A Novel Generative Adversarial Network Based on Gaussian-Perceptual for Downscaling Precipitation

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Abstract
In the field of numerical weather prediction, fine-grained precipitation fields play a crucial role in forecasting and analyzing the spatial distribution and intensity of the precipitation. Historically, it is customary to employ the interpolation technique to downscale the low-resolution initial field output by assimilation systems, aligning with the requirements of a high-resolution forecasting model. Currently, data-driven deep learning methods offer novel solutions to address this challenge. In this letter, we propose a spatial downscaling algorithm for precipitation data generated from the North American Land Data Assimilation System (NLDAS), called Gaussian-Perceptual-based Generative Adversarial Network (GP-GAN). Specifically, the GP-GAN introduces a Siamese Gaussian-Perceptual Module (SGPM) which maps the data reconstructed from the generator and ground-truth to Gaussian latent space to learn the distribution of precipitation. Moreover, the Adaptive Weighted Loss Function (AWLF) is proposed to strengthen the emphasis and understanding of extreme precipitation events. Experimental results on the RainNet dataset comprising hourly precipitation over the USA demonstrate that GP-GAN provides better performance than other generative adversarial networks and diffusion models in improving spatial resolution.
Funding Information
  • science and technology innovation Program of Hunan Province (2022RC3070)
  • Hunan Provincial Science and Technology Innovation Leading Talent Fund

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