diff --git a/about/index.html b/about/index.html index f4993f11..3a18fb8e 100644 --- a/about/index.html +++ b/about/index.html @@ -7,6 +7,6 @@
  • News

    • About Us

    M²LInES (pronounced M-square-lines) mission is to improve coupled climate models by reimagining physics model development through innovative use of data and AI. We aim to accelerate the pace of climate model development by learning physics from data with scientific machine learning, and ultimately enhance climate model fidelity and reliability for future projections.

    Our funder

    This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    Read more in our press release

    International team

    Our project comprises ocean, atmosphere, sea-ice scientists, numerical model developers, and machine learning experts. Our collaboration spans 2 continents, 7 academic institutions, and 3 numerical climate modeling centers.

    Machine Learning

    Our project leads the developement of machine learning models for climate physics, and ultimately of interpretable data-driven models to deepen our understanding of complex multiscale processes in the climate system.

    Innovation

    This innovative effort leverages the availability of big data from high-resolution simulations, as well as data assimilation products (combining models and observations), with powerful machine learning algorithms to improve the representation of subgrid physics in the ocean, sea-ice and atmosphere components of existing climate models.

    Code of conduct

    You will find our code of conduct here

    Code of Conduct
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    About Us

    M²LInES (pronounced M-square-lines) mission is to improve coupled climate models by reimagining physics model development through innovative use of data and AI. We aim to accelerate the pace of climate model development by learning physics from data with scientific machine learning, and ultimately enhance climate model fidelity and reliability for future projections.

    Our funder

    This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    Read more in our press release

    International team

    Our project comprises ocean, atmosphere, sea-ice scientists, numerical model developers, and machine learning experts. Our collaboration spans 2 continents, 7 academic institutions, and 3 numerical climate modeling centers.

    Machine Learning

    Our project leads the developement of machine learning models for climate physics, and ultimately of interpretable data-driven models to deepen our understanding of complex multiscale processes in the climate system.

    Innovation

    This innovative effort leverages the availability of big data from high-resolution simulations, as well as data assimilation products (combining models and observations), with powerful machine learning algorithms to improve the representation of subgrid physics in the ocean, sea-ice and atmosphere components of existing climate models.

    Code of conduct

    You will find our code of conduct here

    Code of Conduct
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/code/beucler0224/index.html b/code/beucler0224/index.html new file mode 100644 index 00000000..92bd0ee2 --- /dev/null +++ b/code/beucler0224/index.html @@ -0,0 +1,12 @@ +Climate-invariant machine learning - M²LInES
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/code/chapman1223/index.html b/code/chapman1223/index.html new file mode 100644 index 00000000..92bd0ee2 --- /dev/null +++ b/code/chapman1223/index.html @@ -0,0 +1,12 @@ +Climate-invariant machine learning - M²LInES
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/code/falasca0424/index.html b/code/falasca0424/index.html new file mode 100644 index 00000000..c1dcf57a --- /dev/null +++ b/code/falasca0424/index.html @@ -0,0 +1,12 @@ +Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields - M²LInES
    M²LInES homepage

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

    Monday, Jan 1, 0001
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/code/gpsat/index.html b/code/gpsat/index.html new file mode 100644 index 00000000..307c3565 --- /dev/null +++ b/code/gpsat/index.html @@ -0,0 +1,12 @@ +GPSat - M²LInES
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
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    M²LInES homepage

    Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations

    Monday, Jan 1, 0001
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
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    Code

    Public repository and data access

    Machine learning tutorials and tools

    Equation Discovery

    Code

    Geospatial ML prediction workflow

    Code

    Machine Learning tutorial for Lorenz 96

    Website Code

    Software packages

    GCM-Filters

    Website -Code

    XGCM

    Website +Code

    GPSat

    Website +Code

    XGCM

    Website Code

    Models and implementations of parameterizations

    Photo of Differentiable QG model in JAX

    Differentiable QG model in JAX

    Website -Code

    Differentiable QG model in pytorch

    Code

    Implementation of data-driven parameterizations in MOM6

    Code

    Implementation of parameterizations in pyqg

    Code

    Implementation of stochastic parameterization in MOM6

    Code

    Stacked shallow water model with stochastic subgrid momentum parameterization

    Code

    Ocean parameterizations

    Photo of Generative data-driven approaches for stochastic subgrid parameterizations

    Generative data-driven approaches for stochastic subgrid parameterizations

    Code
    Photo of Neural network parameterization for vertical mixing

    Neural network parameterization for vertical mixing

    Code

    Stochastic parameterization of subgrid momentum forcing

    Code -Model

    Atmosphere parameterizations

    Neural networks for parameterization of subgrid atmospheric processes

    Code +Code

    Differentiable QG model in pytorch

    Code

    Implementation of data-driven parameterizations in MOM6

    Code

    Implementation of parameterizations in pyqg

    Code

    Implementation of stochastic parameterization in MOM6

    Code

    Stacked shallow water model with stochastic subgrid momentum parameterization

    Code

    Ocean parameterizations

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

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

    Code
    Photo of Generative data-driven approaches for stochastic subgrid parameterizations

    Generative data-driven approaches for stochastic subgrid parameterizations

    Code
    Photo of Neural network parameterization for vertical mixing

    Neural network parameterization for vertical mixing

    Code

    Stochastic parameterization of subgrid momentum forcing

    Code +Model

    Atmosphere parameterizations

    Photo of Climate-invariant machine learning

    Climate-invariant machine learning

    Code
    Photo of Climate-invariant machine learning

    Climate-invariant machine learning

    Code

    Neural networks for parameterization of subgrid atmospheric processes

    Code Data

    Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere

    Code -Data

    Random forest to learn atmospheric parameterization

    Code

    Sea Ice parameterizations

    Photo of Deep learning of systematic sea ice model errors from data assimilation increments

    Deep learning of systematic sea ice model errors from data assimilation increments

    Code

    Datasets

    Benchmark

    Bias corrected ERA5 skin temperature over Arctic sea ice

    Data

    Pyqg parameterization benchmarks

    Website +Data

    Random forest to learn atmospheric parameterization

    Code

    Sea Ice parameterizations

    Photo of Deep learning of systematic sea ice model errors from data assimilation increments

    Deep learning of systematic sea ice model errors from data assimilation increments

    Code
    Photo of Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations

    Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations

    Code
    Photo of Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

    Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

    Code

    Datasets

    Benchmark

    Bias corrected ERA5 skin temperature over Arctic sea ice

    Data

    Pyqg parameterization benchmarks

    Website Code

    Usage example:

    import xarray as xr
 import fsspec
 # Datasets are hosted on globus as zarr files
diff --git a/code/index.xml b/code/index.xml
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-Code on M²LInEShttps://m2lines.github.io/code/Recent content in Code on M²LInESHugo -- gohugo.ioen-usBias corrected ERA5 skin temperature over Arctic sea icehttps://m2lines.github.io/code/corrected_era5/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/corrected_era5/CM2.6https://m2lines.github.io/code/cm26/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/cm26/Usage example:
-import xarray as xr import gcsfs fs = gcsfs.GCSFileSystem() fs.ls("gs://leap-persistent-ro/groundpepper/GFDL_cm2.6") ds = xr.open_dataset( 'gs://leap-persistent-ro/groundpepper/GFDL_cm2.6/ GFDL_CM2_6_CONTROL_DAILY_SURF.zarr', engine='zarr')Deep learning of systematic sea ice model errors from data assimilation incrementshttps://m2lines.github.io/code/icemodelerrors/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/icemodelerrors/Differentiable QG model in JAXhttps://m2lines.github.io/code/diffqg_jax/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_jax/Differentiable QG model in pytorchhttps://m2lines.github.io/code/diffqg_pytorch/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_pytorch/Equation Discoveryhttps://m2lines.github.io/code/equationdiscovery/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/equationdiscovery/GCM-Filtershttps://m2lines.github.io/code/gcm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gcm/Generative data-driven approaches for stochastic subgrid parameterizationshttps://m2lines.github.io/code/datadrivenparam/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/datadrivenparam/Geospatial ML prediction workflowhttps://m2lines.github.io/code/geotrainflow/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/geotrainflow/Implementation of data-driven parameterizations in MOM6https://m2lines.github.io/code/mom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/mom6/Implementation of parameterizations in pyqghttps://m2lines.github.io/code/parampyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parampyqg/Implementation of stochastic parameterization in MOM6https://m2lines.github.io/code/parammom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parammom6/Machine Learning tutorial for Lorenz 96https://m2lines.github.io/code/lorenz96/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/lorenz96/Neural network parameterization for vertical mixinghttps://m2lines.github.io/code/verticalmix/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/verticalmix/Neural networks for parameterization of subgrid atmospheric processeshttps://m2lines.github.io/code/subgridatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/subgridatmosphere/Neural-Network Parameterization of Subgrid Momentum Transport in the Atmospherehttps://m2lines.github.io/code/transportatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/transportatmosphere/Pyqg parameterization benchmarkshttps://m2lines.github.io/code/pyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/pyqg/Usage example:
+Code on M²LInEShttps://m2lines.github.io/code/Recent content in Code on M²LInESHugo -- gohugo.ioen-usBias corrected ERA5 skin temperature over Arctic sea icehttps://m2lines.github.io/code/corrected_era5/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/corrected_era5/Climate-invariant machine learninghttps://m2lines.github.io/code/beucler0224/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/beucler0224/Climate-invariant machine learninghttps://m2lines.github.io/code/chapman1223/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/chapman1223/CM2.6https://m2lines.github.io/code/cm26/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/cm26/Usage example:
+import xarray as xr import gcsfs fs = gcsfs.GCSFileSystem() fs.ls("gs://leap-persistent-ro/groundpepper/GFDL_cm2.6") ds = xr.open_dataset( 'gs://leap-persistent-ro/groundpepper/GFDL_cm2.6/ GFDL_CM2_6_CONTROL_DAILY_SURF.zarr', engine='zarr')Data-driven dimensionality reduction and causal inference for spatiotemporal climate fieldshttps://m2lines.github.io/code/falasca0424/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/falasca0424/Deep learning of systematic sea ice model errors from data assimilation incrementshttps://m2lines.github.io/code/icemodelerrors/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/icemodelerrors/Differentiable QG model in JAXhttps://m2lines.github.io/code/diffqg_jax/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_jax/Differentiable QG model in pytorchhttps://m2lines.github.io/code/diffqg_pytorch/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_pytorch/Equation Discoveryhttps://m2lines.github.io/code/equationdiscovery/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/equationdiscovery/GCM-Filtershttps://m2lines.github.io/code/gcm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gcm/Generative data-driven approaches for stochastic subgrid parameterizationshttps://m2lines.github.io/code/datadrivenparam/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/datadrivenparam/Geospatial ML prediction workflowhttps://m2lines.github.io/code/geotrainflow/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/geotrainflow/GPSathttps://m2lines.github.io/code/gpsat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gpsat/Implementation of data-driven parameterizations in MOM6https://m2lines.github.io/code/mom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/mom6/Implementation of parameterizations in pyqghttps://m2lines.github.io/code/parampyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parampyqg/Implementation of stochastic parameterization in MOM6https://m2lines.github.io/code/parammom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parammom6/Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulationshttps://m2lines.github.io/code/gregory0124/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gregory0124/Machine Learning tutorial for Lorenz 96https://m2lines.github.io/code/lorenz96/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/lorenz96/Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiChttps://m2lines.github.io/code/zampieri0424/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/zampieri0424/Neural network parameterization for vertical mixinghttps://m2lines.github.io/code/verticalmix/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/verticalmix/Neural networks for parameterization of subgrid atmospheric processeshttps://m2lines.github.io/code/subgridatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/subgridatmosphere/Neural-Network Parameterization of Subgrid Momentum Transport in the Atmospherehttps://m2lines.github.io/code/transportatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/transportatmosphere/Pyqg parameterization benchmarkshttps://m2lines.github.io/code/pyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/pyqg/Usage example:
 import xarray as xr import fsspec # Datasets are hosted on globus as zarr files def get_dataset( path, base_url="https://g-402b74.00888.8540.data.globus.org" ): mapper = fsspec.get_mapper(f"{base_url}/{path}.zarr") return xr.open_zarr(mapper, consolidated=True)Random forest to learn atmospheric parameterizationhttps://m2lines.github.io/code/randomforest/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/randomforest/Stacked shallow water model with stochastic subgrid momentum parameterizationhttps://m2lines.github.io/code/shallowwater/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/shallowwater/Stochastic parameterization of subgrid momentum forcinghttps://m2lines.github.io/code/momentumforcing/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/momentumforcing/XGCMhttps://m2lines.github.io/code/xgcm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/xgcm/
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+Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC - M²LInES
    M²LInES homepage
    Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC
    Monday, Jan 1, 0001
    • This project is supported by Schmidt Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    
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  • 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

    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 Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.

    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 Futures, a philanthropic initiative founded by Eric and Wendy Schmidt that bets early on exceptional people making the world better, particularly through innovative breakthroughs in science and technology.
    \ No newline at end of file diff --git a/index.xml b/index.xml index 543e7604..58ef0036 100644 --- a/index.xml +++ b/index.xml @@ -16,6 +16,6 @@ Read the book chapter hereClimate-Invariant Ma In particular several M²LInES team members will be presenting on the following dates: Paul O’Gorman - Nov 3rd Laure Zanna - Nov 10th. Pierre Gentine - Nov 17th</description></item><item><title>Adaptive Denoising via GainTuning - Carlos Fernandez-Grandahttps://m2lines.github.io/news/fernandezgranda/Thu, 29 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/fernandezgranda/Carlos Fernandez-Granda and his team introduces a new technique for adaptive denoising of Deep convolutional neural networks. Available to read here as a preprint.Impact of Sea-Ice Model Complexity on model performance by Lorenzo Zampierihttps://m2lines.github.io/news/zampierijames/Thu, 29 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/zampierijames/Incoming postdoc Lorenzo Zampieri demonstrates in this article how increased sea-ice model complexity can improve the simulated sea-ice concentration and snow thickness. Available to read in open access here .Article by Dhruv Balwada et al. - Role of submesoscale flows on vertical fluxeshttps://m2lines.github.io/news/balwada-article/Wed, 28 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/balwada-article/Dhruv Balwada is the lead author in this article analyzing the role of submesoscale flows on tracer fluxes. You can read it here (or in earthArXiv here ). Ryan Abernathey also contributed to the research.Estimating Ocean Surface Currents With Machine Learning - Ryan Abernatheyhttps://m2lines.github.io/news/abernatheypaper/Tue, 27 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/abernatheypaper/Ryan Abernathey is the senior author in this publication where deep learning models are trained to effectively “learn” spatial gradients and the physics of surface currents. You can read it here .Seasonal Prediction and Predictability of Regional Antarctic Sea Ice - Mitch Bushukhttps://m2lines.github.io/news/bushukarticle/Tue, 27 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/bushukarticle/In this article, lead author Mitch Bushuk uses the forecast system that his team will be using for M²LInES. You can read it here (or in Researchgate here ).Neural Networks as Geometric Chaotic Maps - by Ziwei Lihttps://m2lines.github.io/news/ziweiliarticle/Mon, 26 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/ziweiliarticle/M²LInES incoming postdoc Ziwei Li is the lead author on a new article on Neural Networks as Geometric Chaotic Maps. You can read it here (or in arXiv here ).Neural network parametrization of a subgrid process - by Janni Yuval and Paul O'Gormanhttps://m2lines.github.io/news/articleyuval/Mon, 19 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/articleyuval/A new M²LInES scientific paper on Neural-network parameterization of subgrid momentum transport in the atmosphere, by members Janni Yuval and Paul O&rsquo;Gorman, is available to read as a preprint here . Article in Eos by Yuval et al. - Call for Papers on Machine Learning and Earth System Modelinghttps://m2lines.github.io/news/articlejamescall/Wed, 14 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/articlejamescall/M²LInES members Janni Yuval is the lead author in this article inviting scientists to contribute to a new special collection in Journal of Advances in Modeling Earth Systems (JAMES). The collection will focus on the use of new machine learning methodologies and applications of machine learning to Earth system modeling. Pierre Gentine and Laure Zanna are co-authors of the article. Link to contribute to the special collection in JAMES here Upcoming Seminar by Laure Zanna at One World Mathematics of Climate - 7/6https://m2lines.github.io/news/oneworld/Fri, 02 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/oneworld/M²LInES lead PI Laure Zanna will give a seminar at the One World Mathematics of Climate on July 6th 12pm EST. She will talk about some of the machine learning work on ocean eddy parameterization and about the work being done at M²LInES. Tune in here A Bayesian approach towards sea ice freeboard estimates - paper by Will Gregoryhttps://m2lines.github.io/news/willgregorypaper/Thu, 01 Jul 2021 09:29:16 +1000https://m2lines.github.io/news/willgregorypaper/Incoming M²LInES postdoc Will Gregory is the lead author in this new publication on using a Bayesian approach towards sea ice freeboard estimates from satellite observations. -This work has 3 important implications: 1) a daily freeboard product allows them, for the first time, to assess radar freeboard (and ultimately sea ice thickness) variability on sub-weekly timescales &ndash; it takes up to 1 month for each satellite to generate pan-Arctic coverage alone. s2s challengehttps://m2lines.github.io/news/s2schallenge/Tue, 29 Jun 2021 10:29:16 +1000https://m2lines.github.io/news/s2schallenge/M²LInES PI Judith Berner is setting up a team to participate to the s2s challenge, which goal is to improve sub-seasonal to seasonal predictions using AI. M²LInES postdocs Janni Yuval and Tarun Verma will also be part of the team. The deadline to register is July 1st 2021. More info here KITP conference: Machine Learning for Climatehttps://m2lines.github.io/news/kitpconference/Mon, 07 Jun 2021 10:29:16 +1000https://m2lines.github.io/news/kitpconference/M²LInES Lead PI Laure Zanna is the co-director of the next conference on Machine Learning for Climate of the Kavli Institute for Theoretical Physics (KITP) . It will take place Nov 1-4 2021. Info and registration here 2021 CESM workshop: M²LInES team members presenting on June 17thhttps://m2lines.github.io/news/cesmworkshop/Mon, 07 Jun 2021 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop/M²LInES team members will present at Machine Learning session for the CESM workshop on June 17. Info and agenda here Call for Papers for JAMEShttps://m2lines.github.io/news/5jamescall/Tue, 01 Jun 2021 09:29:16 +1000https://m2lines.github.io/news/5jamescall/M²LInES members Janni Yuval, Pierre Gentine and Laure Zanna are part of the organizers of a special collection at JAMES (Journal of Advances in Modeling Earth Systems) that will include both new ML methodologies developed for advancing Earth system science and ML applications to Earth system modeling. Papers submissions are open. The community is invited to contribute. More information hereUsing deep learning to emulate cloud superparameterization - new paper by PI Gentinehttps://m2lines.github.io/news/3gentinepaper1/Fri, 23 Apr 2021 09:29:16 +1000https://m2lines.github.io/news/3gentinepaper1/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 Neural Network emulating physical constraints - paper by PI Gentinehttps://m2lines.github.io/news/4gentinepaper2/Sun, 21 Mar 2021 09:29:16 +1000https://m2lines.github.io/news/4gentinepaper2/M²LInES PI Pierre Gentine is the senior author and Research Scientist Tom Beucler is the lead author in this new publication on enforcing analytic constraints in Neural Networks emulating physical systems. Read their paper here New paper on deep learning parametrization by Guillaumin and Zannahttps://m2lines.github.io/news/1guillauminpaper/Mon, 08 Mar 2021 09:29:16 +1000https://m2lines.github.io/news/1guillauminpaper/In this paper, M²LInES postdoc Arthur Guillaumin and lead PI Laure Zanna developed a stochastic deep learning parametrization that is trained on high-resolution data from CM2.6, a coupled climate model. Read their paper here Modelisation using neural network - paper by PI Le Sommerhttps://m2lines.github.io/news/2jlesommerpaper/Mon, 22 Feb 2021 09:29:16 +1000https://m2lines.github.io/news/2jlesommerpaper/M²LInES PI Julien Le Sommer is a co-author in this new publication using neural network to model subgrid-scale processes. Read their paper hereAbouthttps://m2lines.github.io/about/Thu, 06 Dec 2018 09:29:16 +1000https://m2lines.github.io/about/<link>https://m2lines.github.io/services/axe1/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe1/</guid><description>Deepening our understanding of key climate processes, such as ocean mixing, clouds, convection, using data</description></item><item><title/><link>https://m2lines.github.io/services/axe2/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe2/</guid><description>Developing new physics-aware multiscale machine learning tools for accelerating climate science discovery</description></item><item><title/><link>https://m2lines.github.io/services/axe3/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe3/</guid><description>Improving coupled climate models from leading modeling centers (NCAR, GFDL, and IPSL).</description></item><item><title>Bias corrected ERA5 skin temperature over Arctic sea icehttps://m2lines.github.io/code/corrected_era5/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/corrected_era5/CM2.6https://m2lines.github.io/code/cm26/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/cm26/Usage example: -import xarray as xr import gcsfs fs = gcsfs.GCSFileSystem() fs.ls(&quot;gs://leap-persistent-ro/groundpepper/GFDL_cm2.6&quot;) ds = xr.open_dataset( 'gs://leap-persistent-ro/groundpepper/GFDL_cm2.6/ GFDL_CM2_6_CONTROL_DAILY_SURF.zarr', engine='zarr')Deep learning of systematic sea ice model errors from data assimilation incrementshttps://m2lines.github.io/code/icemodelerrors/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/icemodelerrors/Differentiable QG model in JAXhttps://m2lines.github.io/code/diffqg_jax/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_jax/Differentiable QG model in pytorchhttps://m2lines.github.io/code/diffqg_pytorch/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_pytorch/Equation Discoveryhttps://m2lines.github.io/code/equationdiscovery/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/equationdiscovery/GCM-Filtershttps://m2lines.github.io/code/gcm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gcm/Generative data-driven approaches for stochastic subgrid parameterizationshttps://m2lines.github.io/code/datadrivenparam/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/datadrivenparam/Geospatial ML prediction workflowhttps://m2lines.github.io/code/geotrainflow/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/geotrainflow/Implementation of data-driven parameterizations in MOM6https://m2lines.github.io/code/mom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/mom6/Implementation of parameterizations in pyqghttps://m2lines.github.io/code/parampyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parampyqg/Implementation of stochastic parameterization in MOM6https://m2lines.github.io/code/parammom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parammom6/Machine Learning tutorial for Lorenz 96https://m2lines.github.io/code/lorenz96/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/lorenz96/Neural network parameterization for vertical mixinghttps://m2lines.github.io/code/verticalmix/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/verticalmix/Neural networks for parameterization of subgrid atmospheric processeshttps://m2lines.github.io/code/subgridatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/subgridatmosphere/Neural-Network Parameterization of Subgrid Momentum Transport in the Atmospherehttps://m2lines.github.io/code/transportatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/transportatmosphere/Pyqg parameterization benchmarkshttps://m2lines.github.io/code/pyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/pyqg/Usage example: +This work has 3 important implications: 1) a daily freeboard product allows them, for the first time, to assess radar freeboard (and ultimately sea ice thickness) variability on sub-weekly timescales &ndash; it takes up to 1 month for each satellite to generate pan-Arctic coverage alone. s2s challengehttps://m2lines.github.io/news/s2schallenge/Tue, 29 Jun 2021 10:29:16 +1000https://m2lines.github.io/news/s2schallenge/M²LInES PI Judith Berner is setting up a team to participate to the s2s challenge, which goal is to improve sub-seasonal to seasonal predictions using AI. M²LInES postdocs Janni Yuval and Tarun Verma will also be part of the team. The deadline to register is July 1st 2021. More info here KITP conference: Machine Learning for Climatehttps://m2lines.github.io/news/kitpconference/Mon, 07 Jun 2021 10:29:16 +1000https://m2lines.github.io/news/kitpconference/M²LInES Lead PI Laure Zanna is the co-director of the next conference on Machine Learning for Climate of the Kavli Institute for Theoretical Physics (KITP) . It will take place Nov 1-4 2021. Info and registration here 2021 CESM workshop: M²LInES team members presenting on June 17thhttps://m2lines.github.io/news/cesmworkshop/Mon, 07 Jun 2021 09:29:16 +1000https://m2lines.github.io/news/cesmworkshop/M²LInES team members will present at Machine Learning session for the CESM workshop on June 17. Info and agenda here Call for Papers for JAMEShttps://m2lines.github.io/news/5jamescall/Tue, 01 Jun 2021 09:29:16 +1000https://m2lines.github.io/news/5jamescall/M²LInES members Janni Yuval, Pierre Gentine and Laure Zanna are part of the organizers of a special collection at JAMES (Journal of Advances in Modeling Earth Systems) that will include both new ML methodologies developed for advancing Earth system science and ML applications to Earth system modeling. Papers submissions are open. The community is invited to contribute. More information hereUsing deep learning to emulate cloud superparameterization - new paper by PI Gentinehttps://m2lines.github.io/news/3gentinepaper1/Fri, 23 Apr 2021 09:29:16 +1000https://m2lines.github.io/news/3gentinepaper1/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 Neural Network emulating physical constraints - paper by PI Gentinehttps://m2lines.github.io/news/4gentinepaper2/Sun, 21 Mar 2021 09:29:16 +1000https://m2lines.github.io/news/4gentinepaper2/M²LInES PI Pierre Gentine is the senior author and Research Scientist Tom Beucler is the lead author in this new publication on enforcing analytic constraints in Neural Networks emulating physical systems. Read their paper here New paper on deep learning parametrization by Guillaumin and Zannahttps://m2lines.github.io/news/1guillauminpaper/Mon, 08 Mar 2021 09:29:16 +1000https://m2lines.github.io/news/1guillauminpaper/In this paper, M²LInES postdoc Arthur Guillaumin and lead PI Laure Zanna developed a stochastic deep learning parametrization that is trained on high-resolution data from CM2.6, a coupled climate model. Read their paper here Modelisation using neural network - paper by PI Le Sommerhttps://m2lines.github.io/news/2jlesommerpaper/Mon, 22 Feb 2021 09:29:16 +1000https://m2lines.github.io/news/2jlesommerpaper/M²LInES PI Julien Le Sommer is a co-author in this new publication using neural network to model subgrid-scale processes. Read their paper hereAbouthttps://m2lines.github.io/about/Thu, 06 Dec 2018 09:29:16 +1000https://m2lines.github.io/about/<link>https://m2lines.github.io/services/axe1/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe1/</guid><description>Deepening our understanding of key climate processes, such as ocean mixing, clouds, convection, using data</description></item><item><title/><link>https://m2lines.github.io/services/axe2/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe2/</guid><description>Developing new physics-aware multiscale machine learning tools for accelerating climate science discovery</description></item><item><title/><link>https://m2lines.github.io/services/axe3/</link><pubDate>Wed, 28 Nov 2018 15:15:34 +1000</pubDate><guid>https://m2lines.github.io/services/axe3/</guid><description>Improving coupled climate models from leading modeling centers (NCAR, GFDL, and IPSL).</description></item><item><title>Bias corrected ERA5 skin temperature over Arctic sea icehttps://m2lines.github.io/code/corrected_era5/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/corrected_era5/Climate-invariant machine learninghttps://m2lines.github.io/code/beucler0224/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/beucler0224/Climate-invariant machine learninghttps://m2lines.github.io/code/chapman1223/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/chapman1223/CM2.6https://m2lines.github.io/code/cm26/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/cm26/Usage example: +import xarray as xr import gcsfs fs = gcsfs.GCSFileSystem() fs.ls(&quot;gs://leap-persistent-ro/groundpepper/GFDL_cm2.6&quot;) ds = xr.open_dataset( 'gs://leap-persistent-ro/groundpepper/GFDL_cm2.6/ GFDL_CM2_6_CONTROL_DAILY_SURF.zarr', engine='zarr')Data-driven dimensionality reduction and causal inference for spatiotemporal climate fieldshttps://m2lines.github.io/code/falasca0424/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/falasca0424/Deep learning of systematic sea ice model errors from data assimilation incrementshttps://m2lines.github.io/code/icemodelerrors/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/icemodelerrors/Differentiable QG model in JAXhttps://m2lines.github.io/code/diffqg_jax/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_jax/Differentiable QG model in pytorchhttps://m2lines.github.io/code/diffqg_pytorch/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/diffqg_pytorch/Equation Discoveryhttps://m2lines.github.io/code/equationdiscovery/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/equationdiscovery/GCM-Filtershttps://m2lines.github.io/code/gcm/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gcm/Generative data-driven approaches for stochastic subgrid parameterizationshttps://m2lines.github.io/code/datadrivenparam/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/datadrivenparam/Geospatial ML prediction workflowhttps://m2lines.github.io/code/geotrainflow/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/geotrainflow/GPSathttps://m2lines.github.io/code/gpsat/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gpsat/Implementation of data-driven parameterizations in MOM6https://m2lines.github.io/code/mom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/mom6/Implementation of parameterizations in pyqghttps://m2lines.github.io/code/parampyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parampyqg/Implementation of stochastic parameterization in MOM6https://m2lines.github.io/code/parammom6/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/parammom6/Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulationshttps://m2lines.github.io/code/gregory0124/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/gregory0124/Machine Learning tutorial for Lorenz 96https://m2lines.github.io/code/lorenz96/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/lorenz96/Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiChttps://m2lines.github.io/code/zampieri0424/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/zampieri0424/Neural network parameterization for vertical mixinghttps://m2lines.github.io/code/verticalmix/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/verticalmix/Neural networks for parameterization of subgrid atmospheric processeshttps://m2lines.github.io/code/subgridatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/subgridatmosphere/Neural-Network Parameterization of Subgrid Momentum Transport in the Atmospherehttps://m2lines.github.io/code/transportatmosphere/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/transportatmosphere/Pyqg parameterization benchmarkshttps://m2lines.github.io/code/pyqg/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/pyqg/Usage example: import xarray as xr import fsspec # Datasets are hosted on globus as zarr files def get_dataset( path, base_url=&quot;https://g-402b74.00888.8540.data.globus.org&quot; ): mapper = fsspec.get_mapper(f&quot;{base_url}/{path}.zarr&quot;) return xr.open_zarr(mapper, consolidated=True)Random forest to learn atmospheric parameterizationhttps://m2lines.github.io/code/randomforest/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/randomforest/Stacked shallow water model with stochastic subgrid momentum parameterizationhttps://m2lines.github.io/code/shallowwater/Mon, 01 Jan 0001 00:00:00 +0000https://m2lines.github.io/code/shallowwater/Stochastic parameterization of subgrid momentum forcinghttps://m2lines.github.io/code/momentumforcing/Mon, 01 Jan 0001 00:00:00 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