| language: en | |
| library_name: pytorch | |
| license: mit | |
| # Cloudcasting | |
| ## Model Description | |
| <!-- Provide a longer summary of what this model is/does. --> | |
| This model is trained to predict future frames of satellite data from past frames. It takes 3 hours | |
| of recent satellkite imagery at 15 minute intervals and predicts 3 hours into the future also at | |
| 15 minute intervals. The satellite inputs and predictions are multispectral with 11 channels. | |
| See [1] for the repo used to train the model. | |
| - **Developed by:** Open Climate Fix and the Alan Turing Institute | |
| - **License:** mit | |
| # Training Details | |
| ## Data | |
| <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| This was trained on EUMETSAT satellite imagery derived from the data stored in [this google public | |
| dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-data/eumetsat-seviri-rss?hl=en-GB&inv=1&invt=AbniZA&project=solar-pv-nowcasting&pli=1). | |
| The data was processed using the protocol in [2] | |
| ## Results | |
| The training logs for the current model can be found here: | |
| - https://wandb.ai/openclimatefix/sat_pred/runs/ckyb2l1s | |
| ### Software | |
| - [1] https://github.com/openclimatefix/sat_pred | |
| - [2] https://github.com/alan-turing-institute/cloudcasting |