Extreme Precipitation Nowcasting using Transformer-based Generative Models

• URL: http://arxiv.org/abs/2403.03929v1
• Date: Wed, 6 Mar 2024 18:39:41 GMT
• Title: Extreme Precipitation Nowcasting using Transformer-based Generative Models
• Authors: Cristian Meo, Ankush Roy, Mircea Lic\u{a}, Junzhe Yin, Zeineb Bou Che, Yanbo Wang, Ruben Imhoff, Remko Uijlenhoet, Justin Dauwels
• Abstract summary: This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. 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.
• Score: 9.497627628556875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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