热带气象学报2024，Vol.40Issue(6)：869-881,13.DOI:10.16032/j.issn.1004-4965.2024.076
基于残差移位扩散模型的数值模式温度预报空间降尺度方法
A Spatial Downscaling Method for Numerical Model Prediction Based on Residual Shift Diffusion Model
摘要
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.关键词
数值模式预报/空间降尺度/扩散模型/噪声控制/残差移位Key words
spatial downscaling/diffusion model/noise control/residual shift/numerical weather prediction分类
资源环境引用本文复制引用
王兴,叶威良,张通,苗子书,吴其亮..基于残差移位扩散模型的数值模式温度预报空间降尺度方法[J].热带气象学报,2024,40(6):869-881,13.基金项目
江苏省自然科学基金面上项目(BK20221344) （BK20221344）
国家自然科学基金项目(41805033) （41805033）
教育部产学合作协同育人项目(220702331220448)共同资助 （220702331220448）
