Extreme Precipitation Nowcasting using Transformer-based Generative
  Models
Cristian Meo Ankush Roy Mircea Lică Junzhe Yin Zeineb Bou Che Yanbo Wang Ruben Imhoff Remko Uijlenhoet Justin Dauwels
This paper presents an innovative approach to extreme precipitation
nowcasting by employing Transformer-based generative models, namely
NowcastingGPT 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
NowcastingGPT-EVL in generating accurate precipitation forecasts, especially
when dealing with extreme precipitation events. The code is available at
\url{https://github.com/Cmeo97/NowcastingGPT}.