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This paper describes ML models for predicting thunderstorms, taking in very similar data to MetNet, and dealing with certain modalities dropping out while still working, and gives probabilistic forecasts. Its forecasting 60min ahead at 5 minute intervals.
They did find the satellite data and radar were the most important inputs to the model:
Context
This seems like its trying to do a similar thing to MetNet/MetNet-2, with similar inputs, so might be helpful with expanding MetNet or adding it as a new option.
@JackKelly@peterdudfield Some more support for satellite being quite important for short term forecasting at least. Not solar, but they did use EUMETSAT data
https://arxiv.org/pdf/2211.01001.pdf
Detailed Description
This paper describes ML models for predicting thunderstorms, taking in very similar data to MetNet, and dealing with certain modalities dropping out while still working, and gives probabilistic forecasts. Its forecasting 60min ahead at 5 minute intervals.
They did find the satellite data and radar were the most important inputs to the model:
Context
This seems like its trying to do a similar thing to MetNet/MetNet-2, with similar inputs, so might be helpful with expanding MetNet or adding it as a new option.
Possible Implementation
Code: https://github.com/MeteoSwiss/c4dl-multi
Data: https://zenodo.org/record/6802292
Pretrained models: https://zenodo.org/record/7157986
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