{"id":72941,"url":"https://github.com/hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting","name":"Awesome-Diffusion-Models-for-Weather-Forecasting","description":"A collection of research papers on Diffusion Models for Weather Forecasting.","projects_count":68,"last_synced_at":"2026-10-18T02:00:24.480Z","repository":{"id":222588701,"uuid":"757826510","full_name":"hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting","owner":"hoonerg","description":"A collection of research papers on Diffusion Models for Weather Forecasting.","archived":false,"fork":false,"pushed_at":"2025-07-15T13:13:19.000Z","size":55,"stargazers_count":257,"open_issues_count":0,"forks_count":24,"subscribers_count":8,"default_branch":"main","last_synced_at":"2026-09-28T12:56:50.453Z","etag":null,"topics":["awesome","diffusion-models","weather-forecast"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hoonerg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-02-15T04:08:02.000Z","updated_at":"2026-09-18T11:43:58.000Z","dependencies_parsed_at":"2024-02-15T05:25:00.014Z","dependency_job_id":"1dfce37c-ac15-40a0-82f9-18dfd99af2ba","html_url":"https://github.com/hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting","commit_stats":null,"previous_names":["hoonerg/awesome-diffusion-models-for-weather-forecasting"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hoonerg%2FAwesome-Diffusion-Models-for-Weather-Forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hoonerg%2FAwesome-Diffusion-Models-for-Weather-Forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hoonerg%2FAwesome-Diffusion-Models-for-Weather-Forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hoonerg%2FAwesome-Diffusion-Models-for-Weather-Forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hoonerg","download_url":"https://codeload.github.com/hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hoonerg%2FAwesome-Diffusion-Models-for-Weather-Forecasting/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":344872684,"owners_count":38336242,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-18T02:00:06.448Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-10-09T00:00:28.289Z","updated_at":"2026-10-18T02:00:24.480Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Diffusion Models"],"sub_categories":[],"readme":"# Awesome Diffusion Models for Weather Forecasting \n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/hoonerg/Awesome-Diffusion-Models-for-Weather-Forecasting) ![PRs Welcome](https://img.shields.io/badge/PRs-Welcome-green) \n\nA collection of research papers on Diffusion Models for Weather Forecasting.  \nThe papers are ordered by date.\n\nI check Arxiv weekly and add any diffusion-related papers on topics in atmospheric science.  \nI might miss the publication of a paper if it is published after its initial upload to Arxiv.\n\n## Diffusion Models\n| (***Model Name***) - Paper Title                | Topic                      | Publication    | Date (\u0026darr;) | Links                                         |\n| ---------------------------------------------- | ------------------ | -------------- | -------- | ----------------- |\n| Diffusion Models for High-Resolution Solar Forecasts | Cloud Cover Forecasting | - | 2023.02  | [[paper]](https://arxiv.org/pdf/2302.00170v1.pdf)   |\n| ***LDCast*** - Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification | Precipitation Nowcasting | - | 2023.04  | [[paper]](https://arxiv.org/pdf/2304.12891.pdf) [[code]](https://github.com/MeteoSwiss/ldcast)  |\n| ***DiffESM*** - Conditional Emulation of Earth System Models with Diffusion Models | Emulation | ICLR 2023 Workshop | 2023.04  | [[paper]](https://arxiv.org/pdf/2304.11699.pdf) [[code]](https://github.com/JGCRI/diffesm)  |\n| ***SEEDS*** - Emulation of Weather Forecast Ensembles with Diffusion Models | Ensemble Forecast Emulation | Science Advances | 2023.06  | [[paper]](https://arxiv.org/pdf/2306.14066.pdf)  |\n| ***DYffusion*** - A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting | Spatiotemporal Forecasting | NeurIPS 2023 | 2023.06  | [[paper]](https://arxiv.org/pdf/2306.01984.pdf) [[code]](https://github.com/Rose-STL-Lab/dyffusion)  |\n| ***SwinRDM*** - Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting | Global Weather Forecasting | AAAI 2023 | 2023.06  | [[paper]](https://arxiv.org/pdf/2306.03110.pdf)  |\n| ***PreDiff*** - Precipitation Nowcasting with Latent Diffusion Models | Precipitation Nowcasting | NeurIPS 2023 | 2023.07  | [[paper]](https://arxiv.org/pdf/2307.10422.pdf) [[code]](https://github.com/gaozhihan/PreDiff)  |\n| ***GDE*** - Precipitation nowcasting with generative diffusion models | Precipitation Nowcasting | - | 2023.08  | [[paper]](https://arxiv.org/pdf/2308.06733.pdf) [[code]](https://github.com/fmerizzi/Precipitation-nowcasting-with-generative-diffusion-models)  |\n| ***CorrDiff*** - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling | Downscaling | - | 2023.10  | [[paper]](https://arxiv.org/pdf/2309.15214.pdf) |\n| ***FuXi-Extreme*** - Improving extreme rainfall and wind forecasts with diffusion model | Global (Extreme) Weather Forecasting | - | 2023.10  | [[paper]](https://arxiv.org/pdf/2310.19822.pdf) |\n| ***OF-Diff*** - Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion | Downscaling | - | 2023.11  | [[paper]](https://arxiv.org/pdf/2312.06071.pdf) |\n| ***GenCast*** - Diffusion-based ensemble forecasting for medium-range weather | Global Weather Forecasting | Nature | 2023.12  | [[paper]](https://arxiv.org/pdf/2312.15796)  [[code]](https://github.com/google-deepmind/graphcast) |\n| ***DiffCast*** - A Unified Framework via Residual Diffusion for Precipitation Nowcasting | Precipitation Nowcasting | CVPR 2024 | 2023.12  | [[paper]](https://arxiv.org/pdf/2312.06734.pdf) [[code]](https://github.com/DeminYu98/DiffCast) |\n| ***DGDM*** - Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting | Weather Forecasting using Satellite | ECCV 2024 | 2023.12  | [[paper]](https://arxiv.org/pdf/2312.02819.pdf) [[code]](https://github.com/DongGeun-Yoon/DGDM?tab=readme-ov-file)  |\n| Advancing Realistic Precipitation Nowcasting With a Spatiotemporal Transformer-Based Denoising Diffusion Model | Precipitation Nowcasting | IEEE TGRS | 2024.01  | [[paper]](https://ieeexplore.ieee.org/abstract/document/10403855)  |\n| ***DiffDA*** - A diffusion model for weather-scale data assimilation | Data Assimilation | ICML 2024 | 2024.01  | [[paper]](https://arxiv.org/pdf/2401.05932.pdf) [[code]](https://github.com/spcl/DiffDA) |\n| ***LT3P*** - Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data | Tropical Cyclone Trajectory Forecasting | ICLR 2024 | 2024.01  | [[paper]](https://arxiv.org/pdf/2401.15726.pdf) [[code]](https://github.com/iclr2024submit/LT3P) |\n| Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models | Super resolution | - | 2024.01  | [[paper]](https://arxiv.org/pdf/2401.15469.pdf) [[code]](https://github.com/fmerizzi/ERA5-to-CERRA-via-Diffusion-Models/) |\n| ***ExtremeCast*** - Boosting Extreme Value Prediction for Global Weather Forecast | Global (Extreme) Weather Forecasting | - | 2024.02  | [[paper]](https://arxiv.org/pdf/2402.01295.pdf) [[code]](https://github.com/black-yt/ExtremeCast) |\n| ***CasCast*** - Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling | Precipitation Nowcasting | ICML 2024 | 2024.02  | [[paper]](https://arxiv.org/pdf/2402.04290.pdf) [[code]](https://github.com/OpenEarthLab/CasCast) |\n| ***SDEdit-Weather*** - Weather Prediction with Diffusion Guided by Realistic Forecast Processes | Global Weather Forecasting | - | 2024.02  | [[paper]](https://arxiv.org/pdf/2402.06666.pdf)  |\n| Physics-Informed Diffusion Model and Sampling for Global Weather Forecasting | Global Weather Forecasting | - | 2024.03  | [[paper]](https://doi.org/10.5194/egusphere-egu24-13882)  |\n| ***DiffObs*** - Generative Diffusion for Global Forecasting of Satellite Observations | Global Preciptation Forecasting | ICLR 2024 Workshop | 2024.04  | [[paper]](https://arxiv.org/pdf/2404.06517.pdf)  |\n| Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models | Emulation | ICLR 2024 Workshop | 2024.04  | [[paper]](https://arxiv.org/pdf/2404.08797.pdf)  |\n| ***SLAMS*** - Deep Generative Data Assimilation in Multimodal Setting | Data Assimilation | CVPR 2024 Workshop | 2024.04  | [[paper]](https://arxiv.org/pdf/2404.06665) [[code]](https://github.com/yongquan-qu/SLAMS)  |\n| ***ClimateDiffuse*** - Generative Diffusion-based Downscaling for Climate | Downscaling | - | 2024.04  | [[paper]](https://arxiv.org/pdf/2404.17752) [[code]](https://github.com/robbiewatt1/ClimateDiffuse)  |\n| ***CloudDiff*** - Super-resolution ensemble retrieval of cloud properties for all day using the generative diffusion model | Super-resolution | - | 2024.05  | [[paper]](https://arxiv.org/pdf/2405.04483)  |\n| ***Spherical DYffusion*** - Probabilistic Emulation of a Global Climate Model with Spherical DYffusion | Global Climate Modelling | NeurIPS 2024 | 2024.06  | [[paper]](https://arxiv.org/pdf/2406.14798)  [[code]](https://github.com/Rose-STL-Lab/spherical-dyffusion) |\n| Towards diffusion models for large-scale sea-ice modelling | Sea-Ice modelling | - | 2024.06  | [[paper]](https://arxiv.org/pdf/2406.18417)  |\n| Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales | Data Assimilation | - | 2024.06  | [[paper]](https://arxiv.org/pdf/2406.16947)  |\n| ***LDM_res*** - Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0_CLM9 simulations | Downscaling | - | 2024.06  | [[paper]](https://arxiv.org/pdf/2406.13627) [[code]](https://github.com/DSIP-FBK/DiffScaler)  |\n| Latent Diffusion Model for Generating Ensembles of Climate Simulations | Ensemble Forecasting | - | 2024.07  | [[paper]](https://arxiv.org/pdf/2407.02070)  |\n| ***CPMGEM*** - Machine learning emulation of precipitation from km-scale regional climate simulations using a diffusion model | Precipitation Forecasting | - | 2024.07  | [[paper]](https://arxiv.org/pdf/2407.14158) [[code]](https://github.com/henryaddison/mlde)  |\n| Forecasting Tropical Cyclones with Cascaded Diffusion Models | TC/Precipitation Forecasting | ICLR 2024 Workshop | 2024.07  | [[paper]](https://arxiv.org/pdf/2310.01690) [[code]](https://github.com/p3jitnath/forecast-diffmodels)  |\n| ***KESHDiff*** - Generative Diffusion Model-based Downscaling of Observed Sea Surface Height over Kuroshio Extension since 2000 | Downscaling | - | 2024.08  | [[paper]](https://arxiv.org/pdf/2408.12632) |\n| ***StormCast*** - Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling | Emulation | - | 2024.08  | [[paper]](https://arxiv.org/pdf/2408.10958)  |\n| ***DifERS*** - A Generative Diffusion Model for Probabilistic Ensembles of Precipitation Maps Conditioned on Multisensor Satellite Observations | Precipitation Forecasting | - | 2024.09  | [[paper]](https://arxiv.org/pdf/2409.16319) |\n| ***CoDiCast*** - Conditional Diffusion Model for Weather Prediction with Uncertainty Quantification | Global Weather Forecasting | - | 2024.09  | [[paper]](https://arxiv.org/pdf/2409.05975) [[code]](https://github.com/JimengShi/CoDiCast)  |\n| ***R2-D2*** - Dynamical-generative downscaling of climate model ensembles | Downscaling | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.01776) |\n| ***Yantian*** - An Application Platform for AI Global Weather Forecasting Models | Global Weather Forecasting | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.04539)  |\n| ***ARCI*** - Continuous Ensemble Weather Forecasting with Diffusion models | Ensemble Forecasting | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.05431) [[code]](https://github.com/martinandrae/Continuous-Ensemble-Forecasting)  |\n| ***IceDiff*** - High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior | Sea Ice Forecasting | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.09111)  |\n| Ensemble-based, large-eddy reconstruction of wind turbine inflow in a near-stationary atmospheric boundary layer through generative artificial intelligence | Data Reconstruction | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.14024)  |\n| ***TCP-Diffusion*** - A Multi-modal Diffusion Model for Global Tropical Cyclone Precipitation Forecasting with Change Awareness | TC/Precipitation Forecasting | - | 2024.10  | [[paper]](https://arxiv.org/pdf/2410.13175) |\n| ***GenBCSR*** - Statistical Downscaling via High-Dimensional Distribution Matching with Generative Models | Downscaling | - | 2024.12  | [[paper]](https://arxiv.org/pdf/2412.08079) |\n| Downscaling Precipitation with Bias-informed Conditional Diffusion Model | Downscaling | - | 2024.12  | [[paper]](https://arxiv.org/pdf/2412.14539) [[code]](https://github.com/RoseLV/research_super-resolution)  |\n| Spatiotemporally Coherent Probabilistic Generation of Weather from Climate | Downscaling | - | 2024.12  | [[paper]](https://arxiv.org/pdf/2412.15361) [[code]](https://github.com/schmidtjonathan/Climate2Weather)  |\n| A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation | Downscaling | - | 2024.12  | [[paper]](https://arxiv.org/pdf/2412.15361) [[code]](https://github.com/schmidtjonathan/Climate2Weather)  |\n| Using Generative Models to Produce Realistic Populations of UK Windstorms | Windstorm Simulation | - | 2025.01  | [[paper]](https://arxiv.org/pdf/2501.16110) |\n| Improving Tropical Cyclone Forecasting With Video Diffusion Models | Tropical Cyclone Forecasting | - | 2025.01  | [[paper]](https://arxiv.org/pdf/2501.16003) [[code]](https://github.com/Ren-creater/forecast-video-diffmodels)  |\n| Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations | Data Assimilation | - | 2025.01  | [[paper]](https://arxiv.org/pdf/2501.12419) [[code]](https://github.com/Siming-Liang/EnSFInpainting)  |\n| ***SGD*** - Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution | Downscaling | - | 2025.02  | [[paper]](https://arxiv.org/pdf/2502.07814)  |\n| ***SATcast*** - Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model | Convective Clouds Nowcasting | - | 2025.02  | [[paper]](https://arxiv.org/pdf/2502.10957) [[code]](https://github.com/cd4tpcell/SATcast/tree/main)  |\n| ***Diffusion-LAM*** - Probabilistic Limited Area Weather Forecasting with Diffusion | Probabilistic/Regional Forecasting | - | 2025.02  | [[paper]](https://arxiv.org/pdf/2502.07532) [[code]](https://github.com/ErikLarssonDev/neural-lam/tree/prob-model-boundary-ICLR-workshop-2025)  |\n| Data-driven Mesoscale Weather Forecasting Combining Swin-Unet and Diffusion Models | Mesoscale Forecasting | - | 2025.03  | [[paper]](https://arxiv.org/pdf/2503.19354) |\n| ***Appa*** - Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation | Data Assimilation | - | 2025.04  | [[paper]](https://arxiv.org/pdf/2504.18720) [[code]](https://montefiore-sail.github.io/appa/)  |\n| ***cBottle*** - Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere | Global Weather Forecasting | - | 2025.05  | [[paper]](https://arxiv.org/pdf/2505.06474v1) [[code]](https://github.com/NVlabs/cBottle)  |\n| Score-based diffusion nowcasting of GOES imagery | Cloud Nowcasting | - | 2025.05  | [[paper]](https://arxiv.org/abs/2505.10432) [[code]](https://github.com/dopplerchase/cira-diff)  |\n| ***Align-DA*** - Align Score-based Atmospheric Data Assimilation with Multiple Preferences | Data Assimilation | - | 2025.05  | [[paper]](https://arxiv.org/pdf/2505.22008) |\n| Using Diffusion Models to do Data Assimilation | Data Assimilation | - | 2025.06  | [[paper]](https://arxiv.org/pdf/2506.02249) |\n| ***DEF*** - Diffusion-augmented Ensemble Forecasting | Emsemble Forecasting | - | 2025.06  | [[paper]](https://arxiv.org/pdf/2506.07324) |\n| ***MODS*** - Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling | Downscaling | - | 2025.06  | [[paper]](https://arxiv.org/abs/2506.14798) |\n| Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting | Probabilistic Forecasting | - | 2025.06  | [[paper]](https://arxiv.org/abs/2506.20024) [[code]](https://github.com/salvaRC/erdm)  |\n| ***LaDCast*** - A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting | Global Weather Forecasting | - | 2025.06  | [[paper]](https://arxiv.org/abs/2506.09193) [[code]](https://github.com/tonyzyl/ladcast)  |\n| Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence | Super-resolution | - | 2025.07  | [[paper]](https://arxiv.org/abs/2507.00719) [[code]](https://github.com/ananthu545/quasi-geostrophic-beta-plane-super-resolution)  |\n| ***HRRRCast*** - Data-driven emulator for regional weather forecasting at convection allowing scales | Regional Weather Forecasting | - | 2025.07  | [[paper]](https://arxiv.org/abs/2507.05658) |\n| Efficient Kilometer-Scale Precipitation Downscaling with Conditional Wavelet Diffusion | Precipitation Downscaling | - | 2025.07  | [[paper]](https://arxiv.org/abs/2507.01354) |\n| Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity | Data Assimilation | - | 2025.07  | [[paper]](https://arxiv.org/abs/2507.06479) [[code]](https://github.com/TACS-UCSC/GenDA-Lagrangian)  |\n| - | - | - | 2025.00  | [[paper]]() [[code]]()  |\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/hoonerg%2Fawesome-diffusion-models-for-weather-forecasting/projects"}