High-Resolution Probabilistic Data-Driven Weather Modeling with a Stretched-Grid
The episode discusses a paper on high-resolution probabilistic data-driven weather modeling using a stretched grid. Hosts analyze how this model achieves kilometer-scale detail while maintaining computational efficiency through grid stretching and uses an ensemble approach to capture uncertainty. Key innovations include a spectral loss function that improves spatial coherence and its operational use at the Norwegian Meteorological Institute.