organized into layers, including input, hidden, and output layers. During training, they adjust the strength of connections between nodes, called weights, based on examples from a dataset, allowing them to learn complex patterns and relationships in the data.Oceanhttps://m2lines.github.io/blog/ocean/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/ocean/Ocean modeling and turbulence Figure: The ocean circulation is composed of many coherent features distributed over a wide range of scales. Snapshot of surface relative vorticity (swirliness) from a global 1/10 degree simulation with MOM6, showing the presence of ocean eddies, sharp boundary currents, tropical instability waves, shelf circulations, etc. +The ocean is a turbulent fluid, in which motions on a wide range of scales – from centimeters to thousands of kilometers – interact and exchange energy.Project Goals and Visionhttps://m2lines.github.io/blog/projectgoals/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/projectgoals/Project Goal We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes. Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations.Sea Icehttps://m2lines.github.io/blog/seaice/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/seaice/Sea Ice Figure: Snapshot of Arctic sea ice thickness (Greenland, Barents and Kara Seas) from a CM4 simulation (⅛-degree ocean and sea ice). The Fram strait (between Greenland and Svalbard) exports approximately 10% of the total Northern Hemisphere sea ice cover out of the Arctic basin annually. +Sea ice is the thin layer of frozen ocean that exists at the high latitudes, forming when ocean temperatures are sufficiently cooled by the atmosphere.Johanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYUDeveloping new physics-aware machine learning toolshttps://m2lines.github.io/blog/research2/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research2/Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop Machine Learning algorithms that are physics- and scale-aware to accelerate climate science discovery . Our transformative methodology will rely on a range of ML algorithms (e.g., convolutional neural networks, equation-discovery), including with strict physical constraints, or with embeded physics for improved interpretability, and generalizations. Those ML models will be used to deepen our understanding of subgrid processes, and will be implemented as subgrid parameterizations or as bias corrections, in coarser resolution ocean (NEMO and MOM6) and coupled climate models (IPSLCM, CM4, CESM).High-resolution simulations and observational datahttps://m2lines.github.io/blog/research4/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/blog/research4/We will diagnose small-scale physical processes such as: -Atmospheric convection and clouds (O’Gorman, Yuval) Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) Ocean mesoscale buoyancy fluxes (Abernathey, Balwada) Ocean submesoscale processes (Le Sommer, Gorbunova) Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) Vertical mixing (Adcroft, Reichl, Sane) Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri)Ziwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/NYU, Courant InstituteAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDL in Ocean divisionCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYU, Courant Institute + Center for Data ScienceJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYU, Courant Institute + Computer Science + Center for Data ScienceRyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/Lamont/Columbia UniversityBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDL in Ocean divisionFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/National Center for Atmospheric ResearchMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/National Center for Atmospheric ResearchMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDL in Ocean divisionPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/L’Institut des Géosciences de l’Environnement (IGE)Arthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonAurora Basinskihttps://m2lines.github.io/team/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aurorabasinki/NYU Courant InstituteJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/NYU, Courant InstituteLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/National Center for Atmospheric ResearchCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYU, Courant InstituteTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/Princeton UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/Lamont/Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/L’Institut des Géosciences de l’Environnement (IGE)Pavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYU, Courant InstituteWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYU, Courant InstituteMario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/Columbia UniversityKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYU, Courant InstituteMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Columbia UniversityHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/L’Institut des Géosciences de l’Environnement (IGE)Adrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/Columbia UniversityFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYU, Courant InstituteAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYU, Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/NYU, Courant InstituteSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYU, Courant InstituteChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Chris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYU, Courant InstituteEmily Newsomhttps://m2lines.github.io/team/former/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/emilynewsom/NYU, Courant InstituteEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/NYU, Courant InstituteNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/Princeton UniversityDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/IGEGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYU CourantMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/IGE, GrenobleShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MIT EAPSPatricia Luchttps://m2lines.github.io/team/former/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/patricialuc/Columbia UniversityKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYU, Courant InstituteMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstituteShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/NYU, Tandon School of EngineeringSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYU, Courant InstituteLeo Liuhttps://m2lines.github.io/team/former/leoliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/leoliu/NYU, Courant InstituteShantanu Acharyahttps://m2lines.github.io/team/former/shantanu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shantanu/NYU, Courant InstituteGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/ETH ZürichPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant InstituteJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia UniversityNeural general circulation models for weather and climatehttps://m2lines.github.io/news/2410yuval/Wed, 02 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410yuval/General circulation models (GCMs) are essential for weather and climate prediction. They use physics-based simulations to model large-scale dynamics and small-scale processes. Recently, machine-learning models have matched or exceeded GCMs in weather forecasting accuracy, but struggled with long-term stability and ensemble forecasts. In this Nature paper , co-led by Janni Yuval, a new model, NeuralGCM, integrates machine learning with a differentiable solver for atmospheric dynamics. It performs as well as top machine-learning and physics-based methods for short-term forecasts and can track climate metrics accurately for decades with prescribed sea surface temperature.Distilling Machine Learning’s Added Valuehttps://m2lines.github.io/news/2410shamekh/Tue, 01 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410shamekh/This project addresses the challenge of explaining the added value of machine learning in weather and climate models, particularly for complex deep learning models. By constructing a hierarchy of Pareto-optimal models along an error-complexity plane, the researchers, including Sara Shamekh, provide insights into model development and performance. Through three applications—cloud cover parameterization, shortwave radiative transfer, and tropical precipitation modeling—it demonstrates how machine learning can uncover nonlinear relationships, improve parameterization, and capture key physical processes.Pushing the frontiers in climate modelling and analysis with machine learninghttps://m2lines.github.io/news/2409gentine/Mon, 02 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gentine/This Nature Climate Change review, explores how machine learning can revolutionize climate modeling and analysis to meet growing demands for better projections and actionable climate information. The authors argue that now is the time to advance beyond current techniques by developing more accurate machine-learning-based Earth system models and creating new tools for predicting extreme events, improving detection methods, and enhancing climate model analysis. The review emphasizes the importance of collaboration between machine learning experts and climate scientists, along with the involvement of the private sector, to drive innovation and progress in climate science.Scalable interpolation of satellite altimetry data with probabilistic machine learninghttps://m2lines.github.io/news/2409gregory/Sun, 01 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gregory/In this study , Will Gregory and co-authors developed an open-source Python library for interpolating sparse altimetry data, using local Gaussian process models. The library allows them to generate full images of daily sea ice fields at high spatial resolution (5 km), which they hypothesize could be used to train ML models for sub-grid scale parameterizations.Constraints imply limited future weakening of AMOChttps://m2lines.github.io/news/2408bonan/Sat, 03 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408bonan/Climate models show varied predictions for the weakening of the Atlantic meridional overturning circulation (AMOC) this century. Researchers, including Laure Zanna and CliMA members, reduced this uncertainty by linking AMOC strength to the meridional density difference and overturning depth. They found that models with a deeper present-day overturning predict greater weakening due to less stratified North Atlantic waters. Using observational data, they estimate a modest AMOC weakening of about 4 Sv by century’s end, regardless of emissions.Bringing Statistics to Storylines!https://m2lines.github.io/news/2408gorman/Fri, 02 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408gorman/Accurately modeling extreme weather events is a major goal in climate science and weather risk management. High-resolution computational models provide precise data but are costly for capturing rare events. This study focuses on localized, short-lived events like heavy precipitation. The researchers, including Paul O’Gorman, developed a more efficient method for sampling these events by combining adaptive multilevel splitting (AMS) and “ensemble boosting.” The new approach, tested on the Lorenz-96 model, improved the sampling of extreme events by a factor of 10.Sampling Hybrid Climate Simulation at Scalehttps://m2lines.github.io/news/2407gentine/Tue, 02 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407gentine/Machine-learning (ML) models could enhance climate simulations by accurately representing small-scale processes like turbulence and convection. However, it’s unclear if better standalone (offline) performance leads to better integrated (online) performance in climate models. In this preprint, researchers, including Pierre Gentine, conducted extensive experiments with 2,970 hybrid simulations, finding that reducing offline error generally improves online accuracy, but some decisions can destabilize the model. Key improvements include incorporating memory, training on diverse climates, converting moisture input to relative humidity, and avoiding certain error metrics.Improved Equatorial Upper Ocean Vertical Mixinghttps://m2lines.github.io/news/2407reichl/Mon, 01 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407reichl/Deficiencies in how upper ocean mixing is modeled cause biases in climate simulations, affecting ocean heat uptake and ENSO predictions. To address this, researchers evaluated different mixing models, To address this, researchers evaluated different mixing models, in this study led by Brandon Reichl, using detailed simulations and applied improvements to NOAA/GFDL’s OM4 ocean model. Enhancements to the model’s mixing were informed by observational data and led to more accurate diurnal mixing, ocean currents, and upper ocean temperature profiles.Transfer Learning for Emulating Ocean Climate Variabilityhttps://m2lines.github.io/news/2406dheeshjith/Sun, 02 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406dheeshjith/We are adding the link to the preprint which is now accepted in ICML 2024 workshop and will be presented as a talk and a poster this week in Vienna: Link to the paper . +Atmospheric convection and clouds (O’Gorman, Yuval) Atmospheric boundary layer processes at the ocean and sea-ice interface (Gentine, Connolly) Ocean mesoscale buoyancy fluxes (Abernathey, Balwada) Ocean submesoscale processes (Le Sommer, Gorbunova) Ocean mesoscale momentum, energy and air-sea interactions (Zanna, Perezhogin) Vertical mixing (Adcroft, Reichl, Sane) Sea-ice heterogeneity and its influence on air-sea-ice interactions (Holland, Zampieri)Ziwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/MetaAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDLCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYUJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYURyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/EarthmoversBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDLFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDLJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/NCARMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/NCARMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDLPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/CNRS-IGEArthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonAurora Basinskihttps://m2lines.github.io/team/former/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/aurorabasinki/Scripps, UCSDJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDLAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/ArcadiaLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/ECMWFCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYUTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/John Hopkins UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/LDEO,Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/ComsolPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYUWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/NCARWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYUAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYUMario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/The University of EdinburghKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYUMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Goldman SachsHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/CNRS-IGEAdrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/CNRS-IPSLFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYUAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYUAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/Yale UniversitySara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYUChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/CNRS-IGEChris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYUEmily Newsomhttps://m2lines.github.io/team/former/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/emilynewsom/CaltechEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/Universitat Politècnica de CatalunyaNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/[C]WorthyDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/CNRS-IGEGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYUMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/CNRS-IGEShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MITPatricia Luchttps://m2lines.github.io/team/former/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/patricialuc/MicrosoftKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYUMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYUShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/Rivet.usSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYULeo Liuhttps://m2lines.github.io/team/former/leoliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/leoliu/Shantanu Acharyahttps://m2lines.github.io/team/former/shantanu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shantanu/NYUGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/NetlightPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYUJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia UniversityAntoine-Alexis Nasserhttps://m2lines.github.io/team/antoinenasser/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/antoinenasser/Princeton UniversityXavier Levinehttps://m2lines.github.io/team/xavierlevine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/xavierlevine/Columbia UniversityM²LInES at AGUhttps://m2lines.github.io/news/2412agu/Sun, 01 Dec 2024 09:29:16 +1000https://m2lines.github.io/news/2412agu/Find all the posters and talks from our team at the AGU 24 conference in our December newsletter . If you are going, make sure to add them to your schedule.Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyseshttps://m2lines.github.io/news/2411zampieri/Fri, 01 Nov 2024 09:29:16 +1000https://m2lines.github.io/news/2411zampieri/Lorenzo Zampieri contributed to a new paper , published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one.The Emerging Human Influence on the Seasonal Cycle of Sea Surface Temperaturehttps://m2lines.github.io/news/2411shi/Fri, 01 Nov 2024 09:29:16 +1000https://m2lines.github.io/news/2411shi/In this paper , Jia-Rui Shi and co-authors present scientific evidence indicating that a human-caused signal has already emerged in the seasonal cycle amplitude of Sea Surface Temperature (SST), distinct from the background noise of natural variability. The geographical patterns of the forced SST seasonal cycle change reveal two notable features: an increase in mid-latitudes of the Northern Hemisphere, which is associated with alterations in mixed-layer depth, and a robust dipole pattern in the Southern Hemisphere mid-latitudes, primarily driven by changes in surface winds.Neural general circulation models for weather and climatehttps://m2lines.github.io/news/2410yuval/Wed, 02 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410yuval/General circulation models (GCMs) are essential for weather and climate prediction. They use physics-based simulations to model large-scale dynamics and small-scale processes. Recently, machine-learning models have matched or exceeded GCMs in weather forecasting accuracy, but struggled with long-term stability and ensemble forecasts. In this Nature paper , co-led by Janni Yuval, a new model, NeuralGCM, integrates machine learning with a differentiable solver for atmospheric dynamics. It performs as well as top machine-learning and physics-based methods for short-term forecasts and can track climate metrics accurately for decades with prescribed sea surface temperature.Distilling Machine Learning’s Added Valuehttps://m2lines.github.io/news/2410shamekh/Tue, 01 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410shamekh/This project addresses the challenge of explaining the added value of machine learning in weather and climate models, particularly for complex deep learning models. By constructing a hierarchy of Pareto-optimal models along an error-complexity plane, the researchers, including Sara Shamekh, provide insights into model development and performance. Through three applications—cloud cover parameterization, shortwave radiative transfer, and tropical precipitation modeling—it demonstrates how machine learning can uncover nonlinear relationships, improve parameterization, and capture key physical processes.Pushing the frontiers in climate modelling and analysis with machine learninghttps://m2lines.github.io/news/2409gentine/Mon, 02 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gentine/This Nature Climate Change review, explores how machine learning can revolutionize climate modeling and analysis to meet growing demands for better projections and actionable climate information. The authors argue that now is the time to advance beyond current techniques by developing more accurate machine-learning-based Earth system models and creating new tools for predicting extreme events, improving detection methods, and enhancing climate model analysis. The review emphasizes the importance of collaboration between machine learning experts and climate scientists, along with the involvement of the private sector, to drive innovation and progress in climate science.Scalable interpolation of satellite altimetry data with probabilistic machine learninghttps://m2lines.github.io/news/2409gregory/Sun, 01 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gregory/In this study , Will Gregory and co-authors developed an open-source Python library for interpolating sparse altimetry data, using local Gaussian process models. The library allows them to generate full images of daily sea ice fields at high spatial resolution (5 km), which they hypothesize could be used to train ML models for sub-grid scale parameterizations.Constraints imply limited future weakening of AMOChttps://m2lines.github.io/news/2408bonan/Sat, 03 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408bonan/Climate models show varied predictions for the weakening of the Atlantic meridional overturning circulation (AMOC) this century. Researchers, including Laure Zanna and CliMA members, reduced this uncertainty by linking AMOC strength to the meridional density difference and overturning depth. They found that models with a deeper present-day overturning predict greater weakening due to less stratified North Atlantic waters. Using observational data, they estimate a modest AMOC weakening of about 4 Sv by century’s end, regardless of emissions.Bringing Statistics to Storylines!https://m2lines.github.io/news/2408gorman/Fri, 02 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408gorman/Accurately modeling extreme weather events is a major goal in climate science and weather risk management. High-resolution computational models provide precise data but are costly for capturing rare events. This study focuses on localized, short-lived events like heavy precipitation. The researchers, including Paul O’Gorman, developed a more efficient method for sampling these events by combining adaptive multilevel splitting (AMS) and “ensemble boosting.” The new approach, tested on the Lorenz-96 model, improved the sampling of extreme events by a factor of 10.Sampling Hybrid Climate Simulation at Scalehttps://m2lines.github.io/news/2407gentine/Tue, 02 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407gentine/Machine-learning (ML) models could enhance climate simulations by accurately representing small-scale processes like turbulence and convection. However, it’s unclear if better standalone (offline) performance leads to better integrated (online) performance in climate models. In this preprint, researchers, including Pierre Gentine, conducted extensive experiments with 2,970 hybrid simulations, finding that reducing offline error generally improves online accuracy, but some decisions can destabilize the model. Key improvements include incorporating memory, training on diverse climates, converting moisture input to relative humidity, and avoiding certain error metrics.Improved Equatorial Upper Ocean Vertical Mixinghttps://m2lines.github.io/news/2407reichl/Mon, 01 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407reichl/Deficiencies in how upper ocean mixing is modeled cause biases in climate simulations, affecting ocean heat uptake and ENSO predictions. To address this, researchers evaluated different mixing models, To address this, researchers evaluated different mixing models, in this study led by Brandon Reichl, using detailed simulations and applied improvements to NOAA/GFDL’s OM4 ocean model. Enhancements to the model’s mixing were informed by observational data and led to more accurate diurnal mixing, ocean currents, and upper ocean temperature profiles.Transfer Learning for Emulating Ocean Climate Variabilityhttps://m2lines.github.io/news/2406dheeshjith/Sun, 02 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406dheeshjith/We are adding the link to the preprint which is now accepted in ICML 2024 workshop and will be presented as a talk and a poster this week in Vienna: Link to the paper . This study, led jointly by Surya Dheeshjith and Adam Subel, showcases Machine Learning Ocean Emulators accurately predicting global ocean surface conditions over 5-8 years under different CO₂ forcing scenarios. While the models struggle to generalize, fine-tuning with small amounts of additional data from warner climates can significantly improve model performance.Data-driven dimensionality reduction and causal inference for spatiotemporal climate fieldshttps://m2lines.github.io/news/2406falasca/Sat, 01 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406falasca/One of the main challenges in statistical inference is to infer causal relations rather than simple correlations. In this paper , Fabrizio Falasca, Pavel Perezhogin, and Laure Zanna, propose a framework, based on linear response theory, to investigate and diagnose causal mechanisms in the climate system directly from data. The method is physically-based and scales to high-dimensional systems. Possible future applications include the response of the climate system to external perturbations and evaluate climate models outputs.FTORCH and FORPY libraries now integrated to CESM!https://m2lines.github.io/news/2405chapman/Thu, 02 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405chapman/The M²LInES team, assisted by Jack Atkinson (ICCS) and NCAR software engineers, have integrated the FTORCH and FORPY libraries for machine learning into CESM2.1.5. To set up a machine learning-enabled CESM instance on NCAR machines, follow the steps outlined in this repository . Contact Will Chapman for questions!Predicting September Arctic Sea Icehttps://m2lines.github.io/news/2405bushuk/Wed, 01 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405bushuk/This study , led by Mitch Bushuk, provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of 34 dynamical and statistical prediction models, quantifying the state-of-the-art in the rapidly growing sea ice prediction research community. The authors find that both dynamical and statistical prediction models can skillfully predict September Arctic sea ice 0–3 months in advance on Pan-Arctic, regional, and local spatial scales. These results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.Advances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inferencehttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulatorshttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulationshttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in “online” testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddieshttps://m2lines.github.io/news/2402nora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402nora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean modelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/Small scale vertical mixing processes in the upper ocean region cannot be resolved by ocean models due to the lack of computing resources, and are thus parameterized. Existing models have a few ad hoc approximations which cause uncertainty in climate change simulations. In this breakthrough preprint , Aakash Sane and co-authors - Brandon Reichl, Alistair Adcroft, and Laure Zanna - replace such an approximation using data driven neural networks (NN), leading to improved physics in an ocean model simulation and a reduction of model bias in the Tropics.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , led by Lorenzo Zampieri, and co-authored by Marika Holland, illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea-ice-covered regions of the Arctic under clear-sky conditions. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , Karl Otness and co-authors are evaluating a proof of concept multiscale approach to predicting subgrid forcings in climate models. They see encouraging preliminary results from first making a prediction in a fine-to-coarse direction which yields a lower resolution, but more confident prediction followed by a refinement to predict finer scale details. This paper won “Best ML innovation” at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning.Emulating Cloud Superparameterization by Pierre Gentinehttps://m2lines.github.io/news/2301gentine/Tue, 03 Jan 2023 09:29:16 +1000https://m2lines.github.io/news/2301gentine/M²LInES PI Pierre Gentine is the senior author in this new publication on using deep learning for emulating cloud superparameterization in climate models. Research Scientist Tom Beucler is a co-author. Read their paper here M²LInES at AGU 22https://m2lines.github.io/news/2212agu22/Tue, 06 Dec 2022 09:29:16 +1000https://m2lines.github.io/news/2212agu22/AGU M²LInES Highlights The M²LInES team is excited to present our recent scientific advances at the Fall 2022 AGU meeting in Chicago and online. Below is a summary of the talks (all times are CST). For your convenience, you can download the schedule for the talks by following this link . Machine learning for physics discovery & modeling We have an extensive lineup of talks and posters from M²LInES team members and affiliates. Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonalityhttps://m2lines.github.io/news/2211balwada/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211balwada/In this article , led by Dhruv Balwada, ocean drifter observations show wave and turbulence merge and split flows, simultaneously transferring energy to large and small scales. Implicit learning of convective organization explains precipitation stochasticityhttps://m2lines.github.io/news/2211shamekh/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211shamekh/Cloud and convection take many structure and degrees of organization, which are still absent in most parameterizations. But does that structure matter for precipitation? This preprint , led by Sara Shamekh, proposes an elegant method to include organization in the parameterization of precipitation that results in a significant improvement in predicting precipitation, especially extremes, at GCM scale. Ocean currents break up a tabular iceberghttps://m2lines.github.io/news/2211adcroft/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211adcroft/This article examines the mechanism that led to the breakup of massive iceberg A68 in Dec 2020. The authors, including Alistair Adcroft, demonstrate that the A68a breakup event may have been triggered by ocean-current shear, a new breakup mechanism not previously reported. Their paper was featured in the news . On Gradient Descent Convergence beyond the Edge of Stabilityhttps://m2lines.github.io/news/2211bruna/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211bruna/Joan Bruna is the senior author in this preprint on Gradient Descent (GD) convergence in unstable conditions. The study examines the conditions for such unstable convergence, focusing on simple, yet representative, learning problems. A year-round satellite sea-ice thickness record from CryoSat-2https://m2lines.github.io/news/2210bushuk/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210bushuk/This study , co-authored by Mitch Bushuk, produced the first ever year-round satellite observational record of Arctic sea ice thickness, employing a deep learning model to produce estimates of sea ice freeboard. These data span the period 2010-2020 and will be critical for better monitoring the changing Arctic climate, advancing climate model evaluation, and improving sea ice predictions. Comparing Storm Resolving Models and Climates via Unsupervised Machine Learninghttps://m2lines.github.io/news/2210gentine/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210gentine/High resolution data is overwhelming and this paper , co-authored by Pierre Gentine and Tom Beucler, tries to define an unsupervised machine learning technique to better compare Storm Resolving Climate Models (SRMs). Their analysis found that only six out of nine considered SRMs are dynamically consistent. This split among SRMs highlights the need to further investigate sub-grid parameterization choices. Semi-automatic tuning of coupled climate models with multiple intrinsic timescaleshttps://m2lines.github.io/news/2210deshayes/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210deshayes/Using the two-scale Lorenz96 model, this article evaluates the potential of History Matching to tune a climate system with multi-scale dynamics. This semi-automatic tuning was more efficient when combined with physical expertise. Julie Deshayes and V Balaji are co-authors on this study. Benchmarking of machine learning ocean subgrid parameterizations in an idealized modelhttps://m2lines.github.io/news/2208ross/Tue, 02 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208ross/In this work , led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Laure Zanna, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations. Cloud-based framework for inter-comparing submesoscale permitting realistic ocean modelshttps://m2lines.github.io/news/2208uchida/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208uchida/This study presents a cloud-based workflow for intercomparing high-resolution ocean models and provides an assessment of one of the most used parameterizations of ocean submesoscales. Julien Le Sommer, Ryan Abernathey, and Aurelie Albert contributed to the research. Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Modelshttps://m2lines.github.io/news/2208behrens/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208behrens/Pierre Gentine and Tom Beucler are co-authors in this article , which demonstrates that Variational Encoder Decoder structures (VED) are capable of learning and accurately reproducing convective processes in an aquaplanet superparameterized climate model simulation, while enabling interpretability and better understanding of sub-grid-scale physical processes. A potential energy analysis of ocean surface mixed layers - Brandon Reichlhttps://m2lines.github.io/news/2207reichl/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207reichl/Analysis of the ocean mixed layer requires one to estimate its vertical extent, for which there are various definitions. In this paper , Brandon Reichl and co-authors, including Alistair Adcroft, show that using the potential energy anomaly to identify the mixed layer depth is practical and it offers conceptual benefits by directly linking to the physics of upper ocean vertical mixing. New preprint led by Adam Subel on transfer learninghttps://m2lines.github.io/news/2207subel/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207subel/Adam Subel is the lead author in this article , which builds a framework to understand and implement transfer learning, a powerful machine learning technique for enabling generalization between datasets from different configurations of a system or of varying quality/quantity, in multiscale physical systems. Subseasonal Earth System Prediction with CESM2 - Judith Bernerhttps://m2lines.github.io/news/2207berner/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207berner/Judith Berner is a co-author in this article , which describes the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): with the Community Atmosphere Model and with Whole Atmosphere Community Climate Model as its atmospheric component. Both systems are compared to NOAA CFSv2 and ECMWF. Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layerhttps://m2lines.github.io/news/gentine-abernathey/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gentine-abernathey/This article , co-authored by Pierre Gentine and Ryan Abernathey, replaces the typically used physically based assumptions with deep learning in subgrid-scale modeling, in large eddy simulations of turbulence. They show that the latter performs better in a priori (offline) tests of atmospheric turbulence. The deep neural networks model also captures key statistics of turbulence in a posteriori (online) tests. Lorenzo Zampieri - A machine learning correction modelhttps://m2lines.github.io/news/zampieri2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/zampieri2022/Lorenzo Zampieri et al. illustrate in this preprint , a novel method based on ML for reducing the surface temperature errors of atmospheric reanalyses in sea-ice-covered regions of the Arctic. The corrected temperature can be employed to support polar research activities, and to better simulate the evolution of the interacting sea ice and ocean systems in numerical models. Will Gregory - Network connectivity articlehttps://m2lines.github.io/news/gregory2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gregory2022/Will Gregory and co-authors hypothesized in this publication that regional biases in sea ice thickness may hinder the ability of CMIP6 models to reproduce an important atmospheric driver of summer sea ice variability, and hence their ability to make reliable sea ice predictions on seasonal to inter-annual time scales. 2022 CESM workshop: Info and submission deadlinehttps://m2lines.github.io/news/cesmworkshop2022/Mon, 02 May 2022 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop2022/Registration is open for the 2022 CESM Workshop on June 13-16, 2022. Please visit the website to register as well as submit working group session talks and /or posters. The deadline to submit a talk / poster is Monday, May 16th. diff --git a/jobs/index.html b/jobs/index.html index b92955ab..f5c8324e 100644 --- a/jobs/index.html +++ b/jobs/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Open positions

    Updated on 10/08/2024 - The links for applications will be updated as they become available.

    M²LInES affirms the value of differing perspectives in Sciences. As such, we strongly encourage applications from women, racial and ethnic minorities, and other individuals who are under-represented in the profession, across color, creed, race, ethnic and national origin, physical ability, gender and sexual identity, or any other legally protected basis.

    Princeton University/GFDL

    Postdoctoral researcher or more senior scientist for Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning. Apply here

    Columbia University (NCAR location)

    Associate Research Scientist at the interface between climate modeling and machine learning. The successful candidate will be hired by Columbia University but the main work location will be at NCAR (Boulder, CO). Apply here

    • This project is supported by Schmidt Sciences, LLC.

    Open positions

    Updated on 11/22/2024 - The links for applications will be updated as they become available.

    M²LInES affirms the value of differing perspectives in Sciences. As such, we strongly encourage applications from women, racial and ethnic minorities, and other individuals who are under-represented in the profession, across color, creed, race, ethnic and national origin, physical ability, gender and sexual identity, or any other legally protected basis.

    Princeton University/GFDL

    Postdoctoral researcher or more senior scientist for Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning. Apply here

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/news/2411shi/index.html b/news/2411shi/index.html new file mode 100644 index 00000000..03ad2dc7 --- /dev/null +++ b/news/2411shi/index.html @@ -0,0 +1,14 @@ +The Emerging Human Influence on the Seasonal Cycle of Sea Surface Temperature - M²LInES +
    M²LInES homepage
    news

    The Emerging Human Influence on the Seasonal Cycle of Sea Surface Temperature

    In this paper +, Jia-Rui Shi and co-authors present scientific evidence indicating that a human-caused signal has already emerged in the seasonal cycle amplitude of Sea Surface Temperature (SST), distinct from the background noise of natural variability. The geographical patterns of the forced SST seasonal cycle change reveal two notable features: an increase in mid-latitudes of the Northern Hemisphere, which is associated with alterations in mixed-layer depth, and a robust dipole pattern in the Southern Hemisphere mid-latitudes, primarily driven by changes in surface winds. These observed changes in the SST seasonal cycle have significant implications for marine ecosystems.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/news/2411zampieri/index.html b/news/2411zampieri/index.html new file mode 100644 index 00000000..9664750f --- /dev/null +++ b/news/2411zampieri/index.html @@ -0,0 +1,14 @@ +Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses - M²LInES +
    M²LInES homepage
    news

    Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses

    Lorenzo Zampieri contributed to a new paper +, published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/news/2412agu/index.html b/news/2412agu/index.html new file mode 100644 index 00000000..b5c4b8fe --- /dev/null +++ b/news/2412agu/index.html @@ -0,0 +1,14 @@ +M²LInES at AGU - M²LInES +
    M²LInES homepage
    news

    M²LInES at AGU

    Find all the posters and talks from our team at the AGU 24 conference in our December newsletter +. If you are going, make sure to add them to your schedule.

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/news/index.html b/news/index.html index d2948c50..b81d5fae 100644 --- a/news/index.html +++ b/news/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • News

    M²LInES in the news and news from M²LInES

    Neural general circulation models for weather and climate

    Distilling Machine Learning’s Added Value

    Pushing the frontiers in climate modelling and analysis with machine learning

    Scalable interpolation of satellite altimetry data with probabilistic machine learning

    Constraints imply limited future weakening of AMOC

    Bringing Statistics to Storylines!

    Sampling Hybrid Climate Simulation at Scale

    Improved Equatorial Upper Ocean Vertical Mixing

    Transfer Learning for Emulating Ocean Climate Variability

    Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields

    FTORCH and FORPY libraries now integrated to CESM!

    Predicting September Arctic Sea Ice

    Advances in Machine Learning Techniques for Sea Ice Applications

    Joint Parameter and Parameterization Inference

    Building Ocean Climate Emulators

    Stress-testing the coupled behavior of hybrid physics-machine simulations

    Stochastic Optimal Control Matching by Joan

    Restratification Effect of Mesoscale Eddies

    Data-driven equation discovery ocean model

    Learning Machine Learning with Lorenz-96

    ClimSim awarded Best Paper Award at NeurIPS

    Will Gregory's latest article featured in Advance Science News

    OceanBench by J. Emmanuel Johnson

    UNetKF by Feiyu Lu

    Multi-fidelity climate model parameterization by Mohamed Aziz Bhouri

    Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model

    Blog Post - A New Generation of Climate Models

    New climate simulation model ensemble by Marika Holland

    Understanding Extreme Precipitation Changes by Griffin Mooers

    Reliable coarse-grained turbulent simulations by Chris Pederson

    Background Pycnocline depth constrains FOHUE by Emily Newsom

    ClimSim: LEAP publication

    Learning Atmospheric Boundary Layer Turbulence by Sara Shamekh

    Parameterizing vertical mixing coefficients in the Ocean by Akash Sane

    Smart correction model for winter sky temperatures by Lorenzo Zampieri

    Data-driven subgrid parameterizations by Karl Otness

    Emulating Cloud Superparameterization by Pierre Gentine

    M²LInES at AGU 22

    Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

    Implicit learning of convective organization explains precipitation stochasticity

    Ocean currents break up a tabular iceberg

    On Gradient Descent Convergence beyond the Edge of Stability

    A year-round satellite sea-ice thickness record from CryoSat-2

    Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning

    Semi-automatic tuning of coupled climate models with multiple intrinsic timescales

    Benchmarking of machine learning ocean subgrid parameterizations in an idealized model

    Cloud-based framework for inter-comparing submesoscale permitting realistic ocean models

    Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Models

    A potential energy analysis of ocean surface mixed layers - Brandon Reichl

    New preprint led by Adam Subel on transfer learning

    Subseasonal Earth System Prediction with CESM2 - Judith Berner

    Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layer

    Lorenzo Zampieri - A machine learning correction model

    Will Gregory - Network connectivity article

    2022 CESM workshop: Info and submission deadline

    Anastasiia Gorbunova - Eulerian spatiotemporal correlations in passive scalar turbulence

    GCM-Filters publication

    Marika Holland - Arctic sea ice sensitivity to lateral melting representation

    Balaji - Are GCMs obsolete?

    Adam Subel - Stable a posteriori LES of 2D turbulence using convolutional neural networks

    Joan Bruna - Neural Galerkin Scheme preprint

    Mitch Bushuk on the Mechanisms of Regional Arctic Sea Ice Predictability

    Dhruv Balwada and Laure Zanna article in SIAM news

    Movie about M²LInES and featured on AGU TV!

    Aakash Sane - Evaluating Coupled Climate Model Parameterizations

    Book Chapter - Isopycnal mixing by Ryan Abernathey

    Climate-Invariant Machine Learning

    Deep Probability Estimation

    Hugo Frezat - A posteriori learning of quasi-geostrophic turbulence parametrization

    Tropical precipitation clusters as islands on a rough water-vapor topography

    AGU presentations by M²LInES team members

    M²LInES Kickoff meeting

    The influence of snow on sea ice as assessed from simulations of CESM2 - Marika Holland

    Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning - Will Chapman

    Response of Northern Hemisphere Weather and Climate to Arctic Sea Ice Decline - Lorenzo Zampieri

    UN AI for Good: AI and Climate Science seminars

    Adaptive Denoising via GainTuning - Carlos Fernandez-Granda

    Impact of Sea-Ice Model Complexity on model performance by Lorenzo Zampieri

    Article by Dhruv Balwada et al. - Role of submesoscale flows on vertical fluxes

    Estimating Ocean Surface Currents With Machine Learning - Ryan Abernathey

    Seasonal Prediction and Predictability of Regional Antarctic Sea Ice - Mitch Bushuk

    Neural Networks as Geometric Chaotic Maps - by Ziwei Li

    Neural network parametrization of a subgrid process - by Janni Yuval and Paul O'Gorman

    Article in Eos by Yuval et al. - Call for Papers on Machine Learning and Earth System Modeling

    Upcoming Seminar by Laure Zanna at One World Mathematics of Climate - 7/6

    A Bayesian approach towards sea ice freeboard estimates - paper by Will Gregory

    s2s challenge

    KITP conference: Machine Learning for Climate

    2021 CESM workshop: M²LInES team members presenting on June 17th

    Call for Papers for JAMES

    Using deep learning to emulate cloud superparameterization - new paper by PI Gentine

    Neural Network emulating physical constraints - paper by PI Gentine

    New paper on deep learning parametrization by Guillaumin and Zanna

    Modelisation using neural network - paper by PI Le Sommer

    Newsletters

    • This project is supported by Schmidt Sciences, LLC.

    News

    M²LInES in the news and news from M²LInES

    M²LInES at AGU

    Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses

    The Emerging Human Influence on the Seasonal Cycle of Sea Surface Temperature

    Neural general circulation models for weather and climate

    Distilling Machine Learning’s Added Value

    Pushing the frontiers in climate modelling and analysis with machine learning

    Scalable interpolation of satellite altimetry data with probabilistic machine learning

    Constraints imply limited future weakening of AMOC

    Bringing Statistics to Storylines!

    Sampling Hybrid Climate Simulation at Scale

    Improved Equatorial Upper Ocean Vertical Mixing

    Transfer Learning for Emulating Ocean Climate Variability

    Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields

    FTORCH and FORPY libraries now integrated to CESM!

    Predicting September Arctic Sea Ice

    Advances in Machine Learning Techniques for Sea Ice Applications

    Joint Parameter and Parameterization Inference

    Building Ocean Climate Emulators

    Stress-testing the coupled behavior of hybrid physics-machine simulations

    Stochastic Optimal Control Matching by Joan

    Restratification Effect of Mesoscale Eddies

    Data-driven equation discovery ocean model

    Learning Machine Learning with Lorenz-96

    ClimSim awarded Best Paper Award at NeurIPS

    Will Gregory's latest article featured in Advance Science News

    OceanBench by J. Emmanuel Johnson

    UNetKF by Feiyu Lu

    Multi-fidelity climate model parameterization by Mohamed Aziz Bhouri

    Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model

    Blog Post - A New Generation of Climate Models

    New climate simulation model ensemble by Marika Holland

    Understanding Extreme Precipitation Changes by Griffin Mooers

    Reliable coarse-grained turbulent simulations by Chris Pederson

    Background Pycnocline depth constrains FOHUE by Emily Newsom

    ClimSim: LEAP publication

    Learning Atmospheric Boundary Layer Turbulence by Sara Shamekh

    Parameterizing vertical mixing coefficients in the Ocean by Akash Sane

    Smart correction model for winter sky temperatures by Lorenzo Zampieri

    Data-driven subgrid parameterizations by Karl Otness

    Emulating Cloud Superparameterization by Pierre Gentine

    M²LInES at AGU 22

    Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonality

    Implicit learning of convective organization explains precipitation stochasticity

    Ocean currents break up a tabular iceberg

    On Gradient Descent Convergence beyond the Edge of Stability

    A year-round satellite sea-ice thickness record from CryoSat-2

    Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning

    Semi-automatic tuning of coupled climate models with multiple intrinsic timescales

    Benchmarking of machine learning ocean subgrid parameterizations in an idealized model

    Cloud-based framework for inter-comparing submesoscale permitting realistic ocean models

    Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Models

    A potential energy analysis of ocean surface mixed layers - Brandon Reichl

    New preprint led by Adam Subel on transfer learning

    Subseasonal Earth System Prediction with CESM2 - Judith Berner

    Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layer

    Lorenzo Zampieri - A machine learning correction model

    Will Gregory - Network connectivity article

    2022 CESM workshop: Info and submission deadline

    Anastasiia Gorbunova - Eulerian spatiotemporal correlations in passive scalar turbulence

    GCM-Filters publication

    Marika Holland - Arctic sea ice sensitivity to lateral melting representation

    Balaji - Are GCMs obsolete?

    Adam Subel - Stable a posteriori LES of 2D turbulence using convolutional neural networks

    Joan Bruna - Neural Galerkin Scheme preprint

    Mitch Bushuk on the Mechanisms of Regional Arctic Sea Ice Predictability

    Dhruv Balwada and Laure Zanna article in SIAM news

    Movie about M²LInES and featured on AGU TV!

    Aakash Sane - Evaluating Coupled Climate Model Parameterizations

    Book Chapter - Isopycnal mixing by Ryan Abernathey

    Climate-Invariant Machine Learning

    Deep Probability Estimation

    Hugo Frezat - A posteriori learning of quasi-geostrophic turbulence parametrization

    Tropical precipitation clusters as islands on a rough water-vapor topography

    AGU presentations by M²LInES team members

    M²LInES Kickoff meeting

    The influence of snow on sea ice as assessed from simulations of CESM2 - Marika Holland

    Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning - Will Chapman

    Response of Northern Hemisphere Weather and Climate to Arctic Sea Ice Decline - Lorenzo Zampieri

    UN AI for Good: AI and Climate Science seminars

    Adaptive Denoising via GainTuning - Carlos Fernandez-Granda

    Impact of Sea-Ice Model Complexity on model performance by Lorenzo Zampieri

    Article by Dhruv Balwada et al. - Role of submesoscale flows on vertical fluxes

    Estimating Ocean Surface Currents With Machine Learning - Ryan Abernathey

    Seasonal Prediction and Predictability of Regional Antarctic Sea Ice - Mitch Bushuk

    Neural Networks as Geometric Chaotic Maps - by Ziwei Li

    Neural network parametrization of a subgrid process - by Janni Yuval and Paul O'Gorman

    Article in Eos by Yuval et al. - Call for Papers on Machine Learning and Earth System Modeling

    Upcoming Seminar by Laure Zanna at One World Mathematics of Climate - 7/6

    A Bayesian approach towards sea ice freeboard estimates - paper by Will Gregory

    s2s challenge

    KITP conference: Machine Learning for Climate

    2021 CESM workshop: M²LInES team members presenting on June 17th

    Call for Papers for JAMES

    Using deep learning to emulate cloud superparameterization - new paper by PI Gentine

    Neural Network emulating physical constraints - paper by PI Gentine

    New paper on deep learning parametrization by Guillaumin and Zanna

    Modelisation using neural network - paper by PI Le Sommer

    Newsletters

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/news/index.xml b/news/index.xml index 3c747692..724b08bd 100644 --- a/news/index.xml +++ b/news/index.xml @@ -1,4 +1,4 @@ -News on M²LInEShttps://m2lines.github.io/news/Recent content in News on M²LInESHugo -- gohugo.ioen-usWed, 02 Oct 2024 09:29:16 +1000Neural general circulation models for weather and climatehttps://m2lines.github.io/news/2410yuval/Wed, 02 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410yuval/General circulation models (GCMs) are essential for weather and climate prediction. They use physics-based simulations to model large-scale dynamics and small-scale processes. Recently, machine-learning models have matched or exceeded GCMs in weather forecasting accuracy, but struggled with long-term stability and ensemble forecasts. In this Nature paper , co-led by Janni Yuval, a new model, NeuralGCM, integrates machine learning with a differentiable solver for atmospheric dynamics. It performs as well as top machine-learning and physics-based methods for short-term forecasts and can track climate metrics accurately for decades with prescribed sea surface temperature.Distilling Machine Learning’s Added Valuehttps://m2lines.github.io/news/2410shamekh/Tue, 01 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410shamekh/This project addresses the challenge of explaining the added value of machine learning in weather and climate models, particularly for complex deep learning models. By constructing a hierarchy of Pareto-optimal models along an error-complexity plane, the researchers, including Sara Shamekh, provide insights into model development and performance. Through three applications—cloud cover parameterization, shortwave radiative transfer, and tropical precipitation modeling—it demonstrates how machine learning can uncover nonlinear relationships, improve parameterization, and capture key physical processes.Pushing the frontiers in climate modelling and analysis with machine learninghttps://m2lines.github.io/news/2409gentine/Mon, 02 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gentine/This Nature Climate Change review, explores how machine learning can revolutionize climate modeling and analysis to meet growing demands for better projections and actionable climate information. The authors argue that now is the time to advance beyond current techniques by developing more accurate machine-learning-based Earth system models and creating new tools for predicting extreme events, improving detection methods, and enhancing climate model analysis. The review emphasizes the importance of collaboration between machine learning experts and climate scientists, along with the involvement of the private sector, to drive innovation and progress in climate science.Scalable interpolation of satellite altimetry data with probabilistic machine learninghttps://m2lines.github.io/news/2409gregory/Sun, 01 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gregory/In this study , Will Gregory and co-authors developed an open-source Python library for interpolating sparse altimetry data, using local Gaussian process models. The library allows them to generate full images of daily sea ice fields at high spatial resolution (5 km), which they hypothesize could be used to train ML models for sub-grid scale parameterizations.Constraints imply limited future weakening of AMOChttps://m2lines.github.io/news/2408bonan/Sat, 03 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408bonan/Climate models show varied predictions for the weakening of the Atlantic meridional overturning circulation (AMOC) this century. Researchers, including Laure Zanna and CliMA members, reduced this uncertainty by linking AMOC strength to the meridional density difference and overturning depth. They found that models with a deeper present-day overturning predict greater weakening due to less stratified North Atlantic waters. Using observational data, they estimate a modest AMOC weakening of about 4 Sv by century’s end, regardless of emissions.Bringing Statistics to Storylines!https://m2lines.github.io/news/2408gorman/Fri, 02 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408gorman/Accurately modeling extreme weather events is a major goal in climate science and weather risk management. High-resolution computational models provide precise data but are costly for capturing rare events. This study focuses on localized, short-lived events like heavy precipitation. The researchers, including Paul O’Gorman, developed a more efficient method for sampling these events by combining adaptive multilevel splitting (AMS) and “ensemble boosting.” The new approach, tested on the Lorenz-96 model, improved the sampling of extreme events by a factor of 10.Sampling Hybrid Climate Simulation at Scalehttps://m2lines.github.io/news/2407gentine/Tue, 02 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407gentine/Machine-learning (ML) models could enhance climate simulations by accurately representing small-scale processes like turbulence and convection. However, it’s unclear if better standalone (offline) performance leads to better integrated (online) performance in climate models. In this preprint, researchers, including Pierre Gentine, conducted extensive experiments with 2,970 hybrid simulations, finding that reducing offline error generally improves online accuracy, but some decisions can destabilize the model. Key improvements include incorporating memory, training on diverse climates, converting moisture input to relative humidity, and avoiding certain error metrics.Improved Equatorial Upper Ocean Vertical Mixinghttps://m2lines.github.io/news/2407reichl/Mon, 01 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407reichl/Deficiencies in how upper ocean mixing is modeled cause biases in climate simulations, affecting ocean heat uptake and ENSO predictions. To address this, researchers evaluated different mixing models, To address this, researchers evaluated different mixing models, in this study led by Brandon Reichl, using detailed simulations and applied improvements to NOAA/GFDL’s OM4 ocean model. Enhancements to the model’s mixing were informed by observational data and led to more accurate diurnal mixing, ocean currents, and upper ocean temperature profiles.Transfer Learning for Emulating Ocean Climate Variabilityhttps://m2lines.github.io/news/2406dheeshjith/Sun, 02 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406dheeshjith/We are adding the link to the preprint which is now accepted in ICML 2024 workshop and will be presented as a talk and a poster this week in Vienna: Link to the paper . +News on M²LInEShttps://m2lines.github.io/news/Recent content in News on M²LInESHugo -- gohugo.ioen-usSun, 01 Dec 2024 09:29:16 +1000M²LInES at AGUhttps://m2lines.github.io/news/2412agu/Sun, 01 Dec 2024 09:29:16 +1000https://m2lines.github.io/news/2412agu/Find all the posters and talks from our team at the AGU 24 conference in our December newsletter . If you are going, make sure to add them to your schedule.Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyseshttps://m2lines.github.io/news/2411zampieri/Fri, 01 Nov 2024 09:29:16 +1000https://m2lines.github.io/news/2411zampieri/Lorenzo Zampieri contributed to a new paper , published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one.The Emerging Human Influence on the Seasonal Cycle of Sea Surface Temperaturehttps://m2lines.github.io/news/2411shi/Fri, 01 Nov 2024 09:29:16 +1000https://m2lines.github.io/news/2411shi/In this paper , Jia-Rui Shi and co-authors present scientific evidence indicating that a human-caused signal has already emerged in the seasonal cycle amplitude of Sea Surface Temperature (SST), distinct from the background noise of natural variability. The geographical patterns of the forced SST seasonal cycle change reveal two notable features: an increase in mid-latitudes of the Northern Hemisphere, which is associated with alterations in mixed-layer depth, and a robust dipole pattern in the Southern Hemisphere mid-latitudes, primarily driven by changes in surface winds.Neural general circulation models for weather and climatehttps://m2lines.github.io/news/2410yuval/Wed, 02 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410yuval/General circulation models (GCMs) are essential for weather and climate prediction. They use physics-based simulations to model large-scale dynamics and small-scale processes. Recently, machine-learning models have matched or exceeded GCMs in weather forecasting accuracy, but struggled with long-term stability and ensemble forecasts. In this Nature paper , co-led by Janni Yuval, a new model, NeuralGCM, integrates machine learning with a differentiable solver for atmospheric dynamics. It performs as well as top machine-learning and physics-based methods for short-term forecasts and can track climate metrics accurately for decades with prescribed sea surface temperature.Distilling Machine Learning’s Added Valuehttps://m2lines.github.io/news/2410shamekh/Tue, 01 Oct 2024 09:29:16 +1000https://m2lines.github.io/news/2410shamekh/This project addresses the challenge of explaining the added value of machine learning in weather and climate models, particularly for complex deep learning models. By constructing a hierarchy of Pareto-optimal models along an error-complexity plane, the researchers, including Sara Shamekh, provide insights into model development and performance. Through three applications—cloud cover parameterization, shortwave radiative transfer, and tropical precipitation modeling—it demonstrates how machine learning can uncover nonlinear relationships, improve parameterization, and capture key physical processes.Pushing the frontiers in climate modelling and analysis with machine learninghttps://m2lines.github.io/news/2409gentine/Mon, 02 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gentine/This Nature Climate Change review, explores how machine learning can revolutionize climate modeling and analysis to meet growing demands for better projections and actionable climate information. The authors argue that now is the time to advance beyond current techniques by developing more accurate machine-learning-based Earth system models and creating new tools for predicting extreme events, improving detection methods, and enhancing climate model analysis. The review emphasizes the importance of collaboration between machine learning experts and climate scientists, along with the involvement of the private sector, to drive innovation and progress in climate science.Scalable interpolation of satellite altimetry data with probabilistic machine learninghttps://m2lines.github.io/news/2409gregory/Sun, 01 Sep 2024 09:29:16 +1000https://m2lines.github.io/news/2409gregory/In this study , Will Gregory and co-authors developed an open-source Python library for interpolating sparse altimetry data, using local Gaussian process models. The library allows them to generate full images of daily sea ice fields at high spatial resolution (5 km), which they hypothesize could be used to train ML models for sub-grid scale parameterizations.Constraints imply limited future weakening of AMOChttps://m2lines.github.io/news/2408bonan/Sat, 03 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408bonan/Climate models show varied predictions for the weakening of the Atlantic meridional overturning circulation (AMOC) this century. Researchers, including Laure Zanna and CliMA members, reduced this uncertainty by linking AMOC strength to the meridional density difference and overturning depth. They found that models with a deeper present-day overturning predict greater weakening due to less stratified North Atlantic waters. Using observational data, they estimate a modest AMOC weakening of about 4 Sv by century’s end, regardless of emissions.Bringing Statistics to Storylines!https://m2lines.github.io/news/2408gorman/Fri, 02 Aug 2024 09:29:16 +1000https://m2lines.github.io/news/2408gorman/Accurately modeling extreme weather events is a major goal in climate science and weather risk management. High-resolution computational models provide precise data but are costly for capturing rare events. This study focuses on localized, short-lived events like heavy precipitation. The researchers, including Paul O’Gorman, developed a more efficient method for sampling these events by combining adaptive multilevel splitting (AMS) and “ensemble boosting.” The new approach, tested on the Lorenz-96 model, improved the sampling of extreme events by a factor of 10.Sampling Hybrid Climate Simulation at Scalehttps://m2lines.github.io/news/2407gentine/Tue, 02 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407gentine/Machine-learning (ML) models could enhance climate simulations by accurately representing small-scale processes like turbulence and convection. However, it’s unclear if better standalone (offline) performance leads to better integrated (online) performance in climate models. In this preprint, researchers, including Pierre Gentine, conducted extensive experiments with 2,970 hybrid simulations, finding that reducing offline error generally improves online accuracy, but some decisions can destabilize the model. Key improvements include incorporating memory, training on diverse climates, converting moisture input to relative humidity, and avoiding certain error metrics.Improved Equatorial Upper Ocean Vertical Mixinghttps://m2lines.github.io/news/2407reichl/Mon, 01 Jul 2024 09:29:16 +1000https://m2lines.github.io/news/2407reichl/Deficiencies in how upper ocean mixing is modeled cause biases in climate simulations, affecting ocean heat uptake and ENSO predictions. To address this, researchers evaluated different mixing models, To address this, researchers evaluated different mixing models, in this study led by Brandon Reichl, using detailed simulations and applied improvements to NOAA/GFDL’s OM4 ocean model. Enhancements to the model’s mixing were informed by observational data and led to more accurate diurnal mixing, ocean currents, and upper ocean temperature profiles.Transfer Learning for Emulating Ocean Climate Variabilityhttps://m2lines.github.io/news/2406dheeshjith/Sun, 02 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406dheeshjith/We are adding the link to the preprint which is now accepted in ICML 2024 workshop and will be presented as a talk and a poster this week in Vienna: Link to the paper . This study, led jointly by Surya Dheeshjith and Adam Subel, showcases Machine Learning Ocean Emulators accurately predicting global ocean surface conditions over 5-8 years under different CO₂ forcing scenarios. While the models struggle to generalize, fine-tuning with small amounts of additional data from warner climates can significantly improve model performance.Data-driven dimensionality reduction and causal inference for spatiotemporal climate fieldshttps://m2lines.github.io/news/2406falasca/Sat, 01 Jun 2024 09:29:16 +1000https://m2lines.github.io/news/2406falasca/One of the main challenges in statistical inference is to infer causal relations rather than simple correlations. In this paper , Fabrizio Falasca, Pavel Perezhogin, and Laure Zanna, propose a framework, based on linear response theory, to investigate and diagnose causal mechanisms in the climate system directly from data. The method is physically-based and scales to high-dimensional systems. Possible future applications include the response of the climate system to external perturbations and evaluate climate models outputs.FTORCH and FORPY libraries now integrated to CESM!https://m2lines.github.io/news/2405chapman/Thu, 02 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405chapman/The M²LInES team, assisted by Jack Atkinson (ICCS) and NCAR software engineers, have integrated the FTORCH and FORPY libraries for machine learning into CESM2.1.5. To set up a machine learning-enabled CESM instance on NCAR machines, follow the steps outlined in this repository . Contact Will Chapman for questions!Predicting September Arctic Sea Icehttps://m2lines.github.io/news/2405bushuk/Wed, 01 May 2024 09:29:16 +1000https://m2lines.github.io/news/2405bushuk/This study , led by Mitch Bushuk, provides a comparison of September Arctic sea ice seasonal prediction skill across a diverse set of 34 dynamical and statistical prediction models, quantifying the state-of-the-art in the rapidly growing sea ice prediction research community. The authors find that both dynamical and statistical prediction models can skillfully predict September Arctic sea ice 0–3 months in advance on Pan-Arctic, regional, and local spatial scales. These results demonstrate that there are bright prospects for skillful operational seasonal predictions of Arctic sea ice and highlight a number of crucial prediction system design aspects to guide future improvements.Advances in Machine Learning Techniques for Sea Ice Applicationshttps://m2lines.github.io/news/2404zampieri/Wed, 03 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404zampieri/Forty-two international researchers, including Lorenzo Zampieri, were invited to Gwangmyeong (South Korea) to discuss recent advances in sea ice modelling to help guide the development of a state-of-the-art Korean sea ice modelling program under the leadership of the Korean Polar Research Institute (KOPRI). The workshop produced a report , which highlights 1) the opportunity to use AI techniques in support of various sea ice modelling tasks both for short-term and climate predictions and 2) the need for strengthened collaborations among international groups to connect existing “research bubbles” and facilitate the dissemination of ideas.Joint Parameter and Parameterization Inferencehttps://m2lines.github.io/news/2404qu/Tue, 02 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404qu/In this article , Yongquan Qu, Mohamed Aziz Bhouri, and Pierre Gentine, tackle the joint inference and uncertainty quantification of poorly known physical parameters and machine learning parametrizations, for sub-grid scale dynamics. Achieved through a Bayesian framework enabled by differentiable programming, this method not only offers accurate parameter estimates, but also enables skillful forecasting, each accompanied by quantified uncertainty. This work is accepted at the ICLR 2024 Workshop on AI4Differential Equations In Science.Building Ocean Climate Emulatorshttps://m2lines.github.io/news/2404adam/Mon, 01 Apr 2024 09:29:16 +1000https://m2lines.github.io/news/2404adam/AI can accelerate climate modeling by generating computationally cheap emulators of existing simulations. The design of climate emulators is an exciting and open challenge in AI+Climate. In this work , PhD student Adam Subel and Laure Zanna focus on two fundamental questions to facilitate the creation of ocean emulators: 1) the role of the atmosphere in improving the decadal skill of the emulator and 2) the representation of variables with distinct timescales (e.Stress-testing the coupled behavior of hybrid physics-machine simulationshttps://m2lines.github.io/news/2402bhouri/Sat, 03 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402bhouri/Machine Learning (ML)-based parameterizations of atmospheric convection have long been hailed as a promising alternative, with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. This work , co-authored by Mohamed Aziz Bhouri, investigates the coupled out-of-distribution extrapolation capabilities in “online” testing of different ML-based parameterization designs of atmospheric convection. Their results show that these design decisions are not enough to obtain satisfactory generalization benefits.Stochastic Optimal Control Matching by Joanhttps://m2lines.github.io/news/2402joan/Fri, 02 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402joan/Joan Bruna is a co-author in this preprint , introducing Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control. Stochastic optimal control’s goal is to drive the behavior of noisy systems. In this work, the control is learned via a least squares problem by trying to fit a matching vector field. The key idea underlying SOCM is the path-wise reparameterization trick, a novel technique that is of independent interest, e.Restratification Effect of Mesoscale Eddieshttps://m2lines.github.io/news/2402nora/Thu, 01 Feb 2024 09:29:16 +1000https://m2lines.github.io/news/2402nora/Nora Loose is lead author of this new paper - part of the Climate Process Team funded by NSF and NOAA - which compares two parameterizations (“GM90” and “GL90”) for the restratification effect of mesoscale eddies. The authors conclude that the less commonly used GL90 parameterization is a promising alternative to the popular GM90 scheme for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable.Data-driven equation discovery ocean modelhttps://m2lines.github.io/news/2401pavel/Tue, 02 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401pavel/Pavel Perezhogin is lead author of this new M²LInES preprint , describing the implementation, in the GFDL MOM6 ocean model, of a data-driven equation-discovery parameterization of mesoscale eddies. This scale-aware parametrization improved biases in the mean flow and energetics for a range of resolutions and in different ocean configurations. The work was done in collaboration with Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda and Laure Zanna.Learning Machine Learning with Lorenz-96https://m2lines.github.io/news/2401l96/Mon, 01 Jan 2024 09:29:16 +1000https://m2lines.github.io/news/2401l96/The M²LInES team is proud to share our Jupyter Book on Learning Machine Learning with Lorenz-96 . This educational tool provides a computationally accessible framework to understand how machine learning techniques can tackle climate science problems, including emulators, parameterizations, data assimilation, and uncertainty quantification. It is for all climate scientists wanting to learn or test machine learning algorithms or for machine learning experts to learn about climate modeling or develop new algorithms.ClimSim awarded Best Paper Award at NeurIPShttps://m2lines.github.io/news/2312climsim/Tue, 12 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312climsim/ClimSim has been awarded Best Paper Award at NeurIPS ! The open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, emerged out of the Science and Technology Center LEAP, received contributions from numerous M²LInES members. Congratulations to all authors!Will Gregory's latest article featured in Advance Science Newshttps://m2lines.github.io/news/2312willgfeature/Fri, 01 Dec 2023 09:29:16 +1000https://m2lines.github.io/news/2312willgfeature/The latest M²LInES article, led by Will Gregory (Princeton) is being featured in Advance Science News. The article details how Will and other colleagues, including M²LInES members Mitch Bushuk, Alistair Adcroft, and Laure Zanna, are using Machine Learning to enhance predictions of Arctic sea ice loss. Read the article by Giorgio Graffino hereOceanBench by J. Emmanuel Johnsonhttps://m2lines.github.io/news/2311johnson/Wed, 01 Nov 2023 09:29:16 +1000https://m2lines.github.io/news/2311johnson/OceanBench is a suite of tools for designing experiments relevant to the oceanography community like interpolation, forecasting and estimation. Lead author J. Emmanuel Johnson and co-authors, including Anastasiia Gorbunova and Julien Le Sommer, demonstrate its use within a benchmark setting for sea surface height interpolation, using general circulation model simulations and real satellite observations.UNetKF by Feiyu Luhttps://m2lines.github.io/news/2310lu/Tue, 03 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310lu/This study by Feiyu Lu incorporates deep learning methods to compliment and improve the ensemble Kalman filter data assimilation algorithm. More specifically, a convolutional neural network is trained to predict the error statistics of a model ensemble, which will reduce the computational costs of the ensemble Kalman filter. This approach is tested in a quasi-geostrophic dynamic model to demonstrate its feasibility in more advanced weather and climate applications.Multi-fidelity climate model parameterization by Mohamed Aziz Bhourihttps://m2lines.github.io/news/2310bhouri/Mon, 02 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310bhouri/In this preprint , Mohamed Aziz Bhouri and co-authors, including Pierre Gentine, developed a probabilistic multi-fidelity atmospheric parameterization for heat and moisture tendency. They implemented it on CESM CAM5 and SPCAM5 data and showed its capabilities to well extrapolate to unseen warmer climate simulations while returning trustworthy uncertainty quantification.Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Modelhttps://m2lines.github.io/news/2310chapman/Sun, 01 Oct 2023 09:29:16 +1000https://m2lines.github.io/news/2310chapman/Stochastic and deterministic tendency adjustments are applied to multiple experiments in the community atmosphere model version 6. The tendency adjustments are gleaned from the DART data assimilation system and a linear relaxation towards ERA-interim reanalysis. In this preprint , Will Chapman and Judith Berner show that the tendency adjustment significantly enhances the representation of the background state climatology for key prognostic and surface variables. Additionally, they show that stochastic tendencies are essential to accurately represent model variability of major atmospheric modes when making tendency adjustments. Blog Post - A New Generation of Climate Modelshttps://m2lines.github.io/news/2309blogpost/Mon, 11 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309blogpost/M²LInES can now demonstrate the impact of data-driven Machine Learning parameterizations in global models! 🌎You can read about new gen climate models with AI in our blog postNew climate simulation model ensemble by Marika Hollandhttps://m2lines.github.io/news/2309holland/Mon, 04 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309holland/Climate simulation uncertainties arise from internal variability, model structure, and external forcings. These uncertainty sources are difficult to disentangle across model generations because both model structure and external forcings typically change. This preprint , led by Marika Holland, presents new ensemble simulations which allow for the separation of uncertainty sources between two large ensembles (LEs) of the Community Earth System Model (CESM). Importantly, we find a strong influence of historical forcing uncertainty in differences between CESM1-LE and CESM2-LE due to aerosol effects on simulated climate.Understanding Extreme Precipitation Changes by Griffin Mooershttps://m2lines.github.io/news/2309mooers/Sun, 03 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309mooers/Griffin Mooers is lead author in this preprint that leverages Variational Autoencoders on SPCAM5 output to examine the changes in the upper quantiles of precipitation between a control and warmed simulation. They find intense precipitation changes are mostly controlled by spatial shifts in deep convection.Reliable coarse-grained turbulent simulations by Chris Pedersonhttps://m2lines.github.io/news/2309pederson/Sat, 02 Sep 2023 09:29:16 +1000https://m2lines.github.io/news/2309pederson/In this preprint , M²LInES postdoc Chris Pedersen, along with Pavel Perezhogin, Joan Bruna, and Laure Zanna, show that the standard offline learning paradigm used to produce ML models of unresolved dynamics can be complemented with some learned time evolution of the system, and produce more stable hybrid physics and ML models.Background Pycnocline depth constrains FOHUE by Emily Newsomhttps://m2lines.github.io/news/2308newsom/Thu, 03 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308newsom/In this paper , Emily Newsom and co-authors Laure Zanna and Jonathan Gregory, showed that the relative depth of ocean heat sequestration, which varies widely across climate models and paces the rate of surface climate warming, is strongly correlated to interior ocean stratification. Using a novel regional decomposition method, they showed that this relationship is entirely driven by mid-latitude ventilation processes and explains 70% of the variance in heat penetration depth across CMIP5/CMIP6 models.ClimSim: LEAP publicationhttps://m2lines.github.io/news/2308bhouri/Wed, 02 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308bhouri/Several of M²LInES members and affiliates (Bhouri, Gentine, Zanna, Abernathey, Busecke, Mooers, Shamekh, and Yuval) have contributed to this LEAP paper presenting ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. The data and code are open access.Learning Atmospheric Boundary Layer Turbulence by Sara Shamekhhttps://m2lines.github.io/news/2308shamekh/Tue, 01 Aug 2023 09:29:16 +1000https://m2lines.github.io/news/2308shamekh/This preprint , by Sara Shamekh and Pierre Gentine, proposes a novel data-driven parameterization of vertical turbulent fluxes in convective boundary layer, that models the fluxes of various scalars across turbulent regimes. By incorporating a physics-based constraint this approach allows us to decompose the total vertical flux into two main modes of variability based on large scale forces that control the turbulence in the atmosphere.Parameterizing vertical mixing coefficients in the Ocean by Akash Sanehttps://m2lines.github.io/news/2307sane/Sat, 01 Jul 2023 09:29:16 +1000https://m2lines.github.io/news/2307sane/Small scale vertical mixing processes in the upper ocean region cannot be resolved by ocean models due to the lack of computing resources, and are thus parameterized. Existing models have a few ad hoc approximations which cause uncertainty in climate change simulations. In this breakthrough preprint , Aakash Sane and co-authors - Brandon Reichl, Alistair Adcroft, and Laure Zanna - replace such an approximation using data driven neural networks (NN), leading to improved physics in an ocean model simulation and a reduction of model bias in the Tropics.Smart correction model for winter sky temperatures by Lorenzo Zampierihttps://m2lines.github.io/news/2306zampieri/Thu, 01 Jun 2023 09:29:16 +1000https://m2lines.github.io/news/2306zampieri/This study , led by Lorenzo Zampieri, and co-authored by Marika Holland, illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea-ice-covered regions of the Arctic under clear-sky conditions. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.Data-driven subgrid parameterizations by Karl Otnesshttps://m2lines.github.io/news/2305otness/Mon, 01 May 2023 09:29:16 +1000https://m2lines.github.io/news/2305otness/In this ongoing project , Karl Otness and co-authors are evaluating a proof of concept multiscale approach to predicting subgrid forcings in climate models. They see encouraging preliminary results from first making a prediction in a fine-to-coarse direction which yields a lower resolution, but more confident prediction followed by a refinement to predict finer scale details. This paper won “Best ML innovation” at the ICLR 2023 Workshop: Tackling Climate Change with Machine Learning.Emulating Cloud Superparameterization by Pierre Gentinehttps://m2lines.github.io/news/2301gentine/Tue, 03 Jan 2023 09:29:16 +1000https://m2lines.github.io/news/2301gentine/M²LInES PI Pierre Gentine is the senior author in this new publication on using deep learning for emulating cloud superparameterization in climate models. Research Scientist Tom Beucler is a co-author. Read their paper here M²LInES at AGU 22https://m2lines.github.io/news/2212agu22/Tue, 06 Dec 2022 09:29:16 +1000https://m2lines.github.io/news/2212agu22/AGU M²LInES Highlights The M²LInES team is excited to present our recent scientific advances at the Fall 2022 AGU meeting in Chicago and online. Below is a summary of the talks (all times are CST). For your convenience, you can download the schedule for the talks by following this link . Machine learning for physics discovery & modeling We have an extensive lineup of talks and posters from M²LInES team members and affiliates. Direct observational evidence of an oceanic dual kinetic energy cascade and its seasonalityhttps://m2lines.github.io/news/2211balwada/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211balwada/In this article , led by Dhruv Balwada, ocean drifter observations show wave and turbulence merge and split flows, simultaneously transferring energy to large and small scales. Implicit learning of convective organization explains precipitation stochasticityhttps://m2lines.github.io/news/2211shamekh/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211shamekh/Cloud and convection take many structure and degrees of organization, which are still absent in most parameterizations. But does that structure matter for precipitation? This preprint , led by Sara Shamekh, proposes an elegant method to include organization in the parameterization of precipitation that results in a significant improvement in predicting precipitation, especially extremes, at GCM scale. Ocean currents break up a tabular iceberghttps://m2lines.github.io/news/2211adcroft/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211adcroft/This article examines the mechanism that led to the breakup of massive iceberg A68 in Dec 2020. The authors, including Alistair Adcroft, demonstrate that the A68a breakup event may have been triggered by ocean-current shear, a new breakup mechanism not previously reported. Their paper was featured in the news . On Gradient Descent Convergence beyond the Edge of Stabilityhttps://m2lines.github.io/news/2211bruna/Thu, 03 Nov 2022 09:29:16 +1000https://m2lines.github.io/news/2211bruna/Joan Bruna is the senior author in this preprint on Gradient Descent (GD) convergence in unstable conditions. The study examines the conditions for such unstable convergence, focusing on simple, yet representative, learning problems. A year-round satellite sea-ice thickness record from CryoSat-2https://m2lines.github.io/news/2210bushuk/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210bushuk/This study , co-authored by Mitch Bushuk, produced the first ever year-round satellite observational record of Arctic sea ice thickness, employing a deep learning model to produce estimates of sea ice freeboard. These data span the period 2010-2020 and will be critical for better monitoring the changing Arctic climate, advancing climate model evaluation, and improving sea ice predictions. Comparing Storm Resolving Models and Climates via Unsupervised Machine Learninghttps://m2lines.github.io/news/2210gentine/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210gentine/High resolution data is overwhelming and this paper , co-authored by Pierre Gentine and Tom Beucler, tries to define an unsupervised machine learning technique to better compare Storm Resolving Climate Models (SRMs). Their analysis found that only six out of nine considered SRMs are dynamically consistent. This split among SRMs highlights the need to further investigate sub-grid parameterization choices. Semi-automatic tuning of coupled climate models with multiple intrinsic timescaleshttps://m2lines.github.io/news/2210deshayes/Mon, 03 Oct 2022 09:29:16 +1000https://m2lines.github.io/news/2210deshayes/Using the two-scale Lorenz96 model, this article evaluates the potential of History Matching to tune a climate system with multi-scale dynamics. This semi-automatic tuning was more efficient when combined with physical expertise. Julie Deshayes and V Balaji are co-authors on this study. Benchmarking of machine learning ocean subgrid parameterizations in an idealized modelhttps://m2lines.github.io/news/2208ross/Tue, 02 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208ross/In this work , led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Laure Zanna, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations. Cloud-based framework for inter-comparing submesoscale permitting realistic ocean modelshttps://m2lines.github.io/news/2208uchida/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208uchida/This study presents a cloud-based workflow for intercomparing high-resolution ocean models and provides an assessment of one of the most used parameterizations of ocean submesoscales. Julien Le Sommer, Ryan Abernathey, and Aurelie Albert contributed to the research. Non-Linear Dimensionality Reduction with a VED to Understand Convective Processes in Climate Modelshttps://m2lines.github.io/news/2208behrens/Mon, 01 Aug 2022 09:29:16 +1000https://m2lines.github.io/news/2208behrens/Pierre Gentine and Tom Beucler are co-authors in this article , which demonstrates that Variational Encoder Decoder structures (VED) are capable of learning and accurately reproducing convective processes in an aquaplanet superparameterized climate model simulation, while enabling interpretability and better understanding of sub-grid-scale physical processes. A potential energy analysis of ocean surface mixed layers - Brandon Reichlhttps://m2lines.github.io/news/2207reichl/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207reichl/Analysis of the ocean mixed layer requires one to estimate its vertical extent, for which there are various definitions. In this paper , Brandon Reichl and co-authors, including Alistair Adcroft, show that using the potential energy anomaly to identify the mixed layer depth is practical and it offers conceptual benefits by directly linking to the physics of upper ocean vertical mixing. New preprint led by Adam Subel on transfer learninghttps://m2lines.github.io/news/2207subel/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207subel/Adam Subel is the lead author in this article , which builds a framework to understand and implement transfer learning, a powerful machine learning technique for enabling generalization between datasets from different configurations of a system or of varying quality/quantity, in multiscale physical systems. Subseasonal Earth System Prediction with CESM2 - Judith Bernerhttps://m2lines.github.io/news/2207berner/Tue, 05 Jul 2022 09:29:16 +1000https://m2lines.github.io/news/2207berner/Judith Berner is a co-author in this article , which describes the design and prediction skill of two subseasonal prediction systems based on two configurations of the Community Earth System Model, version 2 (CESM2): with the Community Atmosphere Model and with Whole Atmosphere Community Climate Model as its atmospheric component. Both systems are compared to NOAA CFSv2 and ECMWF. Deep Learning for Subgrid-Scale Turbulence Modeling in LES of the Convective Atmospheric Boundary Layerhttps://m2lines.github.io/news/gentine-abernathey/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gentine-abernathey/This article , co-authored by Pierre Gentine and Ryan Abernathey, replaces the typically used physically based assumptions with deep learning in subgrid-scale modeling, in large eddy simulations of turbulence. They show that the latter performs better in a priori (offline) tests of atmospheric turbulence. The deep neural networks model also captures key statistics of turbulence in a posteriori (online) tests. Lorenzo Zampieri - A machine learning correction modelhttps://m2lines.github.io/news/zampieri2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/zampieri2022/Lorenzo Zampieri et al. illustrate in this preprint , a novel method based on ML for reducing the surface temperature errors of atmospheric reanalyses in sea-ice-covered regions of the Arctic. The corrected temperature can be employed to support polar research activities, and to better simulate the evolution of the interacting sea ice and ocean systems in numerical models. Will Gregory - Network connectivity articlehttps://m2lines.github.io/news/gregory2022/Tue, 07 Jun 2022 09:29:16 +1000https://m2lines.github.io/news/gregory2022/Will Gregory and co-authors hypothesized in this publication that regional biases in sea ice thickness may hinder the ability of CMIP6 models to reproduce an important atmospheric driver of summer sea ice variability, and hence their ability to make reliable sea ice predictions on seasonal to inter-annual time scales. 2022 CESM workshop: Info and submission deadlinehttps://m2lines.github.io/news/cesmworkshop2022/Mon, 02 May 2022 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop2022/Registration is open for the 2022 CESM Workshop on June 13-16, 2022. Please visit the website to register as well as submit working group session talks and /or posters. The deadline to submit a talk / poster is Monday, May 16th. diff --git a/news/newsletters/index.html b/news/newsletters/index.html index 0cc17d85..415c5fb6 100644 --- a/news/newsletters/index.html +++ b/news/newsletters/index.html @@ -9,6 +9,6 @@

    Newsletters

    Subscribe to our newsletter

    * indicates required

    Links to our past newsletters are below.

    2024

    2023

    2022

    2021

    • This project is supported by Schmidt Sciences, LLC.

    Links to our past newsletters are below.

    2024

    2023

    2022

    2021

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/publications/index.html b/publications/index.html index a8d5a30d..df9e4e85 100644 --- a/publications/index.html +++ b/publications/index.html @@ -9,18 +9,23 @@

    Publications

    Research work and relevant papers by our team

    M²LInES research and other relevant publications

    If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook Learning Machine Learning for Climate modeling with Lorenz 96 -. This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub.

    DOI icon M²LInES funded research

    2024

    Dmitrii Kochkov, ... Stephan Hoyer
    Neural general circulation models for weather and climate
    Nature 2024 DOI: 10.1038/s41586-024-07744-y

    DOI icon +. This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub.

    DOI icon M²LInES funded research

    2024

    DOI icon +William E. Chapman and Judith Berner
    A State-Dependent Model-Error Representation for Online Climate Model Bias Correction
    Preprint in ESS DOI:10.22541/essoar.172526800.05354621/v2

    DOI icon +John Schreck, Yingkai Sha, William Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, David John Gagne II
    Community Research Earth Digital Intelligence Twin (CREDIT)
    ArXiv 2024 DOI: 10.48550/arXiv.2411.07814

    DOI icon +Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
    A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization
    JAMES DOI: 10.1029/2023MS004104

    DOI icon +Brandon G Reichl, Andrew T. Wittenberg, Stephen M Griffies, Alistair Adcroft
    Improved Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model
    Earth and Space Science DOI:10.1029/2023EA003485

    DOI icon +Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
    Neural Galerkin schemes with active learning for high-dimensional evolution equations
    Journal of Computational Physics DOI:10.1016/j.jcp.2023.112588

    Dmitrii Kochkov, ... Stephan Hoyer
    Neural general circulation models for weather and climate
    Nature 2024 DOI: 10.1038/s41586-024-07744-y

    DOI icon Tom Beucler, Arthur Grundner, Sara Shamekh, Peter Ukkonen, Matthew Chantry, Ryan Lagerquist
    Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
    ArXiv 2024 DOI: 10.48550/arXiv.2408.02161

    DOI icon William Gregory, Ronald MacEachern, So Takao, Isobel R. Lawrence, Carmen Nab, Marc Peter Deisenroth, Michel Tsamados
    Scalable interpolation of satellite altimetry data with probabilistic machine learning
    Nature Communications 2024 DOI: 10.1038/s41467-024-51900-x

    DOI icon -Veronika Eyring, ... Laure Zanna
    Pushing the frontiers in climate modelling and analysis with machine learning
    Nature Climate Change 2024 DOI: 10.1038/s41558-024-02095-y

    Jerry Lin, Sungduk Yu, Liran Peng, Tom Beucler, Eliot Wong-Toi, Zeyuan Hu, Pierre Gentine, Margarita Geleta, Michael S Pritchard
    Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine Learning Parameterization
    Preprint submitted to JAMES DOI:10.22541/essoar.172072688.86581349/v1

    DOI icon -Brandon G Reichl, Andrew T. Wittenberg, Stephen M Griffies, Alistair Adcroft
    Improved Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model
    Preprint submitted to ESS DOI:10.22541/essoar.170785794.47537760/v1

    DOI icon +Veronika Eyring, ... Laure Zanna
    Pushing the frontiers in climate modelling and analysis with machine learning
    Nature Climate Change 2024 DOI: 10.1038/s41558-024-02095-y

    Jerry Lin, Sungduk Yu, Liran Peng, Tom Beucler, Eliot Wong-Toi, Zeyuan Hu, Pierre Gentine, Margarita Geleta, Michael S Pritchard
    Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine Learning Parameterization
    Preprint submitted to JAMES DOI:10.22541/essoar.172072688.86581349/v1

    DOI icon Surya Dheeshjith, Adam Subel, Shubham Gupta, Alistair Adcroft, Carlos Fernandez-Granda, Julius Busecke, Laure Zanna
    Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing
    Preprint accepted at ICML 2024 ML4ESM DOI: 10.48550/arXiv.2405.18585

    Justin Finkel and Paul A. O’Gorman
    Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events
    JAMES 2024 DOI: 10.1029/2024MS004264

    David Bonan, Andrew Thompson, Tapio Schneider, Laure Zanna, Kyle Armour, Shantong Sun
    Constraints imply limited future weakening of Atlantic meridional overturning circulation
    Preprint under review at Nature Portfolio DOI: doi.org/10.21203/rs.3.rs-4456168/v1

    DOI icon Lorenzo Zampieri, David Clemens-Sewall, Anne Sledd, Nils Hutter, Marika Holland
    Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC
    Geophysical Research Letters 2024 DOI: 10.1029/2023GL106760

    DOI icon William Gregory, Ronald MacEachern, So Takao, Isobel Lawrence, Carmen Nab, Marc Deisenroth, Michel Tsamados
    Scalable interpolation of satellite altimetry data with probabilistic machine learning
    Nature Comms. 2024 DOI: 10.21203/rs.3.rs-4209064/v1

    DOI icon Adam Subel, Laure Zanna
    Building Ocean Climate Emulators
    ICLR 2024 Workshop: Tackling Climate Change with Machine Learning. DOI: 10.48550/arXiv.2402.04342

    DOI icon William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna
    Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
    Geophysical Research Letters 2024. DOI: 10.1029/2023GL106776

    DOI icon Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
    Data-driven framework for dimensionality reduction and causal inference in climate fields
    APS Physics Review E 2024 DOI: 10.1103/PhysRevE.109.044202

    DOI icon -Tom Beucler, ... Pierre Gentine
    Climate-Invariant Machine Learning
    Science Advances 2024 DOI: 10.1126/sciadv.adj7250

    2023

    DOI icon +Tom Beucler, ... Pierre Gentine
    Climate-Invariant Machine Learning
    Science Advances 2024 DOI: 10.1126/sciadv.adj7250

    2023

    DOI icon +Abigail Bodner, Dhruv Balwada, Laure Zanna
    A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer
    Arxiv 2023. DOI: 10.48550/arXiv.2312.06972

    DOI icon Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
    Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model
    James 2023. DOI: 10.1029/2023MS003697

    DOI icon Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
    Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
    James 2023. DOI: 10.48550/arXiv.2311.02517

    DOI icon Will Chapman and Judith Berner
    Deterministic and stochastic tendency adjustments derivedfrom data assimilation and nudging
    QJRMS 2023. DOI: 10.1002/qj.4652

    DOI icon diff --git a/research/talks/index.html b/research/talks/index.html index f6956529..c4e675bb 100644 --- a/research/talks/index.html +++ b/research/talks/index.html @@ -8,7 +8,7 @@

  • Resources
  • News

    • Talks

    Your can find most of our past talks, and much more, on our Youtube Channel

    Themes of the talks:

    • 📊 Big data
    • 💻 Machine Learning
    • 🎆 Physics discovery
    • 🌎 Modeling

    Recent talks

    2024

    Video Preview

    Aakash Sane
    Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks
    CESM OMWG - February 💻 🎆

    2023

    Video Preview

    Pavel Perezhogin
    Machine-Learned parameterizations of mesoscale eddies in MOM6 ocean model: convolutional neural network and symbolic regression
    28th Annual CESM Workshop - June 💻 🎆

    Video Preview

    Laure Zanna
    Ocean and Atmosphere Dynamics
    Climatematch academy - July 💻 🎆

    Video Preview

    Fabrizio Falasca
    Exploting the nonstationarity of coastal sea level probability distributions
    NOAA-CVP Webinar Series 2023 - April 💻 🎆

    Video Preview

    Paul O'Gorman
    Improving climate models using machine learning
    MIT Generative AI Week - November 💻 🎆

    Video Preview

    Laure Zanna
    Transforming Climate Modeling with AI: hype or Reality?
    UN AI for Good - March 💻 🎆

    Video Preview

    Pavel Perezhogin
    Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
    NEMO working group on Machine-Learning - April 💻 🎆

    2022

    Video Preview

    Janni Yuval
    Neural-network parameterization of subgrid momentum transport learned from a high-resolution simulation
    23rd AMS Conference on Atmospheric and Oceanic Fluid Dynamics - June 💻 🎆

    Video Preview

    Aakash Sane
    Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Neural Networks
    February 💻 🎆

    Video Preview

    Lorenzo Zampieri
    On the design strategy of subgrid parameterizations in modern sea ice models
    Arctic Model Parameterization Development - December 22nd 💻 🎆

    Video Preview

    Ryan Abernathey
    Climate Science and AI in Big Tech
    Climate Change AI Webinar Series - December 15th 📊 💻
    NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

    Video Preview

    Pierre Gentine
    Physics to Machine Learning and Machine Learning Back to Physics
    Columbia Center of AI Technology - December 7th 💻 🎆

    Video Preview

    Laure Zanna and Steven Brunton
    Machine learning in fluid dynamics and climate physics
    Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

    Video Preview

    Laure Zanna
    Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
    LEAP Seminar - September 22nd 💻 🎆

    Video Preview

    Laure Zanna
    AI for Climate Physics
    Hammers and Nails - August 4th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Plenary talk
    SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
    NOTE: More info at this website.

    Video Preview

    Laure Zanna
    Joint plenary talk with Galen McKinley on M²LInES and LEAP
    2022 CESM Workshop - June 13th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Machine Learning for Mesoscale Closures in Ocean Models
    NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

    Video Preview

    Pierre Gentine
    Can Artificial Intelligence Help Us Better Project Climate Change?
    The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

    Video Preview

    Andrew Ross
    Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
    NOTE: You can find the panel on the top main video at 7:49:25 here .

    Video Preview

    Laure Zanna
    Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
    Ocean Sciences Meeting - March 4th 💻 🎆 🌎

    Video Preview

    Mitch Bushuk
    Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
    Ocean Sciences Meeting - March 3rd 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud: A New Approach to Ocean Data and Computing
    Ocean Sciences Meeting - March 3rd 📊

    Video Preview

    Lorenzo Zampieri
    A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
    Ocean Sciences Meeting - March 1st 💻 🎆

    Video Preview

    Dhruv Balwada
    Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
    Ocean Sciences Meeting - March 1st 🎆

    Video Preview

    Brandon Reichl
    A potential energy analysis of ocean surface mixed layers
    Ocean Sciences Meeting - February 28th

    Video Preview

    Andrew Ross
    Evaluating machine learning parameterizations of ocean subgrid forcing
    Ocean Sciences Meeting - February 28th 💻 🎆

    Video Preview

    Dhruv Balwada
    Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
    Ocean Sciences Meeting - February 28th 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud - Transforming oceanography with a new approach to data and computing
    Ocean Data Conference - February 15th 📊

    Video Preview

    Ryan Abernathey
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

    Video Preview

    Pierre Gentine
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

    Video Preview

    Laure Zanna
    Data-driven turbulence closures for ocean and climate models: advances and challenges
    UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆



    Center Image
    Atmosphere +

    Talks

    Your can find most of our past talks, and much more, on our Youtube Channel

    Themes of the talks:

    • 📊 Big data
    • 💻 Machine Learning
    • 🎆 Physics discovery
    • 🌎 Modeling

    Recent talks

    2024

    Video Preview

    Pierre Gentine
    Harnessing AI to confront environmental challenges
    AI for Climate and Nature Spring Convening - Bezos Earth Fund - October 💻 🎆

    Video Preview

    Julia Simpson
    Improving Parameterizations of Heat + Momentum Air-Sea Fluxes
    LEAP Research Updates - September 💻 🎆

    Video Preview

    Laure Zanna
    (Keynote) AI-augmented climate Simulators and Emulators
    OMDP/COMMODORE Workshop - September 💻 🎆 🌎

    Video Preview

    Marika Holland
    The influence of historical forcing uncertainty on simulated Arctic change.
    IARPC Collaborations - August 📊 🌎

    Video Preview

    Laure Zanna, Sara Shamekh, Pierre Gentine
    Navigating the Academic Search Process
    LEAP Professional Development Series

    Video Preview

    Paul O'Gorman
    AMIT's BC3 and Climate and Weather Extremes
    ICCS Cross VESRI Journal - February 💻 🎆

    Video Preview

    Will Chapman
    Advancing Weather and Climate Prediction with Data Driven Methods
    Paul G. Allen School - February 💻 🌎

    Video Preview

    Laure Zanna
    Accelerating Discovery in Climate Physics with Machine Learning
    Simons foundation Presidential Lecture - February 💻 🎆

    Video Preview

    Pierre Gentine
    AI in Climate Science: From Emulation to New Discoveries
    Simons foundation Presidential Lecture - February 💻 🎆

    Video Preview

    Pierre Gentine
    Machine learning across scales to improve climate models: from emulation to discoveries
    Mathematics of Climate - February 💻 🎆 🌎

    Video Preview

    Aakash Sane
    Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks
    CESM OMWG - February 💻 🎆

    2023

    Video Preview

    Paul O'Gorman
    Improving climate models using machine learning
    MIT Generative AI Week - November 💻 🎆 🌎

    Video Preview

    Laure Zanna
    A New Generation of Global Climate Models Augmented by AI
    Distinguished Seminar Series in Computational Science and Engineering - October 2023 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Ocean and Atmosphere Dynamics
    Climatematch academy - July 💻 🎆

    Video Preview

    Pavel Perezhogin
    Machine-Learned parameterizations of mesoscale eddies in MOM6 ocean model: convolutional neural network and symbolic regression
    28th Annual CESM Workshop - June 💻 🌎

    Video Preview

    Fabrizio Falasca
    Exploting the nonstationarity of coastal sea level probability distributions
    NOAA-CVP Webinar Series 2023 - April 💻 🎆

    Video Preview

    Pavel Perezhogin
    Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
    NEMO working group on Machine-Learning - April 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Transforming Climate Modeling with AI: hype or Reality?
    UN AI for Good - March 💻 🌎

    2022

    Video Preview

    Janni Yuval
    Neural-network parameterization of subgrid momentum transport learned from a high-resolution simulation
    23rd AMS Conference on Atmospheric and Oceanic Fluid Dynamics - June 💻 🎆

    Video Preview

    Aakash Sane
    Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Neural Networks
    February 💻 🎆

    Video Preview

    Lorenzo Zampieri
    On the design strategy of subgrid parameterizations in modern sea ice models
    Arctic Model Parameterization Development - December 22nd 💻 🌎

    Video Preview

    Ryan Abernathey
    Climate Science and AI in Big Tech
    Climate Change AI Webinar Series - December 15th 📊 💻
    NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

    Video Preview

    Pierre Gentine
    Physics to Machine Learning and Machine Learning Back to Physics
    Columbia Center of AI Technology - December 7th 💻 🎆

    Video Preview

    Laure Zanna and Steven Brunton
    Machine learning in fluid dynamics and climate physics
    Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

    Video Preview

    Laure Zanna
    Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
    LEAP Seminar - September 22nd 💻 🎆 🌎

    Video Preview

    Laure Zanna
    AI for Climate Physics
    Hammers and Nails - August 4th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Plenary talk
    SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
    NOTE: More info at this website.

    Video Preview

    Laure Zanna
    Joint plenary talk with Galen McKinley on M²LInES and LEAP
    2022 CESM Workshop - June 13th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Machine Learning for Mesoscale Closures in Ocean Models
    NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

    Video Preview

    Pierre Gentine
    Can Artificial Intelligence Help Us Better Project Climate Change?
    The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

    Video Preview

    Andrew Ross
    Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
    NOTE: You can find the panel on the top main video at 7:49:25 here .

    Video Preview

    Laure Zanna
    Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
    Ocean Sciences Meeting - March 4th 💻 🎆 🌎

    Video Preview

    Mitch Bushuk
    Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
    Ocean Sciences Meeting - March 3rd 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud: A New Approach to Ocean Data and Computing
    Ocean Sciences Meeting - March 3rd 📊

    Video Preview

    Lorenzo Zampieri
    A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
    Ocean Sciences Meeting - March 1st 💻 🎆

    Video Preview

    Dhruv Balwada
    Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
    Ocean Sciences Meeting - March 1st 🎆

    Video Preview

    Brandon Reichl
    A potential energy analysis of ocean surface mixed layers
    Ocean Sciences Meeting - February 28th

    Video Preview

    Andrew Ross
    Evaluating machine learning parameterizations of ocean subgrid forcing
    Ocean Sciences Meeting - February 28th 💻 🎆

    Video Preview

    Dhruv Balwada
    Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
    Ocean Sciences Meeting - February 28th 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud - Transforming oceanography with a new approach to data and computing
    Ocean Data Conference - February 15th 📊

    Video Preview

    Ryan Abernathey
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

    Video Preview

    Pierre Gentine
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

    Video Preview

    Laure Zanna
    Data-driven turbulence closures for ocean and climate models: advances and challenges
    UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆



    Center Image
    Atmosphere Climate Model Development Coupled Physics Data Assimilation diff --git a/sitemap.xml b/sitemap.xml index f3bbf968..bceaa5fa 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1 +1 @@ -https://m2lines.github.io/team/laure-zanna/2018-11-19T10:47:58+10:00https://m2lines.github.io/blog/atmosphere/https://m2lines.github.io/blog/climate/https://m2lines.github.io/blog/coupledphysics/https://m2lines.github.io/blog/dataassimilation/https://m2lines.github.io/blog/climateprocess/https://m2lines.github.io/blog/machinelearning/https://m2lines.github.io/blog/ocean/https://m2lines.github.io/blog/projectgoals/https://m2lines.github.io/blog/seaice/https://m2lines.github.io/team/johannagoldman/2018-11-19T10:47:58+10:00https://m2lines.github.io/blog/research2/https://m2lines.github.io/blog/research4/https://m2lines.github.io/team/former/ziweili/https://m2lines.github.io/team/alistairadcroft/https://m2lines.github.io/team/carlosferndandezgranda/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/joan-bruna/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/ryanabernathy/https://m2lines.github.io/team/brandonreichl/https://m2lines.github.io/team/feiyulu/https://m2lines.github.io/team/judithberner/https://m2lines.github.io/team/marikaholland/https://m2lines.github.io/team/mitchbushuk/https://m2lines.github.io/team/pierregentine/https://m2lines.github.io/team/paulogorman/https://m2lines.github.io/team/juliedeshayes/https://m2lines.github.io/team/julienlesommer/https://m2lines.github.io/team/arthurguillaumin/https://m2lines.github.io/team/aurorabasinki/https://m2lines.github.io/team/janniyuval/https://m2lines.github.io/team/alexconnolly/https://m2lines.github.io/team/juliusbusecke/https://m2lines.github.io/team/aakashsane/https://m2lines.github.io/team/former/andrewross/https://m2lines.github.io/team/lorenzozampieri/https://m2lines.github.io/team/cemgultekin/https://m2lines.github.io/team/tarunverma/https://m2lines.github.io/team/dhruvbalwada/https://m2lines.github.io/team/chengzhang/https://m2lines.github.io/team/former/anastasiiagorbunova/https://m2lines.github.io/team/pavelperezhogin/https://m2lines.github.io/team/willchapman/https://m2lines.github.io/team/willgregory/https://m2lines.github.io/team/abigailbodner/https://m2lines.github.io/team/andrewbrettin/https://m2lines.github.io/team/former/marioherrerogonzalez/https://m2lines.github.io/team/karlotness/https://m2lines.github.io/team/former/azizbhouri/https://m2lines.github.io/team/former/hugofrezat/https://m2lines.github.io/team/former/adrienburq/https://m2lines.github.io/team/fabriziofalasca/https://m2lines.github.io/team/adamsubel/https://m2lines.github.io/team/alexisbarge/https://m2lines.github.io/team/maudtissot/https://m2lines.github.io/team/former/elizabethyankovsky/https://m2lines.github.io/team/sarashamekh/https://m2lines.github.io/team/former/chrisdupuis/https://m2lines.github.io/team/former/jemanjohnson/https://m2lines.github.io/team/chrispedersen/https://m2lines.github.io/team/former/emilynewsom/https://m2lines.github.io/team/etiennemeunier/https://m2lines.github.io/team/former/mariapratcolomer/https://m2lines.github.io/team/noraloose/https://m2lines.github.io/team/dannidu/https://m2lines.github.io/team/davidkamm/https://m2lines.github.io/team/gabrielmouttapa/https://m2lines.github.io/team/gretamiller/https://m2lines.github.io/team/jia-ruishi/https://m2lines.github.io/team/marcelacontreras/https://m2lines.github.io/team/shuchangliu/https://m2lines.github.io/team/former/patricialuc/https://m2lines.github.io/team/kelseyeverard/https://m2lines.github.io/team/former/matiasortiz/https://m2lines.github.io/team/former/shubham/https://m2lines.github.io/team/suryadheeshjith/https://m2lines.github.io/team/former/leoliu/https://m2lines.github.io/team/former/shantanu/https://m2lines.github.io/team/griffinmooers/https://m2lines.github.io/team/former/friedrichginnold/https://m2lines.github.io/team/praninalluri/https://m2lines.github.io/team/renaudfalga/https://m2lines.github.io/team/former/jisuhan/https://m2lines.github.io/team/juliasimpson/https://m2lines.github.io/team/jiarongwu/https://m2lines.github.io/team/yongquanqu-/https://m2lines.github.io/news/2024-10-02T09:29:16+10:00https://m2lines.github.io/news/2410yuval/2024-10-02T09:29:16+10:00https://m2lines.github.io/news/2410shamekh/2024-10-01T09:29:16+10:00https://m2lines.github.io/news/2409gentine/2024-09-02T09:29:16+10:00https://m2lines.github.io/news/2409gregory/2024-09-01T09:29:16+10:00https://m2lines.github.io/news/2408bonan/2024-08-03T09:29:16+10:00https://m2lines.github.io/news/2408gorman/2024-08-02T09:29:16+10:00https://m2lines.github.io/news/2407gentine/2024-07-02T09:29:16+10:00https://m2lines.github.io/news/2407reichl/2024-07-01T09:29:16+10:00https://m2lines.github.io/news/2406dheeshjith/2024-06-02T09:29:16+10:00https://m2lines.github.io/news/2406falasca/2024-06-01T09:29:16+10:00https://m2lines.github.io/news/2405chapman/2024-05-02T09:29:16+10:00https://m2lines.github.io/news/2405bushuk/2024-05-01T09:29:16+10:00https://m2lines.github.io/news/2404zampieri/2024-04-03T09:29:16+10:00https://m2lines.github.io/news/2404qu/2024-04-02T09:29:16+10:00https://m2lines.github.io/news/2404adam/2024-04-01T09:29:16+10:00https://m2lines.github.io/news/2402bhouri/2024-02-03T09:29:16+10:00https://m2lines.github.io/news/2402joan/2024-02-02T09:29:16+10:00https://m2lines.github.io/news/2402nora/2024-02-01T09:29:16+10:00https://m2lines.github.io/news/2401pavel/2024-01-02T09:29:16+10:00https://m2lines.github.io/news/2401l96/2024-01-01T09:29:16+10:00https://m2lines.github.io/news/2312climsim/2023-12-12T09:29:16+10:00https://m2lines.github.io/news/2312willgfeature/2023-12-01T09:29:16+10:00https://m2lines.github.io/news/2311johnson/2023-11-01T09:29:16+10:00https://m2lines.github.io/news/2310lu/2023-10-03T09:29:16+10:00https://m2lines.github.io/news/2310bhouri/2023-10-02T09:29:16+10:00https://m2lines.github.io/news/2310chapman/2023-10-01T09:29:16+10:00https://m2lines.github.io/news/2309blogpost/2023-09-11T09:29:16+10:00https://m2lines.github.io/news/2309holland/2023-09-04T09:29:16+10:00https://m2lines.github.io/news/2309mooers/2023-09-03T09:29:16+10:00https://m2lines.github.io/news/2309pederson/2023-09-02T09:29:16+10:00https://m2lines.github.io/news/2308newsom/2023-08-03T09:29:16+10:00https://m2lines.github.io/news/2308bhouri/2023-08-02T09:29:16+10:00https://m2lines.github.io/news/2308shamekh/2023-08-01T09:29:16+10:00https://m2lines.github.io/news/2307sane/2023-07-01T09:29:16+10:00https://m2lines.github.io/news/2306zampieri/2023-06-01T09:29:16+10:00https://m2lines.github.io/news/2305otness/2023-05-01T09:29:16+10:00https://m2lines.github.io/news/2301gentine/2023-01-03T09:29:16+10:00https://m2lines.github.io/news/2212agu22/2022-12-06T09:29:16+10:00https://m2lines.github.io/news/2211balwada/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211shamekh/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211adcroft/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211bruna/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2210bushuk/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2210gentine/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2210deshayes/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2208ross/2022-08-02T09:29:16+10:00https://m2lines.github.io/news/2208uchida/2022-08-01T09:29:16+10:00https://m2lines.github.io/news/2208behrens/2022-08-01T09:29:16+10:00https://m2lines.github.io/news/2207reichl/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/2207subel/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/2207berner/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/gentine-abernathey/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/zampieri2022/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/gregory2022/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/cesmworkshop2022/2022-05-02T09:29:16+10:00https://m2lines.github.io/news/gorbunova2021/2022-05-01T09:29:16+10:00https://m2lines.github.io/news/gcmfilter/2022-05-01T09:29:16+10:00https://m2lines.github.io/news/holland2022/2022-05-01T08:29:16+10:00https://m2lines.github.io/news/balaji2022/2022-05-01T07:29:16+10:00https://m2lines.github.io/news/220401subel/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/220401bruna/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/220401bushuk/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/balwadazanna22/2022-03-01T09:29:16+10:00https://m2lines.github.io/news/m2linesmovie/2022-02-02T09:29:16+10:00https://m2lines.github.io/news/sanepaper2022/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/abernatheychapter/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/beuclerpreprint/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/fernandezgranda21/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/frezatpreprint/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/ziweili21/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/agupresentation/2021-12-08T09:29:16+10:00https://m2lines.github.io/news/kickoffoct/2021-10-29T09:29:16+10:00https://m2lines.github.io/news/hollandetal/2021-10-28T09:29:16+10:00https://m2lines.github.io/news/chapmanetal/2021-10-14T09:29:16+10:00https://m2lines.github.io/news/zampierietal21/2021-10-01T09:29:16+10:00https://m2lines.github.io/news/aiforgood/2021-08-25T09:29:16+10:00https://m2lines.github.io/news/fernandezgranda/2021-07-29T09:29:16+10:00https://m2lines.github.io/news/zampierijames/2021-07-29T09:29:16+10:00https://m2lines.github.io/news/balwada-article/2021-07-28T09:29:16+10:00https://m2lines.github.io/news/abernatheypaper/2021-07-27T09:29:16+10:00https://m2lines.github.io/news/bushukarticle/2021-07-27T09:29:16+10:00https://m2lines.github.io/news/ziweiliarticle/2021-07-26T09:29:16+10:00https://m2lines.github.io/news/articleyuval/2021-07-19T09:29:16+10:00https://m2lines.github.io/news/articlejamescall/2021-07-14T09:29:16+10:00https://m2lines.github.io/news/oneworld/2021-07-02T09:29:16+10:00https://m2lines.github.io/news/willgregorypaper/2021-07-01T09:29:16+10:00https://m2lines.github.io/news/s2schallenge/2021-06-29T10:29:16+10:00https://m2lines.github.io/news/kitpconference/2021-06-07T10:29:16+10:00https://m2lines.github.io/news/cesmworkshop/2021-06-07T09:29:16+10:00https://m2lines.github.io/news/5jamescall/2021-06-01T09:29:16+10:00https://m2lines.github.io/news/3gentinepaper1/2021-04-23T09:29:16+10:00https://m2lines.github.io/news/4gentinepaper2/2021-03-21T09:29:16+10:00https://m2lines.github.io/news/1guillauminpaper/2021-03-08T09:29:16+10:00https://m2lines.github.io/news/2jlesommerpaper/2021-02-22T09:29:16+10:00https://m2lines.github.io/about/2018-12-06T09:29:16+10:00https://m2lines.github.io/pages/code-of-conduct/2018-12-06T09:29:16+10:00https://m2lines.github.io/pages/2018-12-06T09:29:16+10:00https://m2lines.github.io/services/axe1/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/axe2/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/axe3/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/2018-11-28T15:15:34+10:00https://m2lines.github.io/tags/climate-model-development/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/machine-learning/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/ocean/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/2018-11-19T10:47:58+10:00https://m2lines.github.io/2018-02-12T15:37:57+07:00https://m2lines.github.io/404/2018-02-10T10:52:03+07:00https://m2lines.github.io/research/https://m2lines.github.io/team/former/https://m2lines.github.io/blog/https://m2lines.github.io/code/https://m2lines.github.io/research/talks/archive/https://m2lines.github.io/tags/atmosphere/https://m2lines.github.io/code/corrected_era5/https://m2lines.github.io/categories/https://m2lines.github.io/code/beucler0224/https://m2lines.github.io/code/chapman1223/https://m2lines.github.io/code/cm26/https://m2lines.github.io/tags/coupled-physics/https://m2lines.github.io/tags/data-assimilation/https://m2lines.github.io/code/falasca0424/https://m2lines.github.io/code/icemodelerrors/https://m2lines.github.io/code/diffqg_jax/https://m2lines.github.io/code/diffqg_pytorch/https://m2lines.github.io/code/equationdiscovery/https://m2lines.github.io/code/gcm/https://m2lines.github.io/code/datadrivenparam/https://m2lines.github.io/code/geotrainflow/https://m2lines.github.io/code/gpsat/https://m2lines.github.io/code/mom6/https://m2lines.github.io/code/parampyqg/https://m2lines.github.io/code/parammom6/https://m2lines.github.io/jobs/https://m2lines.github.io/code/gregory0124/https://m2lines.github.io/code/lorenz96/https://m2lines.github.io/code/zampieri0424/https://m2lines.github.io/code/verticalmix/https://m2lines.github.io/code/subgridatmosphere/https://m2lines.github.io/code/transportatmosphere/https://m2lines.github.io/tags/newsletters/https://m2lines.github.io/news/newsletters/https://m2lines.github.io/publications/https://m2lines.github.io/publications/archive/https://m2lines.github.io/code/pyqg/https://m2lines.github.io/code/randomforest/https://m2lines.github.io/resources/https://m2lines.github.io/tags/sea-ice/https://m2lines.github.io/code/shallowwater/https://m2lines.github.io/code/momentumforcing/https://m2lines.github.io/tags/talks/https://m2lines.github.io/research/talks/https://m2lines.github.io/code/xgcm/ \ No newline at end of file +https://m2lines.github.io/team/laure-zanna/2018-11-19T10:47:58+10:00https://m2lines.github.io/blog/atmosphere/https://m2lines.github.io/blog/climate/https://m2lines.github.io/blog/coupledphysics/https://m2lines.github.io/blog/dataassimilation/https://m2lines.github.io/blog/climateprocess/https://m2lines.github.io/blog/machinelearning/https://m2lines.github.io/blog/ocean/https://m2lines.github.io/blog/projectgoals/https://m2lines.github.io/blog/seaice/https://m2lines.github.io/team/johannagoldman/2018-11-19T10:47:58+10:00https://m2lines.github.io/blog/research2/https://m2lines.github.io/blog/research4/https://m2lines.github.io/team/former/ziweili/https://m2lines.github.io/team/alistairadcroft/https://m2lines.github.io/team/carlosferndandezgranda/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/joan-bruna/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/ryanabernathy/https://m2lines.github.io/team/brandonreichl/https://m2lines.github.io/team/feiyulu/https://m2lines.github.io/team/judithberner/https://m2lines.github.io/team/marikaholland/https://m2lines.github.io/team/mitchbushuk/https://m2lines.github.io/team/pierregentine/https://m2lines.github.io/team/paulogorman/https://m2lines.github.io/team/juliedeshayes/https://m2lines.github.io/team/julienlesommer/https://m2lines.github.io/team/arthurguillaumin/https://m2lines.github.io/team/former/aurorabasinki/https://m2lines.github.io/team/janniyuval/https://m2lines.github.io/team/alexconnolly/https://m2lines.github.io/team/juliusbusecke/https://m2lines.github.io/team/aakashsane/https://m2lines.github.io/team/former/andrewross/https://m2lines.github.io/team/lorenzozampieri/https://m2lines.github.io/team/cemgultekin/https://m2lines.github.io/team/tarunverma/https://m2lines.github.io/team/dhruvbalwada/https://m2lines.github.io/team/chengzhang/https://m2lines.github.io/team/former/anastasiiagorbunova/https://m2lines.github.io/team/pavelperezhogin/https://m2lines.github.io/team/willchapman/https://m2lines.github.io/team/willgregory/https://m2lines.github.io/team/abigailbodner/https://m2lines.github.io/team/andrewbrettin/https://m2lines.github.io/team/former/marioherrerogonzalez/https://m2lines.github.io/team/karlotness/https://m2lines.github.io/team/former/azizbhouri/https://m2lines.github.io/team/former/hugofrezat/https://m2lines.github.io/team/former/adrienburq/https://m2lines.github.io/team/fabriziofalasca/https://m2lines.github.io/team/adamsubel/https://m2lines.github.io/team/alexisbarge/https://m2lines.github.io/team/maudtissot/https://m2lines.github.io/team/former/elizabethyankovsky/https://m2lines.github.io/team/sarashamekh/https://m2lines.github.io/team/former/chrisdupuis/https://m2lines.github.io/team/former/jemanjohnson/https://m2lines.github.io/team/chrispedersen/https://m2lines.github.io/team/former/emilynewsom/https://m2lines.github.io/team/etiennemeunier/https://m2lines.github.io/team/former/mariapratcolomer/https://m2lines.github.io/team/noraloose/https://m2lines.github.io/team/dannidu/https://m2lines.github.io/team/davidkamm/https://m2lines.github.io/team/gabrielmouttapa/https://m2lines.github.io/team/gretamiller/https://m2lines.github.io/team/jia-ruishi/https://m2lines.github.io/team/marcelacontreras/https://m2lines.github.io/team/shuchangliu/https://m2lines.github.io/team/former/patricialuc/https://m2lines.github.io/team/kelseyeverard/https://m2lines.github.io/team/former/matiasortiz/https://m2lines.github.io/team/former/shubham/https://m2lines.github.io/team/suryadheeshjith/https://m2lines.github.io/team/former/leoliu/https://m2lines.github.io/team/former/shantanu/https://m2lines.github.io/team/griffinmooers/https://m2lines.github.io/team/former/friedrichginnold/https://m2lines.github.io/team/praninalluri/https://m2lines.github.io/team/renaudfalga/https://m2lines.github.io/team/former/jisuhan/https://m2lines.github.io/team/juliasimpson/https://m2lines.github.io/team/jiarongwu/https://m2lines.github.io/team/yongquanqu-/https://m2lines.github.io/team/antoinenasser/https://m2lines.github.io/team/xavierlevine/https://m2lines.github.io/news/2024-12-01T09:29:16+10:00https://m2lines.github.io/news/2412agu/2024-12-01T09:29:16+10:00https://m2lines.github.io/news/2411zampieri/2024-11-01T09:29:16+10:00https://m2lines.github.io/news/2411shi/2024-11-01T09:29:16+10:00https://m2lines.github.io/news/2410yuval/2024-10-02T09:29:16+10:00https://m2lines.github.io/news/2410shamekh/2024-10-01T09:29:16+10:00https://m2lines.github.io/news/2409gentine/2024-09-02T09:29:16+10:00https://m2lines.github.io/news/2409gregory/2024-09-01T09:29:16+10:00https://m2lines.github.io/news/2408bonan/2024-08-03T09:29:16+10:00https://m2lines.github.io/news/2408gorman/2024-08-02T09:29:16+10:00https://m2lines.github.io/news/2407gentine/2024-07-02T09:29:16+10:00https://m2lines.github.io/news/2407reichl/2024-07-01T09:29:16+10:00https://m2lines.github.io/news/2406dheeshjith/2024-06-02T09:29:16+10:00https://m2lines.github.io/news/2406falasca/2024-06-01T09:29:16+10:00https://m2lines.github.io/news/2405chapman/2024-05-02T09:29:16+10:00https://m2lines.github.io/news/2405bushuk/2024-05-01T09:29:16+10:00https://m2lines.github.io/news/2404zampieri/2024-04-03T09:29:16+10:00https://m2lines.github.io/news/2404qu/2024-04-02T09:29:16+10:00https://m2lines.github.io/news/2404adam/2024-04-01T09:29:16+10:00https://m2lines.github.io/news/2402bhouri/2024-02-03T09:29:16+10:00https://m2lines.github.io/news/2402joan/2024-02-02T09:29:16+10:00https://m2lines.github.io/news/2402nora/2024-02-01T09:29:16+10:00https://m2lines.github.io/news/2401pavel/2024-01-02T09:29:16+10:00https://m2lines.github.io/news/2401l96/2024-01-01T09:29:16+10:00https://m2lines.github.io/news/2312climsim/2023-12-12T09:29:16+10:00https://m2lines.github.io/news/2312willgfeature/2023-12-01T09:29:16+10:00https://m2lines.github.io/news/2311johnson/2023-11-01T09:29:16+10:00https://m2lines.github.io/news/2310lu/2023-10-03T09:29:16+10:00https://m2lines.github.io/news/2310bhouri/2023-10-02T09:29:16+10:00https://m2lines.github.io/news/2310chapman/2023-10-01T09:29:16+10:00https://m2lines.github.io/news/2309blogpost/2023-09-11T09:29:16+10:00https://m2lines.github.io/news/2309holland/2023-09-04T09:29:16+10:00https://m2lines.github.io/news/2309mooers/2023-09-03T09:29:16+10:00https://m2lines.github.io/news/2309pederson/2023-09-02T09:29:16+10:00https://m2lines.github.io/news/2308newsom/2023-08-03T09:29:16+10:00https://m2lines.github.io/news/2308bhouri/2023-08-02T09:29:16+10:00https://m2lines.github.io/news/2308shamekh/2023-08-01T09:29:16+10:00https://m2lines.github.io/news/2307sane/2023-07-01T09:29:16+10:00https://m2lines.github.io/news/2306zampieri/2023-06-01T09:29:16+10:00https://m2lines.github.io/news/2305otness/2023-05-01T09:29:16+10:00https://m2lines.github.io/news/2301gentine/2023-01-03T09:29:16+10:00https://m2lines.github.io/news/2212agu22/2022-12-06T09:29:16+10:00https://m2lines.github.io/news/2211balwada/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211shamekh/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211adcroft/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2211bruna/2022-11-03T09:29:16+10:00https://m2lines.github.io/news/2210bushuk/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2210gentine/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2210deshayes/2022-10-03T09:29:16+10:00https://m2lines.github.io/news/2208ross/2022-08-02T09:29:16+10:00https://m2lines.github.io/news/2208uchida/2022-08-01T09:29:16+10:00https://m2lines.github.io/news/2208behrens/2022-08-01T09:29:16+10:00https://m2lines.github.io/news/2207reichl/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/2207subel/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/2207berner/2022-07-05T09:29:16+10:00https://m2lines.github.io/news/gentine-abernathey/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/zampieri2022/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/gregory2022/2022-06-07T09:29:16+10:00https://m2lines.github.io/news/cesmworkshop2022/2022-05-02T09:29:16+10:00https://m2lines.github.io/news/gorbunova2021/2022-05-01T09:29:16+10:00https://m2lines.github.io/news/gcmfilter/2022-05-01T09:29:16+10:00https://m2lines.github.io/news/holland2022/2022-05-01T08:29:16+10:00https://m2lines.github.io/news/balaji2022/2022-05-01T07:29:16+10:00https://m2lines.github.io/news/220401subel/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/220401bruna/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/220401bushuk/2022-03-31T09:29:16+10:00https://m2lines.github.io/news/balwadazanna22/2022-03-01T09:29:16+10:00https://m2lines.github.io/news/m2linesmovie/2022-02-02T09:29:16+10:00https://m2lines.github.io/news/sanepaper2022/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/abernatheychapter/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/beuclerpreprint/2022-02-01T09:29:16+10:00https://m2lines.github.io/news/fernandezgranda21/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/frezatpreprint/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/ziweili21/2022-01-01T09:29:16+10:00https://m2lines.github.io/news/agupresentation/2021-12-08T09:29:16+10:00https://m2lines.github.io/news/kickoffoct/2021-10-29T09:29:16+10:00https://m2lines.github.io/news/hollandetal/2021-10-28T09:29:16+10:00https://m2lines.github.io/news/chapmanetal/2021-10-14T09:29:16+10:00https://m2lines.github.io/news/zampierietal21/2021-10-01T09:29:16+10:00https://m2lines.github.io/news/aiforgood/2021-08-25T09:29:16+10:00https://m2lines.github.io/news/fernandezgranda/2021-07-29T09:29:16+10:00https://m2lines.github.io/news/zampierijames/2021-07-29T09:29:16+10:00https://m2lines.github.io/news/balwada-article/2021-07-28T09:29:16+10:00https://m2lines.github.io/news/abernatheypaper/2021-07-27T09:29:16+10:00https://m2lines.github.io/news/bushukarticle/2021-07-27T09:29:16+10:00https://m2lines.github.io/news/ziweiliarticle/2021-07-26T09:29:16+10:00https://m2lines.github.io/news/articleyuval/2021-07-19T09:29:16+10:00https://m2lines.github.io/news/articlejamescall/2021-07-14T09:29:16+10:00https://m2lines.github.io/news/oneworld/2021-07-02T09:29:16+10:00https://m2lines.github.io/news/willgregorypaper/2021-07-01T09:29:16+10:00https://m2lines.github.io/news/s2schallenge/2021-06-29T10:29:16+10:00https://m2lines.github.io/news/kitpconference/2021-06-07T10:29:16+10:00https://m2lines.github.io/news/cesmworkshop/2021-06-07T09:29:16+10:00https://m2lines.github.io/news/5jamescall/2021-06-01T09:29:16+10:00https://m2lines.github.io/news/3gentinepaper1/2021-04-23T09:29:16+10:00https://m2lines.github.io/news/4gentinepaper2/2021-03-21T09:29:16+10:00https://m2lines.github.io/news/1guillauminpaper/2021-03-08T09:29:16+10:00https://m2lines.github.io/news/2jlesommerpaper/2021-02-22T09:29:16+10:00https://m2lines.github.io/about/2018-12-06T09:29:16+10:00https://m2lines.github.io/pages/code-of-conduct/2018-12-06T09:29:16+10:00https://m2lines.github.io/pages/2018-12-06T09:29:16+10:00https://m2lines.github.io/services/axe1/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/axe2/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/axe3/2018-11-28T15:15:34+10:00https://m2lines.github.io/services/2018-11-28T15:15:34+10:00https://m2lines.github.io/tags/climate-model-development/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/machine-learning/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/ocean/2018-11-19T10:47:58+10:00https://m2lines.github.io/tags/2018-11-19T10:47:58+10:00https://m2lines.github.io/team/2018-11-19T10:47:58+10:00https://m2lines.github.io/2018-02-12T15:37:57+07:00https://m2lines.github.io/404/2018-02-10T10:52:03+07:00https://m2lines.github.io/research/https://m2lines.github.io/team/former/https://m2lines.github.io/blog/https://m2lines.github.io/code/https://m2lines.github.io/research/talks/archive/https://m2lines.github.io/tags/atmosphere/https://m2lines.github.io/code/corrected_era5/https://m2lines.github.io/categories/https://m2lines.github.io/code/beucler0224/https://m2lines.github.io/code/chapman1223/https://m2lines.github.io/code/cm26/https://m2lines.github.io/tags/coupled-physics/https://m2lines.github.io/tags/data-assimilation/https://m2lines.github.io/code/falasca0424/https://m2lines.github.io/code/icemodelerrors/https://m2lines.github.io/code/diffqg_jax/https://m2lines.github.io/code/diffqg_pytorch/https://m2lines.github.io/code/equationdiscovery/https://m2lines.github.io/code/gcm/https://m2lines.github.io/code/datadrivenparam/https://m2lines.github.io/code/geotrainflow/https://m2lines.github.io/code/gpsat/https://m2lines.github.io/code/mom6/https://m2lines.github.io/code/parampyqg/https://m2lines.github.io/code/parammom6/https://m2lines.github.io/jobs/https://m2lines.github.io/code/gregory0124/https://m2lines.github.io/code/lorenz96/https://m2lines.github.io/code/zampieri0424/https://m2lines.github.io/code/verticalmix/https://m2lines.github.io/code/subgridatmosphere/https://m2lines.github.io/code/transportatmosphere/https://m2lines.github.io/tags/newsletters/https://m2lines.github.io/news/newsletters/https://m2lines.github.io/publications/https://m2lines.github.io/publications/archive/https://m2lines.github.io/code/pyqg/https://m2lines.github.io/code/randomforest/https://m2lines.github.io/resources/https://m2lines.github.io/tags/sea-ice/https://m2lines.github.io/code/shallowwater/https://m2lines.github.io/code/momentumforcing/https://m2lines.github.io/tags/talks/https://m2lines.github.io/research/talks/https://m2lines.github.io/code/xgcm/ \ No newline at end of file diff --git a/tags/atmosphere/index.html b/tags/atmosphere/index.html index c5013ed4..41032b35 100644 --- a/tags/atmosphere/index.html +++ b/tags/atmosphere/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Atmosphere

    NYU, Courant Institute

    National Center for Atmospheric Research

    Columbia University

    MIT

    Google Research

    Columbia University

    National Center for Atmospheric Research

    Columbia University

    Columbia University

    NYU, Courant Institute

    Lamont/Columbia University

    University of Oxford/NCAR

    MIT EAPS

    MIT

    ETH Zürich

    NYU, Courant Institute

    Columbia University

    Columbia University

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Atmosphere

    NCAR

    Columbia University

    MIT

    Google Research

    Columbia University

    NCAR

    NYU

    University of Oxford/NCAR

    MIT

    MIT

    NYU

    Columbia University

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/atmosphere/index.xml b/tags/atmosphere/index.xml index 6ce2694b..4a10063d 100644 --- a/tags/atmosphere/index.xml +++ b/tags/atmosphere/index.xml @@ -1 +1 @@ -Atmosphere on M²LInEShttps://m2lines.github.io/tags/atmosphere/Recent content in Atmosphere on M²LInESHugo -- gohugo.ioen-usZiwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/NYU, Courant InstituteJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/National Center for Atmospheric ResearchPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Columbia UniversityAdrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/Columbia UniversitySara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYU, Courant InstituteChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MIT EAPSGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/ETH ZürichRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant InstituteJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file +Atmosphere on M²LInEShttps://m2lines.github.io/tags/atmosphere/Recent content in Atmosphere on M²LInESHugo -- gohugo.ioen-usJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/NCARPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/NCARSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYUGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MITGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYUJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file diff --git a/tags/climate-model-development/index.html b/tags/climate-model-development/index.html index fa6d0a53..3e43a042 100644 --- a/tags/climate-model-development/index.html +++ b/tags/climate-model-development/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Climate Model Development

    NYU, Courant Institute + Center for Data Science

    Princeton University, Affiliate NOAA-GFDL in Ocean division

    NOAA-GFDL in Ocean division

    Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal division

    National Center for Atmospheric Research

    CNRS-IPSL

    L’Institut des Géosciences de l’Environnement (IGE)

    Google Research

    Princeton University, Affiliate NOAA-GFDL in Ocean Division

    NYU, Courant Institute

    NYU, Courant Institute

    CNRS-IGE

    CNRS-IPSL

    Princeton University

    IGE, Grenoble

    NYU, Courant Institute

    Columbia University

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Climate Model Development

    NYU

    Princeton University, Affiliate NOAA-GFDL

    NOAA-GFDL

    Princeton University, Affiliate NOAA-GFDL

    NCAR

    CNRS-IPSL

    CNRS-IGE

    Google Research

    Princeton University, Affiliate NOAA-GFDL

    NYU

    NYU

    CNRS-IGE

    CNRS-IPSL

    Princeton University

    CNRS-IGE

    Columbia University

    NYU

    Princeton University

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/climate-model-development/index.xml b/tags/climate-model-development/index.xml index 61d9a873..e05e148e 100644 --- a/tags/climate-model-development/index.xml +++ b/tags/climate-model-development/index.xml @@ -1 +1 @@ -Climate Model Development on M²LInEShttps://m2lines.github.io/tags/climate-model-development/Recent content in Climate Model Development on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYU, Courant Institute + Center for Data ScienceAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDL in Ocean divisionBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDL in Ocean divisionFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/National Center for Atmospheric ResearchJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/L’Institut des Géosciences de l’Environnement (IGE)Janni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYU, Courant InstituteAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/IGE, GrenobleMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstitutePrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant Institute \ No newline at end of file +Climate Model Development on M²LInEShttps://m2lines.github.io/tags/climate-model-development/Recent content in Climate Model Development on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYUAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDLBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDLFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDLMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/NCARJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/CNRS-IGEJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDLPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYUAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYUAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/CNRS-IGEPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYUAntoine-Alexis Nasserhttps://m2lines.github.io/team/antoinenasser/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/antoinenasser/Princeton UniversityXavier Levinehttps://m2lines.github.io/team/xavierlevine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/xavierlevine/Columbia University \ No newline at end of file diff --git a/tags/coupled-physics/index.html b/tags/coupled-physics/index.html index 6a39efe9..71f7488a 100644 --- a/tags/coupled-physics/index.html +++ b/tags/coupled-physics/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Coupled Physics

    CNRS-IPSL

    NYU Courant Institute

    CNRS-IGE

    NYU, Courant Institute

    NYU Courant

    IGE, Grenoble

    NYU, Courant Institute

    NYU

    • This project is supported by Schmidt Sciences, LLC.

    Coupled Physics

    CNRS-IPSL

    CNRS-IGE

    NYU

    CNRS-IGE

    NYU

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/coupled-physics/index.xml b/tags/coupled-physics/index.xml index abe91b07..b95d887a 100644 --- a/tags/coupled-physics/index.xml +++ b/tags/coupled-physics/index.xml @@ -1 +1 @@ -Coupled Physics on M²LInEShttps://m2lines.github.io/tags/coupled-physics/Recent content in Coupled Physics on M²LInESHugo -- gohugo.ioen-usJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLAurora Basinskihttps://m2lines.github.io/team/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aurorabasinki/NYU Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEEmily Newsomhttps://m2lines.github.io/team/former/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/emilynewsom/NYU, Courant InstituteJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYU CourantMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/IGE, GrenobleMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstituteJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYU \ No newline at end of file +Coupled Physics on M²LInEShttps://m2lines.github.io/tags/coupled-physics/Recent content in Coupled Physics on M²LInESHugo -- gohugo.ioen-usJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYUMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/CNRS-IGEJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUXavier Levinehttps://m2lines.github.io/team/xavierlevine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/xavierlevine/Columbia University \ No newline at end of file diff --git a/tags/data-assimilation/index.html b/tags/data-assimilation/index.html index 3581e478..ec29aa65 100644 --- a/tags/data-assimilation/index.html +++ b/tags/data-assimilation/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Data Assimilation

    Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal division

    National Center for Atmospheric Research

    UCAR, Affiliate NOAA-GFDL in Ocean division

    Princeton University

    National Center for Atmospheric Research

    Princeton University, Affiliate NOAA-GFDL

    Columbia University

    L’Institut des Géosciences de l’Environnement (IGE)

    Princeton University

    IGE

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Data Assimilation

    Princeton University, Affiliate NOAA-GFDL

    NCAR

    UCAR, Affiliate NOAA-GFDL

    Princeton University

    NCAR

    Princeton University, Affiliate NOAA-GFDL

    Princeton University

    CNRS-IGE

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/data-assimilation/index.xml b/tags/data-assimilation/index.xml index 8ec0cef4..4a288005 100644 --- a/tags/data-assimilation/index.xml +++ b/tags/data-assimilation/index.xml @@ -1 +1 @@ -Data Assimilation on M²LInEShttps://m2lines.github.io/tags/data-assimilation/Recent content in Data Assimilation on M²LInESHugo -- gohugo.ioen-usFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/National Center for Atmospheric ResearchMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDL in Ocean divisionCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLMario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Danni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/IGEYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file +Data Assimilation on M²LInEShttps://m2lines.github.io/tags/data-assimilation/Recent content in Data Assimilation on M²LInESHugo -- gohugo.ioen-usFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDLJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/NCARMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDLCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/NCARWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/CNRS-IGEYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file diff --git a/tags/machine-learning/index.html b/tags/machine-learning/index.html index cf1106b8..752cb16f 100644 --- a/tags/machine-learning/index.html +++ b/tags/machine-learning/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Machine Learning

    NYU, Courant Institute + Center for Data Science

    NYU, Courant Institute

    Princeton University, Affiliate NOAA-GFDL in Ocean division

    NYU, Courant Institute + Center for Data Science

    NYU, Courant Institute + Computer Science + Center for Data Science

    Columbia University

    MIT

    Queen Mary University of London

    Google Research

    Columbia University

    Princeton University, Affiliate NOAA-GFDL in Ocean Division

    NYU, Courant Institute

    NYU, Courant Institute

    Princeton University

    Lamont/Columbia University

    Princeton University

    National Center for Atmospheric Research

    Princeton University, Affiliate NOAA-GFDL

    NYU, Courant Institute

    NYU, Courant Institute

    NYU, Courant Institute

    Columbia University

    L’Institut des Géosciences de l’Environnement (IGE)

    Columbia University

    NYU, Courant Institute

    CNRS-IGE

    CNRS-IPSL

    NYU, Courant Institute

    L’Institut des Géosciences de l’Environnement (IGE)

    NYU, Courant Institute

    CNRS-IPSL

    NYU, Courant Institute

    Princeton University

    Princeton University

    University of Oxford/NCAR

    MIT EAPS

    Columbia University

    NYU, Courant Institute

    NYU, Tandon School of Engineering

    NYU, Courant Institute

    MIT

    ETH Zürich

    Columbia University

    NYU, Courant Institute

    Columbia University

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Machine Learning

    NYU

    Princeton University, Affiliate NOAA-GFDL

    NYU

    NYU

    Columbia University

    MIT

    Queen Mary University of London

    Google Research

    Columbia University

    Princeton University, Affiliate NOAA-GFDL

    NYU

    John Hopkins University

    LDEO,Columbia University

    Princeton University

    NCAR

    Princeton University, Affiliate NOAA-GFDL

    NYU

    NYU

    NYU

    NYU

    CNRS-IGE

    CNRS-IPSL

    NYU

    NYU

    CNRS-IPSL

    [C]Worthy

    Princeton University

    University of Oxford/NCAR

    MIT

    NYU

    NYU

    MIT

    Columbia University

    NYU

    Columbia University

    Columbia University

    Princeton University

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/machine-learning/index.xml b/tags/machine-learning/index.xml index 3bcc58d6..e0e6a977 100644 --- a/tags/machine-learning/index.xml +++ b/tags/machine-learning/index.xml @@ -1 +1 @@ -Machine Learning on M²LInEShttps://m2lines.github.io/tags/machine-learning/Recent content in Machine Learning on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYU, Courant Institute + Center for Data ScienceZiwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/NYU, Courant InstituteAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDL in Ocean divisionCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYU, Courant Institute + Center for Data ScienceJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYU, Courant Institute + Computer Science + Center for Data SciencePierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITArthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/NYU, Courant InstituteCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYU, Courant InstituteTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/Princeton UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/Lamont/Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYU, Courant InstituteKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYU, Courant InstituteMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Columbia UniversityHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/L’Institut des Géosciences de l’Environnement (IGE)Adrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/Columbia UniversityAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYU, Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYU, Courant InstituteJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Chris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYU, Courant InstituteEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/NYU, Courant InstituteNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/Princeton UniversityDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MIT EAPSPatricia Luchttps://m2lines.github.io/team/former/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/patricialuc/Columbia UniversityKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYU, Courant InstituteShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/NYU, Tandon School of EngineeringSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYU, Courant InstituteGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/ETH ZürichPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant InstituteJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file +Machine Learning on M²LInEShttps://m2lines.github.io/tags/machine-learning/Recent content in Machine Learning on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYUAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDLCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYUJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYUPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITArthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDLCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYUTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/John Hopkins UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/LDEO,Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/NCARWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYUAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYUKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYUAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYUAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYUChris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYUEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/[C]WorthyDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MITKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYUSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYUGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYUJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia UniversityAntoine-Alexis Nasserhttps://m2lines.github.io/team/antoinenasser/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/antoinenasser/Princeton UniversityXavier Levinehttps://m2lines.github.io/team/xavierlevine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/xavierlevine/Columbia University \ No newline at end of file diff --git a/tags/newsletters/index.html b/tags/newsletters/index.html index a0b56c1d..6dfe8ab3 100644 --- a/tags/newsletters/index.html +++ b/tags/newsletters/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Newsletters

    Links to our past newsletters are below.

    2024

    2023

    2022

    2021

    • This project is supported by Schmidt Sciences, LLC.

    Newsletters

    Links to our past newsletters are below.

    2024

    2023

    2022

    2021

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/newsletters/index.xml b/tags/newsletters/index.xml index cd087d06..5a9abc8d 100644 --- a/tags/newsletters/index.xml +++ b/tags/newsletters/index.xml @@ -1,2 +1,2 @@ Newsletters on M²LInEShttps://m2lines.github.io/tags/newsletters/Recent content in Newsletters on M²LInESHugo -- gohugo.ioen-usNewslettershttps://m2lines.github.io/news/newsletters/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/news/newsletters/Links to our past newsletters are below. -2024 11/01/2024 - M²LInES newsletter - November 2024 10/01/2024 - M²LInES newsletter - October 2024 09/03/2024 - M²LInES newsletter - September 2024 08/05/2024 - M²LInES newsletter - August 2024 07/22/2024 - M²LInES newsletter - July 2024 06/14/2024 - M²LInES newsletter - June 2024 05/01/2024 - M²LInES newsletter - May 2024 04/01/2024 - M²LInES newsletter - April 2024 02/16/2024 - M²LInES newsletter - Special Ocean Sciences Meeting 02/01/2024 - M²LInES newsletter - Feburary 2024 01/08/2024 - M²LInES newsletter - January 2024 2023 12/01/2023 - M²LInES newsletter - December 2023 - Special AGU 11/02/2023 - M²LInES newsletter - November 2023 10/04/2023 - M²LInES newsletter - October 2023 09/06/2023 - M²LInES newsletter - September 2023 08/01/2023 - M²LInES newsletter - August 2023 07/03/2023 - M²LInES newsletter - July 2023 06/13/2023 - M²LInES newsletter - June 2023 05/11/2023 - M²LInES newsletter - May 2023 01/03/2023 - M²LInES newsletter - January 2023 2022 12/05/2022 - M²LInES newsletter - December 2022 AGU Special 11/01/2022 - M²LInES newsletter - November 2022 10/03/2022 - M²LInES newsletter - October 2022 09/16/2022 - M²LInES newsletter - September 2022 Research Roundup 08/01/2022 - M²LInES newsletter - August 2022 07/05/2022 - M²LInES newsletter - July 2022 06/06/2022 - M²LInES newsletter - June 2022 05/02/2022 - M²LInES newsletter - May 2022 04/01/2022 - M²LInES newsletter - April 2022 02/24/2022 - M²LInES newsletter - OSM22 special 02/01/2022 - M²LInES newsletter - February 2022 01/01/2022 - M²LInES newsletter - January 2022 2021 12/01/2021 - M²LInES newsletter - December 2021 AGU special 11/01/2021 - M²LInES newsletter - November 2021 10/01/2021 - M²LInES newsletter - October 2021 09/01/2021 - M²LInES newsletter - September 2021 08/02/2021 - M²LInES newsletter - August 2021 06/29/2021 - M²LInES newsletter - June/July 2021 \ No newline at end of file +2024 12/02/2024 - M²LInES newsletter - December 2024 11/01/2024 - M²LInES newsletter - November 2024 10/01/2024 - M²LInES newsletter - October 2024 09/03/2024 - M²LInES newsletter - September 2024 08/05/2024 - M²LInES newsletter - August 2024 07/22/2024 - M²LInES newsletter - July 2024 06/14/2024 - M²LInES newsletter - June 2024 05/01/2024 - M²LInES newsletter - May 2024 04/01/2024 - M²LInES newsletter - April 2024 02/16/2024 - M²LInES newsletter - Special Ocean Sciences Meeting 02/01/2024 - M²LInES newsletter - Feburary 2024 01/08/2024 - M²LInES newsletter - January 2024 2023 12/01/2023 - M²LInES newsletter - December 2023 - Special AGU 11/02/2023 - M²LInES newsletter - November 2023 10/04/2023 - M²LInES newsletter - October 2023 09/06/2023 - M²LInES newsletter - September 2023 08/01/2023 - M²LInES newsletter - August 2023 07/03/2023 - M²LInES newsletter - July 2023 06/13/2023 - M²LInES newsletter - June 2023 05/11/2023 - M²LInES newsletter - May 2023 01/03/2023 - M²LInES newsletter - January 2023 2022 12/05/2022 - M²LInES newsletter - December 2022 AGU Special 11/01/2022 - M²LInES newsletter - November 2022 10/03/2022 - M²LInES newsletter - October 2022 09/16/2022 - M²LInES newsletter - September 2022 Research Roundup 08/01/2022 - M²LInES newsletter - August 2022 07/05/2022 - M²LInES newsletter - July 2022 06/06/2022 - M²LInES newsletter - June 2022 05/02/2022 - M²LInES newsletter - May 2022 04/01/2022 - M²LInES newsletter - April 2022 02/24/2022 - M²LInES newsletter - OSM22 special 02/01/2022 - M²LInES newsletter - February 2022 01/01/2022 - M²LInES newsletter - January 2022 2021 12/01/2021 - M²LInES newsletter - December 2021 AGU special 11/01/2021 - M²LInES newsletter - November 2021 10/01/2021 - M²LInES newsletter - October 2021 09/01/2021 - M²LInES newsletter - September 2021 08/02/2021 - M²LInES newsletter - August 2021 06/29/2021 - M²LInES newsletter - June/July 2021 \ No newline at end of file diff --git a/tags/ocean/index.html b/tags/ocean/index.html index b69df10f..5f6e3ac9 100644 --- a/tags/ocean/index.html +++ b/tags/ocean/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Ocean

    NYU, Courant Institute + Center for Data Science

    NYU, Courant Institute

    NYU, Courant Institute

    Princeton University, Affiliate NOAA-GFDL in Ocean division

    NYU, Courant Institute + Center for Data Science

    Lamont/Columbia University

    NOAA-GFDL in Ocean division

    Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal division

    CNRS-IPSL

    L’Institut des Géosciences de l’Environnement (IGE)

    NYU Courant Institute

    Columbia University

    Princeton University, Affiliate NOAA-GFDL in Ocean Division

    Princeton University

    Lamont/Columbia University

    Princeton University

    L’Institut des Géosciences de l’Environnement (IGE)

    NYU, Courant Institute

    NYU, Courant Institute

    NYU, Courant Institute

    L’Institut des Géosciences de l’Environnement (IGE)

    NYU, Courant Institute

    NYU, Courant Institute

    CNRS-IPSL

    NYU, Courant Institute

    L’Institut des Géosciences de l’Environnement (IGE)

    NYU, Courant Institute

    CNRS-IPSL

    Princeton University

    CNRS-IPSL

    IGE

    NYU Courant

    IGE, Grenoble

    NYU, Courant Institute

    NYU, Courant Institute

    NYU, Tandon School of Engineering

    Columbia University

    Columbia University

    Columbia University

    NYU

    • This project is supported by Schmidt Sciences, LLC.

    Ocean

    NYU

    NYU

    Princeton University, Affiliate NOAA-GFDL

    NYU

    Earthmovers

    NOAA-GFDL

    Princeton University, Affiliate NOAA-GFDL

    CNRS-IPSL

    CNRS-IGE

    Columbia University

    Princeton University, Affiliate NOAA-GFDL

    John Hopkins University

    LDEO,Columbia University

    Princeton University

    NYU

    NYU

    NYU

    NYU

    NYU

    CNRS-IPSL

    NYU

    CNRS-IPSL

    [C]Worthy

    CNRS-IPSL

    CNRS-IGE

    NYU

    CNRS-IGE

    NYU

    Columbia University

    Columbia University

    NYU

    Princeton University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/ocean/index.xml b/tags/ocean/index.xml index 1f8d78e4..a0adac29 100644 --- a/tags/ocean/index.xml +++ b/tags/ocean/index.xml @@ -1 +1 @@ -Ocean on M²LInEShttps://m2lines.github.io/tags/ocean/Recent content in Ocean on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYU, Courant Institute + Center for Data ScienceJohanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYU, Courant InstituteZiwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/NYU, Courant InstituteAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDL in Ocean divisionCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYU, Courant Institute + Center for Data ScienceRyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/Lamont/Columbia UniversityBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDL in Ocean divisionFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/L’Institut des Géosciences de l’Environnement (IGE)Aurora Basinskihttps://m2lines.github.io/team/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aurorabasinki/NYU Courant InstituteJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/Princeton UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/Lamont/Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/L’Institut des Géosciences de l’Environnement (IGE)Pavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYU, Courant InstituteAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYU, Courant InstituteHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/L’Institut des Géosciences de l’Environnement (IGE)Fabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYU, Courant InstituteAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYU, Courant InstituteMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/NYU, Courant InstituteJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Chris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYU, Courant InstituteEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/IGEJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYU CourantMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/IGE, GrenobleKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYU, Courant InstituteMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstituteShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/NYU, Tandon School of EngineeringPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYU \ No newline at end of file +Ocean on M²LInEShttps://m2lines.github.io/tags/ocean/Recent content in Ocean on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYUJohanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYUAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDLCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYURyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/EarthmoversBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDLFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDLJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/CNRS-IGEJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDLTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/John Hopkins UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/LDEO,Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYUAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYUAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYUFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYUAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYUMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLChris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYUEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/[C]WorthyDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/CNRS-IGEJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYUMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/CNRS-IGEKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYUPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUAntoine-Alexis Nasserhttps://m2lines.github.io/team/antoinenasser/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/antoinenasser/Princeton University \ No newline at end of file diff --git a/tags/sea-ice/index.html b/tags/sea-ice/index.html index f0820dde..0d8ebce2 100644 --- a/tags/sea-ice/index.html +++ b/tags/sea-ice/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Sea Ice

    National Center for Atmospheric Research

    UCAR, Affiliate NOAA-GFDL in Ocean division

    National Center for Atmospheric Research

    Princeton University, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.

    Sea Ice

    NCAR

    UCAR, Affiliate NOAA-GFDL

    ECMWF

    Princeton University, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/tags/sea-ice/index.xml b/tags/sea-ice/index.xml index 3e532ef7..4afb6952 100644 --- a/tags/sea-ice/index.xml +++ b/tags/sea-ice/index.xml @@ -1 +1 @@ -Sea Ice on M²LInEShttps://m2lines.github.io/tags/sea-ice/Recent content in Sea Ice on M²LInESHugo -- gohugo.ioen-usMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/National Center for Atmospheric ResearchMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDL in Ocean divisionLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDL \ No newline at end of file +Sea Ice on M²LInEShttps://m2lines.github.io/tags/sea-ice/Recent content in Sea Ice on M²LInESHugo -- gohugo.ioen-usMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/NCARMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDLLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/ECMWFWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDL \ No newline at end of file diff --git a/tags/talks/index.html b/tags/talks/index.html index c4326b2e..3bd7c91c 100644 --- a/tags/talks/index.html +++ b/tags/talks/index.html @@ -10,6 +10,6 @@

    talks

    A list of talks from previous years.

    2021

    • Joan Bruna - Deep Learning Barcelona: Geometric Deep Learning: Prospects and Challenges. 💻 Dec 22nd Video
    • AGU - Dec 13th-17th 2021 Find the list of talks and posters by our team members in our latest newsletter. 📊 💻 🎆 Newsletter
    • Laure Zanna - NeurIPS: Machine Learning and the Physical Sciences: The future of climate modeling in the age of machine learning (invited). 💻 🎆 Dec 13th More Info
    • Judith Berner - ASP S2S Colloquium 2021: S2S predictability and uncertainty. 🎆 🌎 Dec 7th Video
    • Joan Bruna - UW Data-driven methods in science and engineering seminar: Mathematical Prospects and Challenges of Machine Learning in the Physical World. 💻 🎆 Dec 3rd Video
    • Laure Zanna - DOE AI4ESP workshop: AI for Oceans: Improving Climate Projections. 💻 🎆 Dec 1st Video
    • Pierre Gentine - AI For Good: Better Better — machine learning for improved climate models and projections. 💻 🎆 Nov 17th Video
    • Laure Zanna - AI For Good: Bridging observations and numerical modelling of the ocean using machine learning. 💻 🎆 Nov 10th Video
    • Paul O’ Gorman and Janni Yuval - AI for Good: Improving rainfall and water-cycle projections through machine learning. 💻 🎆 Nov 3rd Video
    • Pierre Gentine - DOE AI4ESP workshop: Potential of Machine Learning for Climate Modeling. 💻 🎆 Nov 2nd Video
    • Laure Zanna - Podcast “Climate Now”: Improving Climate Models with Machine Learning. 💻 🎆 Nov 2nd Listen
    • KITP Conference: Machine Learning for Climate. Talks are available here -. In particular:
      • Laure Zanna - Machine Learning for Ocean Closures: Advances and Lessons. 💻 🎆 Nov 1st Video
      • Alistair Adcroft - Towards using machine learning in real climate models. 💻 🌎 Nov 1st Video
    • Paul O’ Gorman - ESSICUMD: Changing Precipitation and the Potential for Machine Learning To Improve Climate Predictions. 💻 🎆 Oct 14th Video
    • Pierre Gentine KGML 2021: Hybrid modeling (Physics + Machine Learning)to improve predictions of the hydrological cycle. 💻 🎆 Aug 13th Video
    • Joan Bruna - MSRI Summer School, Mathematics of Deep Learning. 💻 Aug 5-7th Part 1 Part 2 Part 3 Part 4 Part 5
    • Laure Zanna - One World Mathematics: Climate Modeling in the Age of Machine Learning. 💻 🎆 July 6th Video
    • Joan Bruna - IPAM: Sparse Linear Programming and (simple) neural networks. 💻 Mar 4th Video
    • Joan Bruna - Paris AI Research Institute: Mathematical aspects of neural network approximation and learning. 💻 Feb 11th Video
    • Ryan Abernathey - ECMWF: Pangeo Forge. 📊 Feb 10th Video
    • Ryan Abernathey - National Academies “Ocean Decade: U.S. Launch Meeting”: OceanCloud: Transforming oceanography with a new approach to data and computing. 📊 Feb 4th Video
    • Laure Zanna - 2021 CESM Ocean Model: Discovering equations from data for ocean mesoscale parametrizations. 💻 🎆 Feb 3rd Video

    2020

    • Ryan Abernathey - JupyterCon 2020: Cloud Native Repositories for Big Scientific Data. 📊 Nov 3rd Video
    • Marika Holland - CAMEE Research Seminar: Using Climate Models to predict changing Arctic sea ice. 🎆 🌎 Oct 23rd Video
    • Laure Zanna - IMSI: Blending Physics and Machine Learning for Climate Projections. 💻 🎆 Oct 9th Video
    • Pierre Gentine - AI4ESS Summer School: Physics-guided Machine Learning. 💻 🎆 Jun 26th Video
    • Marika Holland - M6 L5: Climate Modeling to Understand a Changing Arctic. 🎆 🌎 Mar 17th Video

    2019

    • Marika Holland - IASC Medal 2019: Perspectives on Predicting Arctic Sea Ice From Seasons to Centuries. 🎆 🌎 May 31st Video
    • Julie Deshayes - Museum national d’Histoire naturelle The Oceanic Change (in French). 🎆 🌎 May 22nd Video
    • Alistair Adcroft - 2019 SIAM conference: Challenges in modelling the oceans for climate. 🎆 🌎 Feb Video

    Your can find most of our past talks, and much more, on our Youtube Channel

    Themes of the talks:

    • 📊 Big data
    • 💻 Machine Learning
    • 🎆 Physics discovery
    • 🌎 Modeling

    Recent talks

    2024

    Video Preview

    Aakash Sane
    Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks
    CESM OMWG - February 💻 🎆

    2023

    Video Preview

    Pavel Perezhogin
    Machine-Learned parameterizations of mesoscale eddies in MOM6 ocean model: convolutional neural network and symbolic regression
    28th Annual CESM Workshop - June 💻 🎆

    Video Preview

    Laure Zanna
    Ocean and Atmosphere Dynamics
    Climatematch academy - July 💻 🎆

    Video Preview

    Fabrizio Falasca
    Exploting the nonstationarity of coastal sea level probability distributions
    NOAA-CVP Webinar Series 2023 - April 💻 🎆

    Video Preview

    Paul O'Gorman
    Improving climate models using machine learning
    MIT Generative AI Week - November 💻 🎆

    Video Preview

    Laure Zanna
    Transforming Climate Modeling with AI: hype or Reality?
    UN AI for Good - March 💻 🎆

    Video Preview

    Pavel Perezhogin
    Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
    NEMO working group on Machine-Learning - April 💻 🎆

    2022

    Video Preview

    Janni Yuval
    Neural-network parameterization of subgrid momentum transport learned from a high-resolution simulation
    23rd AMS Conference on Atmospheric and Oceanic Fluid Dynamics - June 💻 🎆

    Video Preview

    Aakash Sane
    Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Neural Networks
    February 💻 🎆

    Video Preview

    Lorenzo Zampieri
    On the design strategy of subgrid parameterizations in modern sea ice models
    Arctic Model Parameterization Development - December 22nd 💻 🎆

    Video Preview

    Ryan Abernathey
    Climate Science and AI in Big Tech
    Climate Change AI Webinar Series - December 15th 📊 💻
    NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

    Video Preview

    Pierre Gentine
    Physics to Machine Learning and Machine Learning Back to Physics
    Columbia Center of AI Technology - December 7th 💻 🎆

    Video Preview

    Laure Zanna and Steven Brunton
    Machine learning in fluid dynamics and climate physics
    Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

    Video Preview

    Laure Zanna
    Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
    LEAP Seminar - September 22nd 💻 🎆

    Video Preview

    Laure Zanna
    AI for Climate Physics
    Hammers and Nails - August 4th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Plenary talk
    SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
    NOTE: More info at this website.

    Video Preview

    Laure Zanna
    Joint plenary talk with Galen McKinley on M²LInES and LEAP
    2022 CESM Workshop - June 13th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Machine Learning for Mesoscale Closures in Ocean Models
    NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

    Video Preview

    Pierre Gentine
    Can Artificial Intelligence Help Us Better Project Climate Change?
    The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

    Video Preview

    Andrew Ross
    Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
    NOTE: You can find the panel on the top main video at 7:49:25 here .

    Video Preview

    Laure Zanna
    Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
    Ocean Sciences Meeting - March 4th 💻 🎆 🌎

    Video Preview

    Mitch Bushuk
    Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
    Ocean Sciences Meeting - March 3rd 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud: A New Approach to Ocean Data and Computing
    Ocean Sciences Meeting - March 3rd 📊

    Video Preview

    Lorenzo Zampieri
    A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
    Ocean Sciences Meeting - March 1st 💻 🎆

    Video Preview

    Dhruv Balwada
    Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
    Ocean Sciences Meeting - March 1st 🎆

    Video Preview

    Brandon Reichl
    A potential energy analysis of ocean surface mixed layers
    Ocean Sciences Meeting - February 28th

    Video Preview

    Andrew Ross
    Evaluating machine learning parameterizations of ocean subgrid forcing
    Ocean Sciences Meeting - February 28th 💻 🎆

    Video Preview

    Dhruv Balwada
    Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
    Ocean Sciences Meeting - February 28th 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud - Transforming oceanography with a new approach to data and computing
    Ocean Data Conference - February 15th 📊

    Video Preview

    Ryan Abernathey
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

    Video Preview

    Pierre Gentine
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

    Video Preview

    Laure Zanna
    Data-driven turbulence closures for ocean and climate models: advances and challenges
    UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆

    • This project is supported by Schmidt Sciences, LLC.

    Your can find most of our past talks, and much more, on our Youtube Channel

    Themes of the talks:

    • 📊 Big data
    • 💻 Machine Learning
    • 🎆 Physics discovery
    • 🌎 Modeling

    Recent talks

    2024

    Video Preview

    Pierre Gentine
    Harnessing AI to confront environmental challenges
    AI for Climate and Nature Spring Convening - Bezos Earth Fund - October 💻 🎆

    Video Preview

    Julia Simpson
    Improving Parameterizations of Heat + Momentum Air-Sea Fluxes
    LEAP Research Updates - September 💻 🎆

    Video Preview

    Laure Zanna
    (Keynote) AI-augmented climate Simulators and Emulators
    OMDP/COMMODORE Workshop - September 💻 🎆 🌎

    Video Preview

    Marika Holland
    The influence of historical forcing uncertainty on simulated Arctic change.
    IARPC Collaborations - August 📊 🌎

    Video Preview

    Laure Zanna, Sara Shamekh, Pierre Gentine
    Navigating the Academic Search Process
    LEAP Professional Development Series

    Video Preview

    Paul O'Gorman
    AMIT's BC3 and Climate and Weather Extremes
    ICCS Cross VESRI Journal - February 💻 🎆

    Video Preview

    Will Chapman
    Advancing Weather and Climate Prediction with Data Driven Methods
    Paul G. Allen School - February 💻 🌎

    Video Preview

    Laure Zanna
    Accelerating Discovery in Climate Physics with Machine Learning
    Simons foundation Presidential Lecture - February 💻 🎆

    Video Preview

    Pierre Gentine
    AI in Climate Science: From Emulation to New Discoveries
    Simons foundation Presidential Lecture - February 💻 🎆

    Video Preview

    Pierre Gentine
    Machine learning across scales to improve climate models: from emulation to discoveries
    Mathematics of Climate - February 💻 🎆 🌎

    Video Preview

    Aakash Sane
    Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks
    CESM OMWG - February 💻 🎆

    2023

    Video Preview

    Paul O'Gorman
    Improving climate models using machine learning
    MIT Generative AI Week - November 💻 🎆 🌎

    Video Preview

    Laure Zanna
    A New Generation of Global Climate Models Augmented by AI
    Distinguished Seminar Series in Computational Science and Engineering - October 2023 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Ocean and Atmosphere Dynamics
    Climatematch academy - July 💻 🎆

    Video Preview

    Pavel Perezhogin
    Machine-Learned parameterizations of mesoscale eddies in MOM6 ocean model: convolutional neural network and symbolic regression
    28th Annual CESM Workshop - June 💻 🌎

    Video Preview

    Fabrizio Falasca
    Exploting the nonstationarity of coastal sea level probability distributions
    NOAA-CVP Webinar Series 2023 - April 💻 🎆

    Video Preview

    Pavel Perezhogin
    Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
    NEMO working group on Machine-Learning - April 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Transforming Climate Modeling with AI: hype or Reality?
    UN AI for Good - March 💻 🌎

    2022

    Video Preview

    Janni Yuval
    Neural-network parameterization of subgrid momentum transport learned from a high-resolution simulation
    23rd AMS Conference on Atmospheric and Oceanic Fluid Dynamics - June 💻 🎆

    Video Preview

    Aakash Sane
    Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Neural Networks
    February 💻 🎆

    Video Preview

    Lorenzo Zampieri
    On the design strategy of subgrid parameterizations in modern sea ice models
    Arctic Model Parameterization Development - December 22nd 💻 🌎

    Video Preview

    Ryan Abernathey
    Climate Science and AI in Big Tech
    Climate Change AI Webinar Series - December 15th 📊 💻
    NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

    Video Preview

    Pierre Gentine
    Physics to Machine Learning and Machine Learning Back to Physics
    Columbia Center of AI Technology - December 7th 💻 🎆

    Video Preview

    Laure Zanna and Steven Brunton
    Machine learning in fluid dynamics and climate physics
    Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

    Video Preview

    Laure Zanna
    Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
    LEAP Seminar - September 22nd 💻 🎆 🌎

    Video Preview

    Laure Zanna
    AI for Climate Physics
    Hammers and Nails - August 4th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Plenary talk
    SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
    NOTE: More info at this website.

    Video Preview

    Laure Zanna
    Joint plenary talk with Galen McKinley on M²LInES and LEAP
    2022 CESM Workshop - June 13th 💻 🎆 🌎

    Video Preview

    Laure Zanna
    Machine Learning for Mesoscale Closures in Ocean Models
    NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

    Video Preview

    Pierre Gentine
    Can Artificial Intelligence Help Us Better Project Climate Change?
    The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

    Video Preview

    Andrew Ross
    Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
    NOTE: You can find the panel on the top main video at 7:49:25 here .

    Video Preview

    Laure Zanna
    Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
    Ocean Sciences Meeting - March 4th 💻 🎆 🌎

    Video Preview

    Mitch Bushuk
    Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
    Ocean Sciences Meeting - March 3rd 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud: A New Approach to Ocean Data and Computing
    Ocean Sciences Meeting - March 3rd 📊

    Video Preview

    Lorenzo Zampieri
    A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
    Ocean Sciences Meeting - March 1st 💻 🎆

    Video Preview

    Dhruv Balwada
    Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
    Ocean Sciences Meeting - March 1st 🎆

    Video Preview

    Brandon Reichl
    A potential energy analysis of ocean surface mixed layers
    Ocean Sciences Meeting - February 28th

    Video Preview

    Andrew Ross
    Evaluating machine learning parameterizations of ocean subgrid forcing
    Ocean Sciences Meeting - February 28th 💻 🎆

    Video Preview

    Dhruv Balwada
    Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
    Ocean Sciences Meeting - February 28th 🎆

    Video Preview

    Ryan Abernathey
    OpenOceanCloud - Transforming oceanography with a new approach to data and computing
    Ocean Data Conference - February 15th 📊

    Video Preview

    Ryan Abernathey
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

    Video Preview

    Pierre Gentine
    Workshop AI for Earth Sciences
    The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

    Video Preview

    Laure Zanna
    Data-driven turbulence closures for ocean and climate models: advances and challenges
    UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/aakashsane/index.html b/team/aakashsane/index.html index 3d568cc9..389dc3b6 100644 --- a/team/aakashsane/index.html +++ b/team/aakashsane/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Aakash Sane

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL in Ocean Division

    • This project is supported by Schmidt Sciences, LLC.

    Aakash Sane

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/abigailbodner/index.html b/team/abigailbodner/index.html index 578c42a9..7b3a7358 100644 --- a/team/abigailbodner/index.html +++ b/team/abigailbodner/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Abigail Bodner

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Abigail Bodner

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/adamsubel/index.html b/team/adamsubel/index.html index edc46578..83769f55 100644 --- a/team/adamsubel/index.html +++ b/team/adamsubel/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Adam Subel

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Adam Subel

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/alistairadcroft/index.html b/team/alistairadcroft/index.html index aee45dea..2188a8cb 100644 --- a/team/alistairadcroft/index.html +++ b/team/alistairadcroft/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Alistair Adcroft

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL in Ocean division

    • This project is supported by Schmidt Sciences, LLC.

    Alistair Adcroft

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/andrewbrettin/index.html b/team/andrewbrettin/index.html index ef020a57..8cccab55 100644 --- a/team/andrewbrettin/index.html +++ b/team/andrewbrettin/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Andrew Brettin

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Andrew Brettin

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/antoinenasser/index.html b/team/antoinenasser/index.html new file mode 100644 index 00000000..b27a824f --- /dev/null +++ b/team/antoinenasser/index.html @@ -0,0 +1,13 @@ +Antoine-Alexis Nasser - M²LInES +
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/brandonreichl/index.html b/team/brandonreichl/index.html index b35474a9..6d757d5f 100644 --- a/team/brandonreichl/index.html +++ b/team/brandonreichl/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Brandon Reichl

    Monday, Jan 1, 0001

    NOAA-GFDL in Ocean division

    • This project is supported by Schmidt Sciences, LLC.

    Brandon Reichl

    Monday, Jan 1, 0001

    NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/carlosferndandezgranda/index.html b/team/carlosferndandezgranda/index.html index 2e436acb..aa53e1d0 100644 --- a/team/carlosferndandezgranda/index.html +++ b/team/carlosferndandezgranda/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Carlos Fernandez-Granda

    Monday, Nov 19, 2018

    NYU, Courant Institute + Center for Data Science

    • This project is supported by Schmidt Sciences, LLC.

    Carlos Fernandez-Granda

    Monday, Nov 19, 2018

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/cemgultekin/index.html b/team/cemgultekin/index.html index 113100ff..21e21aa3 100644 --- a/team/cemgultekin/index.html +++ b/team/cemgultekin/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Cem Gultekin

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Cem Gultekin

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/chrispedersen/index.html b/team/chrispedersen/index.html index e223de26..a5065f7a 100644 --- a/team/chrispedersen/index.html +++ b/team/chrispedersen/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Chris Pedersen

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Chris Pedersen

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/dhruvbalwada/index.html b/team/dhruvbalwada/index.html index 2aa4d172..2a36cd86 100644 --- a/team/dhruvbalwada/index.html +++ b/team/dhruvbalwada/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Dhruv Balwada

    Monday, Jan 1, 0001

    Lamont/Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Dhruv Balwada

    Monday, Jan 1, 0001

    LDEO,Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/fabriziofalasca/index.html b/team/fabriziofalasca/index.html index 6b5c9a7b..287989e9 100644 --- a/team/fabriziofalasca/index.html +++ b/team/fabriziofalasca/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Fabrizio Falasca

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Fabrizio Falasca

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/feiyulu/index.html b/team/feiyulu/index.html index d3cc9b9b..62651828 100644 --- a/team/feiyulu/index.html +++ b/team/feiyulu/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Feiyu Lu

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal division

    • This project is supported by Schmidt Sciences, LLC.

    Feiyu Lu

    Monday, Jan 1, 0001

    Princeton University, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/adrienburq/index.html b/team/former/adrienburq/index.html index 62c77c26..bbd23c5b 100644 --- a/team/former/adrienburq/index.html +++ b/team/former/adrienburq/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Adrien Burq

    Monday, Jan 1, 0001

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Adrien Burq

    Monday, Jan 1, 0001

    CNRS-IPSL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/anastasiiagorbunova/index.html b/team/former/anastasiiagorbunova/index.html index 198c7ee3..e926ac55 100644 --- a/team/former/anastasiiagorbunova/index.html +++ b/team/former/anastasiiagorbunova/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Anastasiia Gorbunova

    Monday, Jan 1, 0001

    L’Institut des Géosciences de l’Environnement (IGE)

    • This project is supported by Schmidt Sciences, LLC.

    Anastasiia Gorbunova

    Monday, Jan 1, 0001

    Comsol

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/andrewross/index.html b/team/former/andrewross/index.html index 95441ec2..153e6ec1 100644 --- a/team/former/andrewross/index.html +++ b/team/former/andrewross/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Andrew Ross

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Andrew Ross

    Monday, Jan 1, 0001

    Arcadia

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/aurorabasinki/index.html b/team/former/aurorabasinki/index.html new file mode 100644 index 00000000..f2ed6f3e --- /dev/null +++ b/team/former/aurorabasinki/index.html @@ -0,0 +1,13 @@ +Aurora Basinski - M²LInES +
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/azizbhouri/index.html b/team/former/azizbhouri/index.html index 6cc6f610..bfb7616f 100644 --- a/team/former/azizbhouri/index.html +++ b/team/former/azizbhouri/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Mohamed Aziz Bhouri

    Monday, Jan 1, 0001

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Mohamed Aziz Bhouri

    Monday, Jan 1, 0001

    Goldman Sachs

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/elizabethyankovsky/index.html b/team/former/elizabethyankovsky/index.html index be881bd9..c9569c0e 100644 --- a/team/former/elizabethyankovsky/index.html +++ b/team/former/elizabethyankovsky/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Elizabeth Yankovsky

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Elizabeth Yankovsky

    Monday, Jan 1, 0001

    Yale University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/emilynewsom/index.html b/team/former/emilynewsom/index.html index f7114019..4c2da578 100644 --- a/team/former/emilynewsom/index.html +++ b/team/former/emilynewsom/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Emily Newsom

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Emily Newsom

    Monday, Jan 1, 0001

    Caltech

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/friedrichginnold/index.html b/team/former/friedrichginnold/index.html index 53b4f75b..604a2fb4 100644 --- a/team/former/friedrichginnold/index.html +++ b/team/former/friedrichginnold/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Friedrich Ginnold

    Monday, Jan 1, 0001

    ETH Zürich

    • This project is supported by Schmidt Sciences, LLC.

    Friedrich Ginnold

    Monday, Jan 1, 0001

    Netlight

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/hugofrezat/index.html b/team/former/hugofrezat/index.html index 6c858166..eb727948 100644 --- a/team/former/hugofrezat/index.html +++ b/team/former/hugofrezat/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Hugo Frezat

    Monday, Jan 1, 0001

    L’Institut des Géosciences de l’Environnement (IGE)

    • This project is supported by Schmidt Sciences, LLC.

    Hugo Frezat

    Monday, Jan 1, 0001

    CNRS-IGE

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/index.html b/team/former/index.html index 66a6e8fb..8b7bf568 100644 --- a/team/former/index.html +++ b/team/former/index.html @@ -8,18 +8,6 @@
  • Resources
  • News

    • Former members of M²LInES

    Photo of Ziwei Li

    Ziwei Li

    Postdoc

    NYU, Courant Institute

    Atmosphere -Ocean -Machine Learning
    Photo of Andrew Ross

    Andrew Ross

    Postdoc

    NYU, Courant Institute

    Machine Learning

    Interpretability

    Photo of Anastasiia Gorbunova

    Anastasiia Gorbunova

    Postdoc

    L’Institut des Géosciences de l’Environnement (IGE)

    Ocean

    Computational Fluid Mechanics

    Photo of Mario Herrero-Gonzalez

    Mario Herrero-Gonzalez

    Affiliate, Graduate Student

    Columbia University

    Data Assimilation

    Deep Learning

    Photo of Mohamed Aziz Bhouri

    Mohamed Aziz Bhouri

    Affiliate, Postdoc

    Columbia University

    Atmosphere -Machine Learning
    Photo of Hugo Frezat

    Hugo Frezat

    Affiliate, Graduate Student

    L’Institut des Géosciences de l’Environnement (IGE)

    Ocean -Machine Learning

    Turbulence paramezizations

    Photo of Adrien Burq

    Adrien Burq

    Affiliate, Graduate Student

    Columbia University

    Atmosphere -Machine Learning
    Photo of Elizabeth Yankovsky

    Elizabeth Yankovsky

    Affiliate, Postdoc

    NYU, Courant Institute

    Ocean

    Arctic Dynamics

    Photo of Chris Dupuis

    Chris Dupuis

    Affiliate, Staff Associate

    Lamont/Columbia University

    Atmosphere

    Software Architecture

    Photo of J Emmanuel Johnson

    J Emmanuel Johnson

    Affiliate, Postdoc

    L’Institut des Géosciences de l’Environnement (IGE)

    Ocean -Data Assimilation -Machine Learning
    Photo of Emily Newsom

    Emily Newsom

    Research Associate

    NYU, Courant Institute

    Coupled Physics

    Diagnostics

    Photo of Maria Prat Colomer

    Maria Prat Colomer

    Affiliate, Grad Student

    NYU, Courant Institute

    Machine Learning

    Signal Processing

    Photo of Patricia Luc

    Patricia Luc

    Affiliate, Undergraduate Student

    Columbia University

    Machine Learning

    Subgrid Parametrizations

    Photo of Matias Ortiz

    Matias Ortiz

    Affiliate, Graduate Student

    NYU, Courant Institute

    Ocean -Climate Model Development -Coupled Physics
    Photo of Shubham Gupta

    Shubham Gupta

    Graduate Student

    NYU, Tandon School of Engineering

    Machine Learning -Ocean

    Website Contact

    Photo of Leo Liu

    Leo Liu

    Affiliate, Undergraduate Student

    NYU, Courant Institute

    Data engineering

    Photo of Shantanu Acharya

    Shantanu Acharya

    Affiliate, Graduate Student

    NYU, Courant Institute

    Deep Learning

    Photo of Friedrich Ginnold

    Friedrich Ginnold

    Affiliate, Graduate Student

    ETH Zürich

    Machine Learning -Atmosphere
    Photo of Jisu Han

    Jisu Han

    Affiliate, Graduate Student

    Columbia University

    Atmosphere -Ocean
    • This project is supported by Schmidt Sciences, LLC.

    Former members of M²LInES

    Photo of Ziwei Li

    Ziwei Li

    Research Scientist

    Meta

    Photo of Aurora Basinski

    Aurora Basinski

    Postdoc

    Scripps, UCSD

    Photo of Andrew Ross

    Andrew Ross

    Applied Scientist

    Arcadia

    Photo of Anastasiia Gorbunova

    Anastasiia Gorbunova

    Development Engineer

    Comsol

    Photo of Mario Herrero-Gonzalez

    Mario Herrero-Gonzalez

    Graduate Student

    The University of Edinburgh

    Photo of Mohamed Aziz Bhouri

    Mohamed Aziz Bhouri

    Quant

    Goldman Sachs

    Photo of Hugo Frezat

    Hugo Frezat

    Researcher

    CNRS-IGE

    Photo of Adrien Burq

    Adrien Burq

    Research Engineer

    CNRS-IPSL

    Photo of Elizabeth Yankovsky

    Elizabeth Yankovsky

    Professor

    Yale University

    Photo of Chris Dupuis

    Chris Dupuis

    Affiliate, Staff Associate

    Lamont/Columbia University

    Photo of J Emmanuel Johnson

    J Emmanuel Johnson

    Postdoc

    CNRS-IGE

    Photo of Emily Newsom

    Emily Newsom

    Senior Research Scientist

    Caltech

    Photo of Maria Prat Colomer

    Maria Prat Colomer

    Grad Student

    Universitat Politècnica de Catalunya

    Photo of Patricia Luc

    Patricia Luc

    Software Engineer

    Microsoft

    Photo of Matias Ortiz

    Matias Ortiz

    Graduate Student

    NYU

    Photo of Shubham Gupta

    Shubham Gupta

    ML Engineer

    Rivet.us

    Photo of Leo Liu

    Leo Liu

    Undergraduate Student

    Photo of Shantanu Acharya

    Shantanu Acharya

    Graduate Student

    NYU

    Photo of Friedrich Ginnold

    Friedrich Ginnold

    IT consultant

    Netlight

    Photo of Jisu Han

    Jisu Han

    Graduate Student

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/index.xml b/team/former/index.xml index 57902d51..17501630 100644 --- a/team/former/index.xml +++ b/team/former/index.xml @@ -1 +1 @@ -M²LInEShttps://m2lines.github.io/team/former/Recent content on M²LInESHugo -- gohugo.ioen-usZiwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/NYU, Courant InstituteAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/NYU, Courant InstituteAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/L’Institut des Géosciences de l’Environnement (IGE)Mario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/Columbia UniversityMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Columbia UniversityHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/L’Institut des Géosciences de l’Environnement (IGE)Adrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/Columbia UniversityElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/NYU, Courant InstituteChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/L’Institut des Géosciences de l’Environnement (IGE)Emily Newsomhttps://m2lines.github.io/team/former/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/emilynewsom/NYU, Courant InstituteMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/NYU, Courant InstitutePatricia Luchttps://m2lines.github.io/team/former/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/patricialuc/Columbia UniversityMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYU, Courant InstituteShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/NYU, Tandon School of EngineeringLeo Liuhttps://m2lines.github.io/team/former/leoliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/leoliu/NYU, Courant InstituteShantanu Acharyahttps://m2lines.github.io/team/former/shantanu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shantanu/NYU, Courant InstituteFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/ETH ZürichJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia University \ No newline at end of file +M²LInEShttps://m2lines.github.io/team/former/Recent content on M²LInESHugo -- gohugo.ioen-usZiwei Lihttps://m2lines.github.io/team/former/ziweili/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/ziweili/MetaAurora Basinskihttps://m2lines.github.io/team/former/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/aurorabasinki/Scripps, UCSDAndrew Rosshttps://m2lines.github.io/team/former/andrewross/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/andrewross/ArcadiaAnastasiia Gorbunovahttps://m2lines.github.io/team/former/anastasiiagorbunova/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/anastasiiagorbunova/ComsolMario Herrero-Gonzalezhttps://m2lines.github.io/team/former/marioherrerogonzalez/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/marioherrerogonzalez/The University of EdinburghMohamed Aziz Bhourihttps://m2lines.github.io/team/former/azizbhouri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/azizbhouri/Goldman SachsHugo Frezathttps://m2lines.github.io/team/former/hugofrezat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/hugofrezat/CNRS-IGEAdrien Burqhttps://m2lines.github.io/team/former/adrienburq/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/adrienburq/CNRS-IPSLElizabeth Yankovskyhttps://m2lines.github.io/team/former/elizabethyankovsky/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/elizabethyankovsky/Yale UniversityChris Dupuishttps://m2lines.github.io/team/former/chrisdupuis/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/chrisdupuis/Lamont/Columbia UniversityJ Emmanuel Johnsonhttps://m2lines.github.io/team/former/jemanjohnson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jemanjohnson/CNRS-IGEEmily Newsomhttps://m2lines.github.io/team/former/emilynewsom/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/emilynewsom/CaltechMaria Prat Colomerhttps://m2lines.github.io/team/former/mariapratcolomer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/mariapratcolomer/Universitat Politècnica de CatalunyaPatricia Luchttps://m2lines.github.io/team/former/patricialuc/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/patricialuc/MicrosoftMatias Ortizhttps://m2lines.github.io/team/former/matiasortiz/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/matiasortiz/NYUShubham Guptahttps://m2lines.github.io/team/former/shubham/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shubham/Rivet.usLeo Liuhttps://m2lines.github.io/team/former/leoliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/leoliu/Shantanu Acharyahttps://m2lines.github.io/team/former/shantanu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/shantanu/NYUFriedrich Ginnoldhttps://m2lines.github.io/team/former/friedrichginnold/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/friedrichginnold/NetlightJisu Hanhttps://m2lines.github.io/team/former/jisuhan/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/former/jisuhan/Columbia University \ No newline at end of file diff --git a/team/former/jemanjohnson/index.html b/team/former/jemanjohnson/index.html index bbd6c64a..0a6f7b7e 100644 --- a/team/former/jemanjohnson/index.html +++ b/team/former/jemanjohnson/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • J Emmanuel Johnson

    Monday, Jan 1, 0001

    L’Institut des Géosciences de l’Environnement (IGE)

    • This project is supported by Schmidt Sciences, LLC.

    J Emmanuel Johnson

    Monday, Jan 1, 0001

    CNRS-IGE

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/leoliu/index.html b/team/former/leoliu/index.html index b7b06f31..0b8113ab 100644 --- a/team/former/leoliu/index.html +++ b/team/former/leoliu/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Leo Liu

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Leo Liu

    Monday, Jan 1, 0001
    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/mariapratcolomer/index.html b/team/former/mariapratcolomer/index.html index e01136c5..028677c2 100644 --- a/team/former/mariapratcolomer/index.html +++ b/team/former/mariapratcolomer/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Maria Prat Colomer

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Maria Prat Colomer

    Monday, Jan 1, 0001

    Universitat Politècnica de Catalunya

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/marioherrerogonzalez/index.html b/team/former/marioherrerogonzalez/index.html index 161326be..1f1f3f1f 100644 --- a/team/former/marioherrerogonzalez/index.html +++ b/team/former/marioherrerogonzalez/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Mario Herrero-Gonzalez

    Monday, Jan 1, 0001

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Mario Herrero-Gonzalez

    Monday, Jan 1, 0001

    The University of Edinburgh

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/matiasortiz/index.html b/team/former/matiasortiz/index.html index 9b1b4260..d66786f9 100644 --- a/team/former/matiasortiz/index.html +++ b/team/former/matiasortiz/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Matias Ortiz

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Matias Ortiz

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/patricialuc/index.html b/team/former/patricialuc/index.html index 4accdbfd..e2fb2e56 100644 --- a/team/former/patricialuc/index.html +++ b/team/former/patricialuc/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Patricia Luc

    Monday, Jan 1, 0001

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Patricia Luc

    Monday, Jan 1, 0001

    Microsoft

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/shantanu/index.html b/team/former/shantanu/index.html index febdd33b..0339e9f8 100644 --- a/team/former/shantanu/index.html +++ b/team/former/shantanu/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Shantanu Acharya

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Shantanu Acharya

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/shubham/index.html b/team/former/shubham/index.html index cb6326ed..e482001a 100644 --- a/team/former/shubham/index.html +++ b/team/former/shubham/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Shubham Gupta

    Monday, Jan 1, 0001

    NYU, Tandon School of Engineering

    • This project is supported by Schmidt Sciences, LLC.

    Shubham Gupta

    Monday, Jan 1, 0001

    Rivet.us

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/former/ziweili/index.html b/team/former/ziweili/index.html index 4fd23299..6b3475fb 100644 --- a/team/former/ziweili/index.html +++ b/team/former/ziweili/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Ziwei Li

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Ziwei Li

    Monday, Jan 1, 0001

    Meta

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/gabrielmouttapa/index.html b/team/gabrielmouttapa/index.html index 44a37c5b..1786a009 100644 --- a/team/gabrielmouttapa/index.html +++ b/team/gabrielmouttapa/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Gabriel Mouttapa

    Monday, Jan 1, 0001

    IGE

    • This project is supported by Schmidt Sciences, LLC.

    Gabriel Mouttapa

    Monday, Jan 1, 0001

    CNRS-IGE

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/index.html b/team/index.html index 328d4b45..31202d35 100644 --- a/team/index.html +++ b/team/index.html @@ -8,68 +8,71 @@
  • Resources
  • News

    • Meet members of M²LInES

    Photo of Laure Zanna

    Laure Zanna

    Lead PI

    NYU, Courant Institute + Center for Data Science

    Ocean +

    Meet members of M²LInES

    Photo of Laure Zanna

    Laure Zanna

    Lead PI

    NYU

    Ocean Climate Model Development -Machine Learning
    Photo of Johanna Goldman

    Johanna Goldman

    Project Manager

    NYU, Courant Institute

    Ocean

    Project Management

    Photo of Alistair Adcroft

    Alistair Adcroft

    PI

    Princeton University, Affiliate NOAA-GFDL in Ocean division

    Ocean +Machine Learning
    Photo of Johanna Goldman

    Johanna Goldman

    Project Manager

    NYU

    Ocean

    Project Management

    Photo of Alistair Adcroft

    Alistair Adcroft

    PI

    Princeton University, Affiliate NOAA-GFDL

    Ocean Machine Learning -Climate Model Development
    Photo of Carlos Fernandez-Granda

    Carlos Fernandez-Granda

    PI

    NYU, Courant Institute + Center for Data Science

    Ocean -Machine Learning

    Deep Learning

    Photo of Joan Bruna

    Joan Bruna

    PI

    NYU, Courant Institute + Computer Science + Center for Data Science

    Machine Learning

    Signal Processing

    Photo of Ryan Abernathey

    Ryan Abernathey

    PI

    Lamont/Columbia University

    Ocean

    Data

    Photo of Brandon Reichl

    Brandon Reichl

    Lead Scientist

    NOAA-GFDL in Ocean division

    Ocean -Climate Model Development

    Air-Sea Interactions

    Photo of Feiyu Lu

    Feiyu Lu

    Lead Scientist

    Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal division

    Ocean +Climate Model Development
    Photo of Carlos Fernandez-Granda

    Carlos Fernandez-Granda

    PI

    NYU

    Ocean +Machine Learning

    Deep Learning

    Photo of Joan Bruna

    Joan Bruna

    PI

    NYU

    Machine Learning

    Signal Processing

    Photo of Ryan Abernathey

    Ryan Abernathey

    Affiliate, PI

    Earthmovers

    Ocean

    Data

    Photo of Brandon Reichl

    Brandon Reichl

    Lead Scientist

    NOAA-GFDL

    Ocean +Climate Model Development

    Air-Sea Interactions

    Photo of Feiyu Lu

    Feiyu Lu

    Lead Scientist

    Princeton University, Affiliate NOAA-GFDL

    Ocean Data Assimilation -Climate Model Development
    Photo of Judith Berner

    Judith Berner

    PI

    National Center for Atmospheric Research

    Atmosphere -Data Assimilation
    Photo of Marika Holland

    Marika Holland

    PI

    National Center for Atmospheric Research

    Sea Ice -Climate Model Development
    Photo of Mitch Bushuk

    Mitch Bushuk

    Lead Scientist

    UCAR, Affiliate NOAA-GFDL in Ocean division

    Sea Ice +Climate Model Development
    Photo of Judith Berner

    Judith Berner

    PI

    NCAR

    Atmosphere +Data Assimilation
    Photo of Marika Holland

    Marika Holland

    PI

    NCAR

    Sea Ice +Climate Model Development
    Photo of Mitch Bushuk

    Mitch Bushuk

    Lead Scientist

    UCAR, Affiliate NOAA-GFDL

    Sea Ice Data Assimilation

    Predictability

    Photo of Pierre Gentine

    Pierre Gentine

    PI

    Columbia University

    Atmosphere Machine Learning
    Photo of Paul O'Gorman

    Paul O'Gorman

    PI

    MIT

    Atmosphere Machine Learning

    Moist Convection

    Photo of Julie Deshayes

    Julie Deshayes

    PI

    CNRS-IPSL

    Ocean Climate Model Development -Coupled Physics
    Photo of Julien Le Sommer

    Julien Le Sommer

    PI

    L’Institut des Géosciences de l’Environnement (IGE)

    Ocean -Climate Model Development
    Photo of Arthur Guillaumin

    Arthur Guillaumin

    Affiliate

    Queen Mary University of London

    Machine Learning

    Statistics

    Photo of Aurora Basinski

    Aurora Basinski

    Affiliate, Graduate Student

    NYU Courant Institute

    Ocean -Coupled Physics
    Photo of Janni Yuval

    Janni Yuval

    Affiliate

    Google Research

    Atmosphere +Coupled Physics
    Photo of Julien Le Sommer

    Julien Le Sommer

    PI

    CNRS-IGE

    Ocean +Climate Model Development
    Photo of Arthur Guillaumin

    Arthur Guillaumin

    Affiliate

    Queen Mary University of London

    Machine Learning

    Statistics

    Photo of Janni Yuval

    Janni Yuval

    Affiliate

    Google Research

    Atmosphere Machine Learning Climate Model Development
    Photo of Alex Connolly

    Alex Connolly

    Postdoc

    Columbia University

    Atmosphere -Machine Learning

    Boundary Layer Turbulence

    Photo of Julius Busecke

    Julius Busecke

    Lead of Open Research

    Columbia University

    Ocean

    Open Source Software

    Photo of Aakash Sane

    Aakash Sane

    Postdoc

    Princeton University, Affiliate NOAA-GFDL in Ocean Division

    Ocean +Machine Learning

    Boundary Layer Turbulence

    Photo of Julius Busecke

    Julius Busecke

    Lead of Open Research

    Columbia University

    Ocean

    Open Source Software

    Photo of Aakash Sane

    Aakash Sane

    Postdoc

    Princeton University, Affiliate NOAA-GFDL

    Ocean Machine Learning -Climate Model Development
    Photo of Lorenzo Zampieri

    Lorenzo Zampieri

    Affiliate

    National Center for Atmospheric Research

    Sea Ice

    HR Data and Observations

    Photo of Cem Gultekin

    Cem Gultekin

    Affiliate

    NYU, Courant Institute

    Machine Learning

    Interpretability

    Photo of Tarun Verma

    Tarun Verma

    Affliate

    Princeton University

    Ocean -Machine Learning

    Predictability

    Photo of Dhruv Balwada

    Dhruv Balwada

    Lead Scientist

    Lamont/Columbia University

    Ocean +Climate Model Development
    Photo of Lorenzo Zampieri

    Lorenzo Zampieri

    Affiliate

    ECMWF

    Sea Ice

    HR Data and Observations

    Photo of Cem Gultekin

    Cem Gultekin

    Affiliate

    NYU

    Machine Learning

    Interpretability

    Photo of Tarun Verma

    Tarun Verma

    Affliate

    John Hopkins University

    Ocean +Machine Learning

    Predictability

    Photo of Dhruv Balwada

    Dhruv Balwada

    Lead Scientist

    LDEO,Columbia University

    Ocean Machine Learning

    Macro-turbulence

    Photo of Cheng Zhang

    Cheng Zhang

    Affiliate

    Princeton University

    Ocean Machine Learning -Data Assimilation
    Photo of Pavel Perezhogin

    Pavel Perezhogin

    Postdoc

    NYU, Courant Institute

    Ocean -Climate Model Development

    Turbulence

    Photo of Will Chapman

    Will Chapman

    Postdoc

    National Center for Atmospheric Research

    Atmosphere +Data Assimilation
    Photo of Pavel Perezhogin

    Pavel Perezhogin

    Postdoc

    NYU

    Ocean +Climate Model Development

    Turbulence

    Photo of Will Chapman

    Will Chapman

    Research Scientist

    NCAR

    Atmosphere Data Assimilation Machine Learning
    Photo of Will Gregory

    Will Gregory

    Postdoc

    Princeton University, Affiliate NOAA-GFDL

    Sea Ice Data Assimilation -Machine Learning

    Predictability

    Photo of Abigail Bodner

    Abigail Bodner

    Affiliate

    NYU, Courant Institute

    Ocean +Machine Learning

    Predictability

    Photo of Abigail Bodner

    Abigail Bodner

    Affiliate

    NYU

    Ocean Machine Learning -Climate Model Development

    Turbulence

    Photo of Andrew Brettin

    Andrew Brettin

    Affiliate, Graduate Student

    NYU, Courant Institute

    Ocean -Machine Learning
    Photo of Karl Otness

    Karl Otness

    Graduate Student

    NYU, Courant Institute

    Machine Learning

    Simulation

    Photo of Fabrizio Falasca

    Fabrizio Falasca

    Affiliate, Postdoc

    NYU, Courant Institute

    Ocean

    Dynamical Systems

    Photo of Adam Subel

    Adam Subel

    Affiliate, Graduate Student

    NYU, Courant Institute

    Ocean +Climate Model Development

    Turbulence

    Photo of Andrew Brettin

    Andrew Brettin

    Affiliate, Graduate Student

    NYU

    Ocean +Machine Learning
    Photo of Karl Otness

    Karl Otness

    Graduate Student

    NYU

    Machine Learning

    Simulation

    Photo of Fabrizio Falasca

    Fabrizio Falasca

    Affiliate, Postdoc

    NYU

    Ocean

    Dynamical Systems

    Photo of Adam Subel

    Adam Subel

    Affiliate, Graduate Student

    NYU

    Ocean Machine Learning
    Photo of Alexis Barge

    Alexis Barge

    Affiliate

    CNRS-IGE

    Machine Learning Climate Model Development Coupled Physics
    Photo of Maud Tissot

    Maud Tissot

    Affiliate

    CNRS-IPSL

    Ocean Machine Learning -Climate Model Development
    Photo of Sara Shamekh

    Sara Shamekh

    Lead Scientist

    NYU, Courant Institute

    Atmosphere -Machine Learning

    Atmospheric Boundary Layer

    Photo of Chris Pedersen

    Chris Pedersen

    Postdoc

    NYU, Courant Institute

    Ocean +Climate Model Development
    Photo of Sara Shamekh

    Sara Shamekh

    Lead Scientist

    NYU

    Atmosphere +Machine Learning

    Atmospheric Boundary Layer

    Photo of Chris Pedersen

    Chris Pedersen

    Postdoc

    NYU

    Ocean Machine Learning
    Photo of Etienne Meunier

    Etienne Meunier

    PostDoc

    CNRS-IPSL

    Ocean -Machine Learning
    Photo of Nora Loose

    Nora Loose

    Affiliate

    Princeton University

    Ocean +Machine Learning
    Photo of Nora Loose

    Nora Loose

    Affiliate

    [C]Worthy

    Ocean Machine Learning

    Mesoscale Eddy Parameterizations

    Photo of Danni Du

    Danni Du

    Postdoc

    Princeton University

    Machine Learning Data Assimilation -Climate Model Development
    Photo of David Kamm

    David Kamm

    Affiliate, Graduate Student

    CNRS-IPSL

    Ocean

    Eddy Parameterizations

    Photo of Gabriel Mouttapa

    Gabriel Mouttapa

    Affiliate

    IGE

    Ocean -Data Assimilation

    Tuning

    Photo of Greta Miller

    Greta Miller

    Postdoc

    University of Oxford/NCAR

    Atmosphere -Machine Learning
    Photo of Jia-Rui (Ray) Shi

    Jia-Rui (Ray) Shi

    Postdoc

    NYU Courant

    Ocean -Coupled Physics

    Climate Science

    Photo of Marcela Contreras

    Marcela Contreras

    Postdoc

    IGE, Grenoble

    Ocean +Climate Model Development
    Photo of David Kamm

    David Kamm

    Affiliate, Graduate Student

    CNRS-IPSL

    Ocean

    Eddy Parameterizations

    Photo of Gabriel Mouttapa

    Gabriel Mouttapa

    Affiliate

    CNRS-IGE

    Ocean +Data Assimilation

    Tuning

    Photo of Greta Miller

    Greta Miller

    Affiliate, Graduate Student

    University of Oxford/NCAR

    Atmosphere +Machine Learning
    Photo of Jia-Rui (Ray) Shi

    Jia-Rui (Ray) Shi

    Postdoc

    NYU

    Ocean +Coupled Physics

    Climate Science

    Photo of Marcela Contreras

    Marcela Contreras

    Postdoc

    CNRS-IGE

    Ocean Climate Model Development -Coupled Physics

    Physical Oceanography

    Photo of Shuchang Liu

    Shuchang Liu

    Affiliate

    MIT EAPS

    Atmosphere -Machine Learning
    Photo of Kelsey Everard

    Kelsey Everard

    Postdoc

    NYU, Courant Institute

    Ocean -Machine Learning

    Mesoscale eddy parameterisation

    Photo of Surya Dheeshjith

    Surya Dheeshjith

    Graduate Student

    NYU, Courant Institute

    Machine Learning

    Data Analysis

    Photo of Griffin Mooers

    Griffin Mooers

    Postdoc

    MIT

    Atmosphere +Coupled Physics

    Physical Oceanography

    Photo of Shuchang Liu

    Shuchang Liu

    Affiliate

    MIT

    Atmosphere +Machine Learning
    Photo of Kelsey Everard

    Kelsey Everard

    Postdoc

    NYU

    Ocean +Machine Learning

    Mesoscale eddy parameterisation

    Photo of Surya Dheeshjith

    Surya Dheeshjith

    Laboratory Associate

    NYU

    Machine Learning

    Data Analysis

    Photo of Griffin Mooers

    Griffin Mooers

    Postdoc

    MIT

    Atmosphere Machine Learning

    Data Analysis

    Photo of Prani Nalluri

    Prani Nalluri

    Affiliate, Graduate Student

    Columbia University

    Ocean Machine Learning -Climate Model Development

    Subgrid-Scale Parametrization

    Photo of Renaud Falga

    Renaud Falga

    Postdoc

    NYU, Courant Institute

    Atmosphere +Climate Model Development

    Subgrid-Scale Parametrization

    Photo of Renaud Falga

    Renaud Falga

    Postdoc

    NYU

    Atmosphere Machine Learning Climate Model Development
    Photo of Julia Simpson

    Julia Simpson

    Affiliate, Graduate Student

    Columbia University

    Ocean Atmosphere Machine Learning

    Air-Sea Fluxes

    Photo of Jiarong Wu

    Jiarong Wu

    Affiliate, Postdoc

    NYU

    Ocean Coupled Physics
    Photo of Yongquan Qu

    Yongquan Qu

    Affiliate, Graduate Student

    Columbia University

    Atmosphere Machine Learning -Data Assimilation

    • This project is supported by Schmidt Sciences, LLC.
    Photo of Antoine-Alexis Nasser

    Antoine-Alexis Nasser

    Postdoc

    Princeton University

    Ocean +Machine Learning +Climate Model Development

    Topography

    Photo of Xavier Levine

    Xavier Levine

    Incoming Research Scientist

    Columbia University

    Machine Learning +Climate Model Development +Coupled Physics

    Multiscale Atmospheric Dynamics

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/index.xml b/team/index.xml index f4e75c8e..6f148625 100644 --- a/team/index.xml +++ b/team/index.xml @@ -1 +1 @@ -Team on M²LInEShttps://m2lines.github.io/team/Recent content in Team on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYU, Courant Institute + Center for Data ScienceJohanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYU, Courant InstituteAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDL in Ocean divisionCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYU, Courant Institute + Center for Data ScienceJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYU, Courant Institute + Computer Science + Center for Data ScienceRyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/Lamont/Columbia UniversityBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDL in Ocean divisionFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDL in Seasonal-decadal divisionJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/National Center for Atmospheric ResearchMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/National Center for Atmospheric ResearchMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDL in Ocean divisionPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/L’Institut des Géosciences de l’Environnement (IGE)Arthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonAurora Basinskihttps://m2lines.github.io/team/aurorabasinki/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aurorabasinki/NYU Courant InstituteJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDL in Ocean DivisionLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/National Center for Atmospheric ResearchCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYU, Courant InstituteTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/Princeton UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/Lamont/Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYU, Courant InstituteWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/National Center for Atmospheric ResearchWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYU, Courant InstituteAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYU, Courant InstituteKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYU, Courant InstituteFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYU, Courant InstituteAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYU, Courant InstituteAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYU, Courant InstituteChris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYU, Courant InstituteEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/Princeton UniversityDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/IGEGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYU CourantMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/IGE, GrenobleShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MIT EAPSKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYU, Courant InstituteSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYU, Courant InstituteGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYU, Courant InstituteJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia University \ No newline at end of file +Team on M²LInEShttps://m2lines.github.io/team/Recent content in Team on M²LInESHugo -- gohugo.ioen-usMon, 19 Nov 2018 10:47:58 +1000Laure Zannahttps://m2lines.github.io/team/laure-zanna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/laure-zanna/NYUJohanna Goldmanhttps://m2lines.github.io/team/johannagoldman/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/johannagoldman/NYUAlistair Adcrofthttps://m2lines.github.io/team/alistairadcroft/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alistairadcroft/Princeton University, Affiliate NOAA-GFDLCarlos Fernandez-Grandahttps://m2lines.github.io/team/carlosferndandezgranda/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/carlosferndandezgranda/NYUJoan Brunahttps://m2lines.github.io/team/joan-bruna/Mon, 19 Nov 2018 10:47:58 +1000https://m2lines.github.io/team/joan-bruna/NYURyan Abernatheyhttps://m2lines.github.io/team/ryanabernathy/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/ryanabernathy/EarthmoversBrandon Reichlhttps://m2lines.github.io/team/brandonreichl/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/brandonreichl/NOAA-GFDLFeiyu Luhttps://m2lines.github.io/team/feiyulu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/feiyulu/Princeton University, Affiliate NOAA-GFDLJudith Bernerhttps://m2lines.github.io/team/judithberner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/judithberner/NCARMarika Hollandhttps://m2lines.github.io/team/marikaholland/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marikaholland/NCARMitch Bushukhttps://m2lines.github.io/team/mitchbushuk/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/mitchbushuk/UCAR, Affiliate NOAA-GFDLPierre Gentinehttps://m2lines.github.io/team/pierregentine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pierregentine/Columbia UniversityPaul O'Gormanhttps://m2lines.github.io/team/paulogorman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/paulogorman/MITJulie Deshayeshttps://m2lines.github.io/team/juliedeshayes/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliedeshayes/CNRS-IPSLJulien Le Sommerhttps://m2lines.github.io/team/julienlesommer/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/julienlesommer/CNRS-IGEArthur Guillauminhttps://m2lines.github.io/team/arthurguillaumin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/arthurguillaumin/Queen Mary University of LondonJanni Yuvalhttps://m2lines.github.io/team/janniyuval/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/janniyuval/Google ResearchAlex Connollyhttps://m2lines.github.io/team/alexconnolly/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexconnolly/Columbia UniversityJulius Buseckehttps://m2lines.github.io/team/juliusbusecke/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliusbusecke/Columbia UniversityAakash Sanehttps://m2lines.github.io/team/aakashsane/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/aakashsane/Princeton University, Affiliate NOAA-GFDLLorenzo Zampierihttps://m2lines.github.io/team/lorenzozampieri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/lorenzozampieri/ECMWFCem Gultekinhttps://m2lines.github.io/team/cemgultekin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/cemgultekin/NYUTarun Vermahttps://m2lines.github.io/team/tarunverma/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/tarunverma/John Hopkins UniversityDhruv Balwadahttps://m2lines.github.io/team/dhruvbalwada/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dhruvbalwada/LDEO,Columbia UniversityCheng Zhanghttps://m2lines.github.io/team/chengzhang/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chengzhang/Princeton UniversityPavel Perezhoginhttps://m2lines.github.io/team/pavelperezhogin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/pavelperezhogin/NYUWill Chapmanhttps://m2lines.github.io/team/willchapman/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willchapman/NCARWill Gregoryhttps://m2lines.github.io/team/willgregory/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/willgregory/Princeton University, Affiliate NOAA-GFDLAbigail Bodnerhttps://m2lines.github.io/team/abigailbodner/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/abigailbodner/NYUAndrew Brettinhttps://m2lines.github.io/team/andrewbrettin/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/andrewbrettin/NYUKarl Otnesshttps://m2lines.github.io/team/karlotness/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/karlotness/NYUFabrizio Falascahttps://m2lines.github.io/team/fabriziofalasca/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/fabriziofalasca/NYUAdam Subelhttps://m2lines.github.io/team/adamsubel/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/adamsubel/NYUAlexis Bargehttps://m2lines.github.io/team/alexisbarge/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/alexisbarge/CNRS-IGEMaud Tissothttps://m2lines.github.io/team/maudtissot/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/maudtissot/CNRS-IPSLSara Shamekhhttps://m2lines.github.io/team/sarashamekh/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/sarashamekh/NYUChris Pedersenhttps://m2lines.github.io/team/chrispedersen/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/chrispedersen/NYUEtienne Meunierhttps://m2lines.github.io/team/etiennemeunier/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/etiennemeunier/CNRS-IPSLNora Loosehttps://m2lines.github.io/team/noraloose/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/noraloose/[C]WorthyDanni Duhttps://m2lines.github.io/team/dannidu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/dannidu/Princeton UniversityDavid Kammhttps://m2lines.github.io/team/davidkamm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/davidkamm/CNRS-IPSLGabriel Mouttapahttps://m2lines.github.io/team/gabrielmouttapa/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gabrielmouttapa/CNRS-IGEGreta Millerhttps://m2lines.github.io/team/gretamiller/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/gretamiller/University of Oxford/NCARJia-Rui (Ray) Shihttps://m2lines.github.io/team/jia-ruishi/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jia-ruishi/NYUMarcela Contrerashttps://m2lines.github.io/team/marcelacontreras/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/marcelacontreras/CNRS-IGEShuchang Liuhttps://m2lines.github.io/team/shuchangliu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/shuchangliu/MITKelsey Everardhttps://m2lines.github.io/team/kelseyeverard/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/kelseyeverard/NYUSurya Dheeshjithhttps://m2lines.github.io/team/suryadheeshjith/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/suryadheeshjith/NYUGriffin Mooershttps://m2lines.github.io/team/griffinmooers/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/griffinmooers/MITPrani Nallurihttps://m2lines.github.io/team/praninalluri/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/praninalluri/Columbia UniversityRenaud Falgahttps://m2lines.github.io/team/renaudfalga/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/renaudfalga/NYUJulia Simpsonhttps://m2lines.github.io/team/juliasimpson/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/juliasimpson/Columbia UniversityJiarong Wuhttps://m2lines.github.io/team/jiarongwu/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/jiarongwu/NYUYongquan Quhttps://m2lines.github.io/team/yongquanqu-/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/yongquanqu-/Columbia UniversityAntoine-Alexis Nasserhttps://m2lines.github.io/team/antoinenasser/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/antoinenasser/Princeton UniversityXavier Levinehttps://m2lines.github.io/team/xavierlevine/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/team/xavierlevine/Columbia University \ No newline at end of file diff --git a/team/jia-ruishi/index.html b/team/jia-ruishi/index.html index 68f26a8b..d2df7fa9 100644 --- a/team/jia-ruishi/index.html +++ b/team/jia-ruishi/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Jia-Rui (Ray) Shi

    Monday, Jan 1, 0001

    NYU Courant

    • This project is supported by Schmidt Sciences, LLC.

    Jia-Rui (Ray) Shi

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/joan-bruna/index.html b/team/joan-bruna/index.html index 14515359..29afc628 100644 --- a/team/joan-bruna/index.html +++ b/team/joan-bruna/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Joan Bruna

    Monday, Nov 19, 2018

    NYU, Courant Institute + Computer Science + Center for Data Science

    • This project is supported by Schmidt Sciences, LLC.

    Joan Bruna

    Monday, Nov 19, 2018

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/johannagoldman/index.html b/team/johannagoldman/index.html index 468601cb..e0f03d68 100644 --- a/team/johannagoldman/index.html +++ b/team/johannagoldman/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Johanna Goldman

    Monday, Nov 19, 2018

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Johanna Goldman

    Monday, Nov 19, 2018

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/judithberner/index.html b/team/judithberner/index.html index 1e809dff..d64e811a 100644 --- a/team/judithberner/index.html +++ b/team/judithberner/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Judith Berner

    Monday, Jan 1, 0001

    National Center for Atmospheric Research

    • This project is supported by Schmidt Sciences, LLC.

    Judith Berner

    Monday, Jan 1, 0001

    NCAR

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/julienlesommer/index.html b/team/julienlesommer/index.html index 99ab13d7..f9cd671b 100644 --- a/team/julienlesommer/index.html +++ b/team/julienlesommer/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Julien Le Sommer

    Monday, Jan 1, 0001

    L’Institut des Géosciences de l’Environnement (IGE)

    • This project is supported by Schmidt Sciences, LLC.

    Julien Le Sommer

    Monday, Jan 1, 0001

    CNRS-IGE

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/karlotness/index.html b/team/karlotness/index.html index bebdc65f..313b7079 100644 --- a/team/karlotness/index.html +++ b/team/karlotness/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Karl Otness

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Karl Otness

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/kelseyeverard/index.html b/team/kelseyeverard/index.html index c4f57c29..02abaf03 100644 --- a/team/kelseyeverard/index.html +++ b/team/kelseyeverard/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Kelsey Everard

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Kelsey Everard

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/laure-zanna/index.html b/team/laure-zanna/index.html index 5c95b524..c8d4e958 100644 --- a/team/laure-zanna/index.html +++ b/team/laure-zanna/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Laure Zanna

    Monday, Nov 19, 2018

    NYU, Courant Institute + Center for Data Science

    • This project is supported by Schmidt Sciences, LLC.

    Laure Zanna

    Monday, Nov 19, 2018

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/lorenzozampieri/index.html b/team/lorenzozampieri/index.html index 80b4ac62..50f32033 100644 --- a/team/lorenzozampieri/index.html +++ b/team/lorenzozampieri/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Lorenzo Zampieri

    Monday, Jan 1, 0001

    National Center for Atmospheric Research

    • This project is supported by Schmidt Sciences, LLC.

    Lorenzo Zampieri

    Monday, Jan 1, 0001

    ECMWF

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/marcelacontreras/index.html b/team/marcelacontreras/index.html index 5318855b..32c4e139 100644 --- a/team/marcelacontreras/index.html +++ b/team/marcelacontreras/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Marcela Contreras

    Monday, Jan 1, 0001

    IGE, Grenoble

    • This project is supported by Schmidt Sciences, LLC.

    Marcela Contreras

    Monday, Jan 1, 0001

    CNRS-IGE

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/marikaholland/index.html b/team/marikaholland/index.html index 647e0e1c..fc798a45 100644 --- a/team/marikaholland/index.html +++ b/team/marikaholland/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Marika Holland

    Monday, Jan 1, 0001

    National Center for Atmospheric Research

    • This project is supported by Schmidt Sciences, LLC.

    Marika Holland

    Monday, Jan 1, 0001

    NCAR

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/mitchbushuk/index.html b/team/mitchbushuk/index.html index a51b33ad..809987f8 100644 --- a/team/mitchbushuk/index.html +++ b/team/mitchbushuk/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Mitch Bushuk

    Monday, Jan 1, 0001

    UCAR, Affiliate NOAA-GFDL in Ocean division

    • This project is supported by Schmidt Sciences, LLC.

    Mitch Bushuk

    Monday, Jan 1, 0001

    UCAR, Affiliate NOAA-GFDL

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/noraloose/index.html b/team/noraloose/index.html index 64da1efc..dc965070 100644 --- a/team/noraloose/index.html +++ b/team/noraloose/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Nora Loose

    Monday, Jan 1, 0001

    Princeton University

    • This project is supported by Schmidt Sciences, LLC.

    Nora Loose

    Monday, Jan 1, 0001

    [C]Worthy

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/pavelperezhogin/index.html b/team/pavelperezhogin/index.html index 21ae41c3..25e477e6 100644 --- a/team/pavelperezhogin/index.html +++ b/team/pavelperezhogin/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Pavel Perezhogin

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Pavel Perezhogin

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/renaudfalga/index.html b/team/renaudfalga/index.html index f31e395a..2dfdc14d 100644 --- a/team/renaudfalga/index.html +++ b/team/renaudfalga/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Renaud Falga

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Renaud Falga

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/ryanabernathy/index.html b/team/ryanabernathy/index.html index ecfe6b71..1760c020 100644 --- a/team/ryanabernathy/index.html +++ b/team/ryanabernathy/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Ryan Abernathey

    Monday, Jan 1, 0001

    Lamont/Columbia University

    • This project is supported by Schmidt Sciences, LLC.

    Ryan Abernathey

    Monday, Jan 1, 0001

    Earthmovers

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/sarashamekh/index.html b/team/sarashamekh/index.html index a8309142..1f4864ff 100644 --- a/team/sarashamekh/index.html +++ b/team/sarashamekh/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Sara Shamekh

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Sara Shamekh

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/shuchangliu/index.html b/team/shuchangliu/index.html index e2612def..da335275 100644 --- a/team/shuchangliu/index.html +++ b/team/shuchangliu/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Shuchang Liu

    Monday, Jan 1, 0001

    MIT EAPS

    • This project is supported by Schmidt Sciences, LLC.

    Shuchang Liu

    Monday, Jan 1, 0001

    MIT

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/suryadheeshjith/index.html b/team/suryadheeshjith/index.html index 3d9500e4..842617ae 100644 --- a/team/suryadheeshjith/index.html +++ b/team/suryadheeshjith/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Surya Dheeshjith

    Monday, Jan 1, 0001

    NYU, Courant Institute

    • This project is supported by Schmidt Sciences, LLC.

    Surya Dheeshjith

    Monday, Jan 1, 0001

    NYU

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/tarunverma/index.html b/team/tarunverma/index.html index 1602e946..b639e222 100644 --- a/team/tarunverma/index.html +++ b/team/tarunverma/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Tarun Verma

    Monday, Jan 1, 0001

    Princeton University

    • This project is supported by Schmidt Sciences, LLC.

    Tarun Verma

    Monday, Jan 1, 0001

    John Hopkins University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/willchapman/index.html b/team/willchapman/index.html index b1baa2ae..a979d56d 100644 --- a/team/willchapman/index.html +++ b/team/willchapman/index.html @@ -8,6 +8,6 @@
  • Resources
  • News

    • Will Chapman

    Monday, Jan 1, 0001

    National Center for Atmospheric Research

    • This project is supported by Schmidt Sciences, LLC.

    Will Chapman

    Monday, Jan 1, 0001

    NCAR

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file diff --git a/team/aurorabasinki/index.html b/team/xavierlevine/index.html similarity index 82% rename from team/aurorabasinki/index.html rename to team/xavierlevine/index.html index d2aa63e7..52c24e5b 100644 --- a/team/aurorabasinki/index.html +++ b/team/xavierlevine/index.html @@ -1,4 +1,4 @@ -Aurora Basinski - M²LInES +Xavier Levine - M²LInES
    • This project is supported by Schmidt Sciences, LLC.

    Xavier Levine

    Monday, Jan 1, 0001

    Columbia University

    • This project is supported by Schmidt Sciences, LLC.
    \ No newline at end of file