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}.
@article{arxiv.2403.03929,
title = {Extreme Precipitation Nowcasting using Transformer-based Generative Models},
author = {Cristian Meo and Ankush Roy and Mircea Lică and Junzhe Yin and Zeineb Bou Che and Yanbo Wang and Ruben Imhoff and Remko Uijlenhoet and Justin Dauwels},
journal= {arXiv preprint arXiv:2403.03929},
year = {2024}
}