https://github.com/openclimatefix/diffusion_weather
Testing out Diffusion-based models for weather and PV forecasting
https://github.com/openclimatefix/diffusion_weather
deep-learning
Last synced: 4 months ago
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Testing out Diffusion-based models for weather and PV forecasting
- Host: GitHub
- URL: https://github.com/openclimatefix/diffusion_weather
- Owner: openclimatefix
- License: mit
- Created: 2022-10-05T09:39:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2026-01-19T17:38:30.000Z (4 months ago)
- Last Synced: 2026-01-19T23:33:22.959Z (4 months ago)
- Topics: deep-learning
- Language: Dockerfile
- Size: 8.79 KB
- Stars: 18
- Watchers: 6
- Forks: 4
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Diffusion Weather
## Installation
This library can be installed through
```bash
pip install diffusion-weather
```
## Example Usage
## Pretrained Weights
Coming soon! We plan to train a model on GFS 0.25 degree operational forecasts, as well as MetOffice NWP forecasts.
We also plan trying out adaptive meshes, and predicting future satellite imagery as well.
## Training Data
Training data will be available through HuggingFace Datasets for the GFS forecasts. The initial set of data is available for [GFSv16 forecasts, raw observations, and FNL Analysis files from 2016 to 2022](https://huggingface.co/datasets/openclimatefix/gfs-reforecast), and for [ERA5 Reanlaysis](https://huggingface.co/datasets/openclimatefix/era5). MetOffice NWP forecasts we cannot
redistribute, but can be accessed through [CEDA](https://data.ceda.ac.uk/).