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https://github.com/HeQinWill/awesome-WeatherAI

A curated list of awesome resources related to using AI for weather forecasting and climate modeling.
https://github.com/HeQinWill/awesome-WeatherAI

List: awesome-WeatherAI

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A curated list of awesome resources related to using AI for weather forecasting and climate modeling.

Host: GitHub
URL: https://github.com/HeQinWill/awesome-WeatherAI
Owner: HeQinWill
Created: 2023-08-12T04:24:24.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-08-12T16:26:56.000Z (over 1 year ago)
Last Synced: 2024-04-10T14:08:08.023Z (9 months ago)
Size: 152 KB
Stars: 2
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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ultimate-awesome - awesome-WeatherAI - A curated list of awesome resources related to using AI for weather forecasting and climate modeling. (Other Lists / PowerShell Lists)

README

        # Awesome Weather AI [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



This repository contains a curated list of papers, datasets, models, surveys, and other resources related to using AI for weather forecasting and climate modeling.  

It includes key papers that have advanced the state-of-the-art in areas like global forecasting, precipitation prediction, and climate projection using deep learning techniques. Popular benchmark datasets and open source implementations of influential models are also listed. The brief description is summarized by GPT.  

The goal of this repo is to provide a overview of the field to help researchers quickly get up to speed on the current progress and opportunities in applying AI to weather and climate domains. Contributions are welcome!  

## Contents

- [Papers](#papers)

- [Datasets](#datasets)

- [Models](#models)

    - [ClimaX](#climax)

    - [FourCastNet](#fourcastnet)

    - [FuXi](#fuxi)

    - [GraphCast](#graphcast)

    - [Pangu](#pangu)

    - [pySTEPS](#pysteps)

- [Blog Posts & News](#blog-posts--news)

## Papers

- [Accurate medium-range global weather forecasting with 3D neural networks](https://www.nature.com/articles/s41586-023-06185-3) - Kaifeng Bi et al., 2023 👉[Pangu](#pangu)

  Used 3D Earth-specific transformer (3DEST) on a icosahedral mesh grid to generate medium-range global weather forecasts. Outperformed operational forecasts from ECMWF.

- [Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data](https://onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001705) - Jonathan A. Weyn et al., 2019

  Proposed a CNN model for weather forecasting basic atmospheric variables. Showed potential for ML in weather forecasting.

- [ClimaX: A foundation model for weather and climate](http://arxiv.org/abs/2301.10343) - Tung Nguyen et al., 2023 👉[ClimaX](#climax)

  Proposed a Transformer-based model for general weather and climate prediction tasks. Showed strong performance with pretraining.

- [Conditional Local Convolution for Spatio-temporal Meteorological Forecasting](http://arxiv.org/abs/2101.01000) - Haitao Lin et al., 2021

  Proposed a graph convolution model for meteorological forecasting that captures local spatial patterns.

- [Deep learning for twelve hour precipitation forecasts](https://www.nature.com/articles/s41467-022-32483-x) - Lasse Espeholt et al., 2022

  Showed a deep learning model that can effectively forecast precipitation and outperform operational models.

- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](http://arxiv.org/abs/2304.02948) - Kang Chen et al., 2023

  Proposed an ensemble ML model for global medium-range forecasting that extends skillful forecasts to 10+ days.

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](http://arxiv.org/abs/2202.11214) - Jaideep Pathak et al., 2022 👉[FourCastNet](#fourcastnet)

  Introduced a Fourier neural operator model for global high-resolution weather forecasting.

- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](http://arxiv.org/abs/2306.12873) - Lei Chen et al., 2023 👉[FuXi](#fuxi)

  Proposed a cascaded ML model that provides skillful 15-day global forecasts.

- [GraphCast: Learning skillful medium-range global weather forecasting](http://arxiv.org/abs/2212.12794) - Remi Lam et al., 2022 👉[GraphCast](#graphcast)

  Showed a GNN model that surpasses operational medium-range weather forecasts.

- [Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere](https://onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002109) - Jonathan A. Weyn et al., 2020

  Improved prior CNN weather forecasting model using cubed sphere mapping and other enhancements.

- [Skilful nowcasting of extreme precipitation with NowcastNet](https://www.nature.com/articles/s41586-023-06184-4) - Yuchen Zhang et al., 2023 👉[NowcastNet](#nowcastnet)

  Proposed NowcastNet model for extreme precipitation nowcasting that outperforms operational models.

- [Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models](https://onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002502) - Jonathan A. Weyn et al., 2021

  Showed a large DL model ensemble can provide skillful subseasonal forecasts.

- [SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation](http://arxiv.org/abs/2205.13158) - Yuan Hu et al., 2023

  Proposed a stochastic weather forecasting model with learned distribution perturbation for ensemble forecasts.

  

- [The Compatibility between the Pangu Weather Forecasting Model and Meteorological Operational Data](https://doi.org/10.48550/arXiv.2308.04460) - Wencong Cheng et al., 2023 👉[Pangu](#pangu)

  

  The Pangu-Weather model has been validated by the ECMWF and is compatible with various NWP systems, exhibiting stable forecasting capability and improved performance with better initial conditions.

## Datasets

### ✨Raw archives

#### ERA5

- [hourly data on pressure levels from 1940](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels)

- [hourly data on single levels from 1940](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels)

### ✨Benchmarks

- [WeatherBench](https://github.com/pangeo-data/WeatherBench)

  📒[paper](https://doi.org/10.1029/2020MS002203) 🔗[https](https://dataserv.ub.tum.de/index.php/s/m1524895) 🔗`ftp://m1524895:[email protected]`  

  A benchmark dataset derived from ERA5 for evaluating data-driven weather forecasting models.

- [WeatherBench 2](https://github.com/google-research/weatherbench2)

  📒[docs](https://weatherbench2.readthedocs.io/en/latest) 🔗`gs://weatherbench2/datasets`  

  Extended version of WeatherBench with more variables.

- [ClimateBench](https://github.com/duncanwp/ClimateBench)

  📒[paper](https://doi.org/10.1029/2021MS002954) 🔗[https](https://zenodo.org/record/7064308)  

  It consists of NorESM2 simulation outputs with associated forcing data processed in to a consistent format from a variety of experiments performed for CMIP6. Multiple ensemble members are included where available.  

## Models

### ✨Official implements

#### ClimaX 

- [ClimaX - Microsoft](https://github.com/microsoft/ClimaX)

- Universal Transformer model

#### FourCastNet

- [FourCastNet - NVIDIA](https://github.com/NVlabs/FourCastNet)

- Fourier neural operator model

#### FuXi

- [FuXi - Fudan](https://github.com/tpys/FuXi)

- Cascaded ML weather forecasting system

- 🔐restricted

#### GraphCast

- [GraphCast - DeepMind](https://github.com/deepmind/graphcast)

- GNN model

#### NowcastNet

- [NowcastNet - Tsinghua](https://doi.org/10.24433/CO.0832447.v1)

- Nonlinear nowcasting model for extreme precipitation

#### Pangu

- [Pangu - Huawei](https://github.com/198808xc/Pangu-Weather)

- Earth-specific transformer (3DEST)

#### pySTEPS

- [pySTEPS](https://github.com/pySTEPS/pysteps)

### ✨Other kits

- [ai-models - ECMWF](https://github.com/ecmwf-lab/ai-models)  

  👉[FourCastNet](#fourcastnet) 👉[Pangu](#pangu)

- [OpenCastKit - High-Flyer](https://github.com/HFAiLab/OpenCastKit)  

  👉[FourCastNet](#fourcastnet) 👉[GraphCast](#graphcast)

## Blog Posts & News

- [The rise of machine learning in weather forecasting](https://www.ecmwf.int/en/about/media-centre/science-blog/2023/rise-machine-learning-weather-forecasting) - ECMWF, 2023

- [The AI Forecaster: Machine Learning Takes On Weather Prediction](http://eos.org/research-spotlights/the-ai-forecaster-machine-learning-takes-on-weather-prediction) - Eos, 2022