Extreme Precipitation Nowcasting using Transformer-based Generative Models
This work addresses the problem of predicting extreme weather events for meteorologists and disaster management, though it is incremental as it builds on existing Transformer-based models with a novel loss function.
The paper tackled extreme precipitation nowcasting by developing NowcastingGPT with Extreme Value Loss regularization, achieving superior performance in generating accurate short-term forecasts, particularly for extreme events, as demonstrated through qualitative and quantitative analyses.
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}.