A Spatial Downscaling Method for Numerical Model Prediction Based on Residual Shift Diffusion Model
WANG Xing
YE Weiliang
ZHANG Tong
MIAO Zishu
WU Qiliang
Abstract：Numerical weather prediction(NWP)plays a central role in meteorological forecasting,and enhancing spatial resolution using deep learning techniques is a key direction in spatial downscaling.However,traditional generative models often suffer from low computational efficiency,insufficient spatial texture accuracy,and mode collapse.To address these issues,this paper proposed a spatial downscaling method for NWP based on a residual shift diffusion model.Specifically,we introduced residuals into the model's diffusion process,aiding in quicker convergence and reducing the number of sampling steps.Unlike latent space diffusion models,our approach decreases inference time by a factor of 40,thereby significantly enhancing computational efficiency.The step-by-step denoising process of the diffusion model generated high-quality,detail-rich downscaled images,thereby enhancing the representation of spatial details.Furthermore,we designed a flexible noise control mechanism that effectively mitigated mode collapse,thereby ensuring the diversity of the generated images.In comparative experiments with generative adversarial models,our method significantly improved peak signal-to-noise ratio,structural similarity,and learned perceptual image patch similarity by 6%,12%,and 16%,respectively.This approach promises to provide more accurate and detailed weather information for weather-sensitive industries,driving technological advancements in related fields.
Keywords：spatial downscalingdiffusion modelnoise controlresidual shiftnumerical weather prediction
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：13( 869-881 )