Uncertainty of AI models in tropical cyclone forecasts: target observation perspective

This study investigates the uncertainties of two AI-driven meteorological models, Pangu-Weather and Fuxi, in the forecasts of tropical cyclones (TCs) from perspective of target observations. The conditional nonlinear optimal perturbation (CNOP) method is used to identify the sensitive areas for target observations, and the TCs in the Northwest Pacific and Bay of Bengal (BoB), with the latter being often referred as “BoB storms”, are investigated. The results suggest that the predictability of the “Pangu-Weather” model with respect to the BoB storm tracks is limited within 24 hours, and model error effects dominate the uncertainty of the forecasts after 24 hours; while for the TCs in the Northwest Pacific, the Fuxi model is shown to be strongly sensitive to initial perturbations and provide much accurate sensitive areas for target observations associated with TC track forecasts. These results illustrate the uncertainties of the two AI models and provide a theoretical basis for implementing field campaigns for target observations using AI models.