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🌍 Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)

🧭 Guideline

A collection of articles on Large Weather Models (LWMs), to make it easier to find and learn. 👏 Contributions to this hub are welcome!

• 🌍 Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
  • 🧭 Guideline
  • 🆕 LWMs News
  • 🗂️ LWMs Lists
  • 🗃️ Dataset Lists
  • 📖 Papers
    • WeatherBench
    • MetNet
    • FourCastNet
    • Pangu-Weather
    • GraphCast
    • ClimaX
    • FengWu
    • FuXi
    • AI-GOMS
    • XiHe
    • FNO
    • Nowcast
    • Physics-AI
    • Datasets
    • More
  • 🚀 Code

🆕 LWMs News

• 2024/09/20: IBM and Nasa Prithvi-WxC Foundation model [link]
• 2024/08/15: MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains [link];
• 2024/07/30: FuXi-S2S published in Nature Communications [link];
• 2024/06/20: WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [link];
• 2024/05/24: ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [link];
• 2024/05/22: Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [link];
• 2024/05/20: Aurora: A Foundation Model of the Atmosphere [link];
• 2024/05/09: FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [link];
• 2024/05/06: CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [link];
• 2024/04/15: ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [link];
• 2024/04/12: FuXi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [link];
• 2024/03/29: SEEDS: Generative emulation of weather forecast ensembles with diffusion models [link];
• 2024/03/13: KARINA: An Efficient Deep Learning Model for Global Weather Forecast [link];
• 2024/02/06: CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [link];
• 2024/02/04: XiHe, the first data-driven 1/12° resolution global ocean eddy-resolving forecasting model [link];
• 2024/02/02: ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [link];

• 2024/01/28: FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09∘ horizontal resolution [link];
• 2023/12/27: GenCast, a ML-based generative model for ensemble weather forecasting [link];
• 2023/12/16: Four-Dimensional Variational (4DVar) assimilation, and develop an AI-based cyclic weather forecasting system, FengWu-4DVar [link];
• 2023/12/15: FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [link];
• 2023/12/11: A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion DiffCast [link];
• 2023/11/13: GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. [link];
• 2023/12/13: FuXi is open source [link];
• 2023/11/14: GraphCast published in Science [link];
• 2023/10/25: IBM and Nasa Prithvi-100M Model [link];
• 2023/09/14: Pangu-Weather published in Nature [link];
• 2023/08/25: ClimaX published in ICML 2023 [link];

🗂️ LWMs Lists

🗃️ Dataset Lists

📖 Papers

WeatherBench

• WeatherBench: A benchmark dataset for data-driven weather forecasting [pdf]
• WeatherBench 2: A benchmark for the next generation of data-driven global weather models [pdf]

MetNet

• MetNet: A Neural Weather Model for Precipitation Forecasting (MetNet) [pdf]
• Deep learning for twelve hour precipitation forecasts (MetNet-2) [pdf]
• Deep Learning for Day Forecasts from Sparse Observations (MetNet-3) [pdf]

FourCastNet

• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators (FourCastNet) [pdf]

Pangu-Weather

• Accurate medium-range global weather forecasting with 3D neural networks (Pangu-Weather) [pdf]

GraphCast

• Learning skillful medium-range global weather forecasting (GraphCast) [pdf]

ClimaX

• ClimaX: A foundation model for weather and climate (ClimaX) [pdf]

FengWu

• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead (FengWu) [pdf]
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [pdf]
• Towards an end-to-end artificial intelligence driven global weather forecasting system [pdf]
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [pdf]
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [pdf]

FuXi

• FuXi: A cascade machine learning forecasting system for 15-day global weather forecast (FuXi) [pdf]
• FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model (FuXi-Extreme) [pdf]
• FuXi-S2S: An accurate machine learning model for global subseasonal forecasts [pdf]
• Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations [pdf]
• FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting [pdf]

AI-GOMS

• AI-GOMS: Large AI-Driven Global Ocean Modeling System (AI-GOMS) [pdf]

XiHe

• XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting [pdf]

FNO

• Fourier Neural Operator with Learned Deformations for PDEs on General Geometries [pdf]
• SFNO: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere [pdf]

Nowcast

• Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [pdf]
• PreDiff: Precipitation Nowcasting with Latent Diffusion Models [pdf]
• DGMR: Skilful precipitation nowcasting using deep generative models of radar [odf]
• Skilful nowcasting of extreme precipitation with NowcastNet (NowcastNet) [pdf]
• DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting [pdf]
• CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [pdf]
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [pdf]

Physics-AI

• Neural General Circulation Models for Weather and Climate [pdf]
• ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [pdf]
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [pdf]

Datasets

• WeatherBench: A benchmark dataset for data-driven weather forecasting [pdf]
• The ERA5 global reanalysis [pdf]
• SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology [pdf]
• WeatherBench 2: A benchmark for the next generation of data-driven global weather models [pdf]
• CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer [pdf]
• WEATHER-5K: A Large-scale Global Station Weather Dataset Towards Comprehensive Time-series Forecasting Benchmark [pdf]

More

• Can deep learning beat numerical weather prediction? [pdf]
• AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning [pdf]
• Anthropogenic fingerprints in daily precipitation revealed by deep learning [pdf]
• GenCast: Diffusion-based ensemble forecasting for medium-range weather [pdf]
• KARINA: An Efficient Deep Learning Model for Global Weather Forecast [pdf]
• SEEDS: Generative emulation of weather forecast ensembles with diffusion models [pdf]
• Aurora: A Foundation Model of the Atmosphere [pdf]
• ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions [pdf]

🚀 Code

• ECMWF AI Models: AI-based weather forecasting models.
• Skyrim: AI weather models united.
• NVIDIA Earth2Mip: Earth-2 Model Intercomparison Project (MIP) is a python framework that enables climate researchers and scientists to inter-compare AI models for weather and climate.
• AI Models for All: Run AI NWP forecasts hassle-free, serverless in the cloud!
• OpenEarthLab: OpenEarthLab, aiming at developing cutting-edge Spatiaotemporal Generation algorithms and promoting the development of Earth Science.

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🎉 Contributors