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SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts.
Global Extreme Heat Forecasting Using Neural Weather Models
We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss.
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