From architecture to atmospheric sensitivity: studying forecast uncertainty with Prithvi-WxC

AI-based weather emulators have begun to rival the accuracy of traditional numerical solvers, for a fraction of the computational cost. The question of whether they can be reliably deployed in all use cases (e.g., for the forecast of extreme scenarios), however, is still open. We outline an ensembling strategy based on architectural variations of the Prithvi WxC foundation model (FM), highlighting the impact of each of these variations on physical accuracy and ability to capture the distributional extremes. A simple of ensemble of 100 models is sufficient to observe the complex mapping between configuration parameters and the forecast sensitivity of different atmospheric variables. We characterize some features of this mapping and connect them to the task of predicting various weather extremes.