• 📎 Papers&Codes
• 📌 Workshop
• 💻 Library
• 💽 Dataset
• 🌏 Others

If I missed any of your work or if there's a need for an update in this review, please email me or just pull a request here. Thank you!

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

• intro: NIPS (2015)
• paper: https://arxiv.org/abs/1506.04214

Rainfall Prediction: A Deep Learning Approach

• intro: International Conference on Hybrid Artificial Intelligence Systems (2016)
• paper: https://link.springer.com/chapter/10.1007/978-3-319-32034-2_13

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

• intro: NIPS (2017)
• paper: https://arxiv.org/abs/1706.03458
• github: https://github.com/sxjscience/HKO-7

A short-term rainfall prediction model using multi-task convolutional neural networks

• intro: IEEE international conference on data mining (2017)
• paper: https://ieeexplore.ieee.org/abstract/document/8215512

All convolutional neural networks for radar-based precipitation nowcasting

• intro: Procedia Computer Science (2019)
• paper: https://www.sciencedirect.com/science/article/pii/S1877050919303801

Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

• intro: Geoscientific Model Development (2019)
• paper: https://gmd.copernicus.org/articles/12/1387/2019/
• github: https://github.com/hydrogo/rainymotion

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

• intro: Geoscientific Model Development (2019)
• paper: https://gmd.copernicus.org/articles/12/4185/2019/
• github: https://github.com/pySTEPS/pysteps

Machine Learning for Precipitation Nowcasting from Radar Images

• intro: arXiv (2019)
• paper: https://arxiv.org/abs/1912.12132
• blog: https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html

A review of radar-based nowcasting of precipitation and applicable machine learning techniques

• intro: arXiv (2020)
• paper: https://arxiv.org/abs/2005.04988

RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

• intro: Geoscientific Model Development (2020)
• paper: https://gmd.copernicus.org/articles/13/2631/2020/gmd-13-2631-2020-discussion.html
• github: https://github.com/hydrogo/rainnet

MetNet: A Neural Weather Model for Precipitation Forecasting

• intro: arXiv (2020)
• paper: https://arxiv.org/abs/2003.12140
• github: https://github.com/openclimatefix/metnet

Skilful precipitation nowcasting using deep generative models of radar

• intro: Nature (2021)
• paper: https://www.nature.com/articles/s41586-021-03854-z
• github: https://github.com/deepmind/deepmind-research/tree/master/nowcasting, https://github.com/openclimatefix/skillful_nowcasting

(1) Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks

(2) Deep learning for twelve hour precipitation forecasts

• intro: (1) arXiv (2021), (2) Nature communications (2022)
• paper: (1) https://arxiv.org/abs/2111.07470, (2) https://www.nature.com/articles/s41467-022-32483-x
• blog: https://ai.googleblog.com/2021/11/metnet-2-deep-learning-for-12-hour.html

Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation

• intro: Computers & Geosciences (2022)
• paper: https://www.sciencedirect.com/science/article/pii/S009830042200036X
• github: https://github.com/jihoonko/DeepRaNE

Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data

• intro: arXiv (2022)
• paper: https://arxiv.org/abs/2210.12853

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

• intro: NIPS (2022)
• paper: https://proceedings.neurips.cc/paper_files/paper/2022/hash/a2affd71d15e8fedffe18d0219f4837a-Abstract-Conference.html
• github: https://github.com/amazon-science/earth-forecasting-transformer

Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

• intro: GIScience & Remote Sensing (2023)
• paper: https://www.tandfonline.com/doi/pdf/10.1080/15481603.2023.2203363

MM-RNN: A Multimodal RNN for Precipitation Nowcasting

• intro: IEEE Transactions on Geoscience and Remote Sensing (2023)
• paper: https://ieeexplore.ieee.org/abstract/document/10092888

ClimaX: A foundation model for weather and climate

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2301.10343
• github: https://github.com/microsoft/ClimaX
• blog: https://www.microsoft.com/en-us/research/group/autonomous-systems-group-robotics/articles/introducing-climax-the-first-foundation-model-for-weather-and-climate/

Skilful nowcasting of extreme precipitation with NowcastNet

• intro: Nature (2023)
• paper: https://www.nature.com/articles/s41586-023-06184-4

Deep Learning Model based on Multi-scale Feature Fusion for Precipitation Nowcasting

• intro: Geoscientific Model Development Discussions (2023)
• paper: https://doi.org/10.5194/gmd-2023-109

Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2304.12891
• github: https://github.com/MeteoSwiss/ldcast

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

• intro: NIPS(2023)
• paper: https://openreview.net/pdf?id=Gh67ZZ6zkS

Physical-Dynamic-Driven AI-Synthetic Precipitation Nowcasting Using Task-Segmented Generative Model

• intro: Geophysical Research Letters (2023)
• paper: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL106084

Learning skillful medium-range global weather forecasting

• intro: Science (2023)
• paper: https://www.science.org/doi/10.1126/science.adi2336
• github: https://github.com/google-deepmind/graphcast

PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2311.18306

RainAI - Precipitation Nowcasting from Satellite Data

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2311.18398

DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting

• intro: arXiv (2023), CVPR (2024)
• paper: https://arxiv.org/abs/2312.06734, https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_DiffCast_A_Unified_Framework_via_Residual_Diffusion_for_Precipitation_Nowcasting_CVPR_2024_paper.pdf

Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands

• intro: Artificial Intelligence for the Earth Systems (2023)
• paper: https://journals.ametsoc.org/configurable/content/journals$002faies$002f2$002f4$002fAIES-D-23-0017.1.xml?t:ac=journals%24002faies%24002f2%24002f4%24002fAIES-D-23-0017.1.xml

CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling

• intro: arXiv (2024)
• paper: https://arxiv.org/abs/2402.04290

DB-RNN: A RNN for Precipitation Nowcasting Deblurring

• intro: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024)
• paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10433653

PP-Loss: An imbalanced regression loss based on plotting position for improved precipitation nowcasting

• intro: Theoretical and Applied Climatology (2024)
• paper: https://link.springer.com/article/10.1007/s00704-024-04984-w

• intro: NIPS 2022
• link: https://www.climatechange.ai/events/neurips2022

• intro: NIPS 2023
• https://neurips.cc/virtual/2023/workshop/66543

• intro: NIPS 2023 competition
• link: https://weather4cast.net/

• intro: NIPS 2024
• link: https://weather4cast.net/neurips2024/

The Python-ARM Radar Toolkit. A data model driven interactive toolkit for working with weather radar data.

• doc: https://arm-doe.github.io/pyart/
• github: https://github.com/ARM-DOE/pyart

wradlib: An Open Source Library for Weather Radar Data Processing

• doc: https://docs.wradlib.org/en/stable/
• github: https://github.com/wradlib/wradlib

Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.

• doc: https://scitools.org.uk/cartopy/docs/latest/
• github: https://github.com/SciTools/cartopy

Satellite Optical Flow with machine learning models

• doc: https://satflow.readthedocs.io/en/stable/
• github: https://github.com/openclimatefix/satflow

Python and JavaScript bindings for calling the Earth Engine API.

• doc: https://earthengine.google.com/
• github: https://github.com/google/earthengine-api

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

• doc: https://openstl.readthedocs.io/en/latest/
• github: https://github.com/chengtan9907/OpenSTL

EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task.

• intro: CVPR Workshop EarthVision (2021)
• paper: https://openaccess.thecvf.com/content/CVPR2021W/EarthVision/html/Requena-Mesa_EarthNet2021_A_Large-Scale_Dataset_and_Challenge_for_Earth_Surface_Forecasting_CVPRW_2021_paper.html
• doc: https://www.earthnet.tech/
• github: https://github.com/earthnet2021/earthnet-model-intercomparison-suite

RainBench: Towards Global Precipitation Forecasting from Satellite Imagery

• intro: AAAI (2021)
• paper: https://ojs.aaai.org/index.php/AAAI/article/view/17749
• github: https://github.com/FrontierDevelopmentLab/PyRain

Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction.

• intro: arXiv (2022)
• paper: https://arxiv.org/abs/2206.15241
• github: https://github.com/osilab-kaist/KoMet-Benchmark-Dataset

POSTRAINBENCH: A COMPREHENSIVE BENCHMARK AND A NEW MODEL FOR PRECIPITATION FORECASTING

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2310.02676
• github: https://github.com/yyyujintang/PostRainBench

A benchmark for the next generation of data-driven global weather models

• intro: arXiv (2023)
• paper: https://arxiv.org/abs/2308.15560
• doc: https://blog.research.google/2023/08/weatherbench-2-benchmark-for-next.html
• github: https://github.com/google-research/weatherbench2

• intro: EarthArXiv publishes articles from all subdomains of Earth Science and related domains of planetary science.
• link: https://eartharxiv.org/repository/about/

• intro: A Suvery about foundation models for weather and cliamte data understanding.
• github: https://github.com/shengchaochen82/Awesome-Foundation-Models-for-Weather-and-Climate

• intro: A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
• github: https://github.com/jaychempan/Awesome-LWMs