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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.
wradlib: An Open Source Library for Weather Radar Data Processing
Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.
Satellite Optical Flow with machine learning models
Python and JavaScript bindings for calling the Earth Engine API.
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
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