Precipitation Nowcasting Using Physics Informed Discriminator Generative Models

• URL: http://arxiv.org/abs/2406.10108v1
• Date: Fri, 14 Jun 2024 15:12:53 GMT
• Title: Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
• Authors: Junzhe Yin, Cristian Meo, Ankush Roy, Zeineh Bou Cher, Yanbo Wang, Ruben Imhoff, Remko Uijlenhoet, Justin Dauwels,
• Abstract summary: State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. We design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.
• Score: 9.497627628556875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.

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