Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
<|reference_start|>Precipitation Nowcasting Using Physics Informed Discriminator Generative Models: 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.<|reference_end|>
@article{yin2024precipitation,
    title={Precipitation Nowcasting Using Physics Informed Discriminator Generative
  Models},
    author={Junzhe Yin, Cristian Meo, Ankush Roy, Zeineh Bou Cher, Yanbo Wang,
  Ruben Imhoff, Remko Uijlenhoet, Justin Dauwels},
    journal={arXiv preprint arXiv:2406.10108},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10108},
    primaryClass={cs.LG cs.AI}
}