README.md

meteoai

The papers or tutorials and relative source code of artificial intelligence for meteorology, ocean and environment science.

Classification, Detection, Cluster

• A cloud detection algorithm for satellite imagery based on deep learning : Code

• Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media : Code

• A daily global mesoscale ocean eddy dataset from satellite altimetry : Code

• Detecting Climate Change Effects on Vb Cyclones in a 50-Member Single-Model Ensemble Using Machine Learning : Zenodo

Satellite & Weather Radar

• Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network : Code

• RainNet: a convolutional neural network for radar-based precipitation nowcasting : Code

• Deep learning for precipitation nowcasting: A benchmark and a new model : Code

Downscaling

• Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling : Code

Weather and Climate Forecast

• Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques: The codes relevant to this paper are available upon request from the corresponding author.

• Predicting Weather Forecast Uncertainty with Machine Learning : Code

• Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting : Code

• STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting : Code

• WeatherBench: A benchmark dataset for data-driven weather forecasting : Code

• Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms : Code

• Deep learning for prediction of meteorological fronts: Keras tutorial : Code

• Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles : Code

• Improving Subseasonal Forecasting in the Western US with Machine Learning : Code

• Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground : Zenodo

• Improving Subseasonal Forecasting in the Western US with Machine Learning : Code

Extreme Events

• Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning : Code

• Using machine learning to predict extreme events in complex systems : Code

• The Application of Machine Learning Techniques to Improve El Niño Prediction Skill : Code

• ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events : Code

• Deep learning for multi-year ENSO forecasts : Code

Numerical Model

• Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems : Code

Parameterization

• Deep learning to represent sub-grid processes in climate models : Code

• Could machine learning break the convection parameterization deadlock? : Code

• A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks : Code

• Deep learning to represent subgrid processes in climate models : Zenodo, Gitlab

• Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model : Zenodo

Post-processing

• Neural networks for post-processing ensemble weather forecasts : Code

Data Assimilation

• Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations- a case study with the Lorenz 96 model : Code

Uncertainty Quantification

• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data : Code

• Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification : Code

• Adversarial uncertainty quantification in physics-informed neural networks : Code

Interpretable

• Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms : Code

• Interpretation of deep-learning models for predicting thunderstorm rotation: Python tutorial : Code

• Interpretable Machine Learning: A Guide for Making Black Box Models Explainable : Code

Partial Differential Equations

• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations : Code

• Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations : Code

• Learning data-driven discretizations for partial differential equations : Code