For AI weather prediction, the training pipeline (loss function, data, optimization strategy) determines forecast skill far more than architectural choices—and current models have a fundamental blind spot for extreme weather events.
This paper explains why training methods, loss functions, and data matter more than model architecture for AI weather prediction. Using math from approximation theory and dynamical systems, the authors show that how you train a model dominates what model you use, and prove that AI weather models systematically underestimate extreme events. They validate this across ten different AI weather models.