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meteoai

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

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# 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](https://www.sciencedirect.com/science/article/pii/S0034425719301294) : [Code](https://github.com/JacobJeppesen/RS-Net)

- [Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023528) : [Code](https://github.com/cics-nd/dcedn-gcs)

- [A daily global mesoscale ocean eddy dataset from satellite altimetry](https://www.nature.com/articles/sdata201528) : [Code](https://github.com/jfaghm/OceanEddies)

- [Detecting Climate Change Effects on Vb Cyclones in a 50-Member Single-Model Ensemble Using Machine Learning](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL084969) : [Zenodo](https://zenodo.org/record/1495000)

## Satellite & Weather Radar

- [Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network](https://doi.org/10.31223/osf.io/w26ja) : [Code](https://github.com/jleinonen/cloudsat-gan)

- [RainNet: a convolutional neural network for radar-based precipitation nowcasting](https://www.geosci-model-dev-discuss.net/gmd-2020-30/gmd-2020-30.pdf) : [Code](https://github.com/hydrogo/rainnet)

- [Deep learning for precipitation nowcasting: A benchmark and a new model](https://papers.nips.cc/paper/7145-deep-learning-for-precipitation-nowcasting-a-benchmark-and-a-new-model.pdf) : [Code](https://github.com/sxjscience/HKO-7)

## Downscaling

- [Conﬁguration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling](https://www.geosci-model-dev-discuss.net/gmd-2019-278/) : [Code](https://github.com/SantanderMetGroup/DeepDownscaling)

## Weather and Climate Forecast

- [Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques](https://www.nature.com/articles/s41612-019-0098-0): The codes relevant to this paper are available upon request from the corresponding author.

- [Predicting Weather Forecast Uncertainty with Machine Learning](https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3410) : [Code](https://github.com/sipposip/Predicting-weather-forecast-uncertainty-with-machine-learning)

- [Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting](https://arxiv.org/abs/1812.09467) : [Code](https://github.com/BruceBinBoxing/Deep_Learning_Weather_Forecasting)

- [STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting](https://arxiv.org/abs/1912.00134) : [Code](https://github.com/MLRG-CEFET-RJ/stconvs2s)

- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://arxiv.org/abs/2002.00469) : [Code](https://github.com/pangeo-data/WeatherBench)

- [Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms](https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0316.1) : [Code](https://github.com/djgagne/deepsky)

- [Deep learning for prediction of meteorological fronts: Keras tutorial]() : [Code](https://github.com/thunderhoser/aiml_symposium)

- [Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles](https://journals.ametsoc.org/doi/full/10.1175/WAF-D-17-0010.1) : [Code](https://github.com/djgagne/hagelslag)

- [Improving Subseasonal Forecasting in the Western US with Machine Learning](https://arxiv.org/abs/1809.07394) : [Code](https://github.com/paulo-o/forecast_rodeo)

- [Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground](https://www.geosci-model-dev.net/12/2797/2019/) : [Zenodo](https://doi.org/10.5281/zenodo.2572863)

- [Improving Subseasonal Forecasting in the Western US with Machine Learning](https://arxiv.org/abs/1809.07394) : [Code](https://github.com/paulo-o/forecast_rodeo)

### Extreme Events

- [Analog Forecasting of Extreme-Causing Weather Patterns Using Deep Learning](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019MS001958) : [Code](https://github.com/ashesh6810/DLC_Extreme)

- [Using machine learning to predict extreme events in complex systems](https://www.pnas.org/content/117/1/52) : [Code](https://github.com/qidigit/CNN_tKdV)

- [The Application of Machine Learning Techniques to Improve El Niño Prediction Skill](https://www.frontiersin.org/articles/10.3389/fphy.2019.00153/full) : [Code](https://github.com/Ambrosys/climatelearn)

- [ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events](https://arxiv.org/abs/1612.02095) : [Code](https://github.com/eracah/hur-detect)

- [Deep learning for multi-year ENSO forecasts](https://www.nature.com/articles/s41586-019-1559-7) : [Code](https://doi.org/10.5281/zenodo.3244463)

## Numerical Model

- [Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems](https://arxiv.org/abs/1909.00912) : [Code](https://github.com/raspstephan/CBRAIN-CAM)

### Parameterization

- [Deep learning to represent sub-grid processes in climate models](https://www.pnas.org/content/115/39/9684) : [Code](https://github.com/raspstephan/CBRAIN-CAM)

- [Could machine learning break the convection parameterization deadlock?](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL078202) : [Code](https://github.com/raspstephan/CBRAIN-CAM)

- [A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks](https://arxiv.org/abs/1903.10274) : [Code](https://github.com/prl900/precip-encoder-decoders)

- [Deep learning to represent subgrid processes in climate models](https://www.pnas.org/content/115/39/9684) : [Zenodo](https://doi.org/10.5281/zenodo.1402384), [Gitlab](https://gitlab.com/mspritch/spcam3.0-neural-net/tree/nn_fbp_engy_ess)

- [Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019MS001896) : [Zenodo](http://doi.org/10.5281/zenodo.3663121)

### Post-processing

- [Neural networks for post-processing ensemble weather forecasts](https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0187.1) : [Code](https://github.com/slerch/ppnn)

## 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](https://arxiv.org/abs/2001.01520) : [Code](https://zenodo.org/record/2925547)

## Uncertainty Quantification

- [Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data](https://doi.org/10.1016/j.jcp.2019.05.024) : [Code](https://github.com/cics-nd/pde-surrogate)

- [Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantiﬁcation](https://arxiv.org/abs/1802.00850) : [Code](https://github.com/rohitkt10/deep-uq-paper)

- [Adversarial uncertainty quantification in physics-informed neural networks](https://doi.org/10.1016/j.jcp.2019.05.027) : [Code](https://github.com/PredictiveIntelligenceLab/UQPINNs)

## Interpretable

- [Interpretable Deep Learning for Spatial Analysis of Severe Hailstorms](https://journals.ametsoc.org/doi/full/10.1175/MWR-D-18-0316.1) : [Code](https://github.com/djgagne/deepsky)

- [Interpretation of deep-learning models for predicting thunderstorm rotation: Python tutorial]() : [Code](https://github.com/djgagne/ams-ml-python-course/blob/ryan_branch/module_4/ML_Short_Course_Module_4_Interpretation.ipynb)

- [Interpretable Machine Learning: A Guide for Making Black Box Models Explainable]() : [Code](https://christophm.github.io/interpretable-ml-book/)

## Partial Differential Equations

- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://arxiv.org/abs/1711.10561) : [Code](https://github.com/maziarraissi/PINNs)

- [Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations](https://arxiv.org/abs/1711.10566) : [Code](https://github.com/maziarraissi/PINNs)

- [Learning data-driven discretizations for partial differential equations](https://www.pnas.org/content/116/31/15344) : [Code](https://github.com/google/data-driven-discretization-1d)

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