Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events

arXiv:2404.17652v1 Announce Type: cross
Abstract: The forecast accuracy of deep-learning-based weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous methods being developed, and limited physical guarantees offered by deep-learning models, there is a critical need for comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events, or on compound impact metrics, for which model …

abstract accuracy arxiv cs.lg evaluation events forecast forecasting impact improving physics.ao-ph prediction prediction models speak type weather weather forecasting weather prediction