Extreme Precipitation Nowcasting using Transformer-based generative models (Papers Track) Spotlight
Cristian Meo (TUDelft); Mircea T Lica (Delft University of Technology); Ankush Roy (TUDelft); Zeina Boucher (TUDelft); Junzhe Yin (TUDelft); Yanbo Wang (Delft University of Technology); Ruben Imhoff (Deltares); Remko Uijlenhoet (TUDelft); Justin Dauwels (TU Delft)
Abstract
This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, specifically VideoGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed VideoGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events.