热带气象学报2023，Vol.39Issue(6)：799-806,8.DOI:10.16032/j.issn.1004-4965.2023.069
基于多尺度特征深度学习的短临降水预报
SHORT-TERM PRECIPITATION NOWCASTING BASED ON MULTI-SCALE FEATURE DEEP LEARNING
摘要
Abstract
Radar echo extrapolation is an important method precipitation nowcasting.To address the problem of the loss of characteristic evolution information in echo extrapolation prediction with the increase of echo intensity and prediction time,this paper proposes a deep learning model for precipitation nowcasting based on multi-scale feature fusion(MSF2).Firstly,the multi-scale convolution kernel is used to extract the features of the shallow information of the network to offset the shortcomings caused by the single feature detection.Secondly,the feature information of different dimensions is spliced and the channels are shuffled to further enhance the information circulation and information expression capabilities between the feature map channels.Finally,the multi-scale information in the feature map is fused in order to effectively keep the channel information after the fusion of the feature map.With the South China radar echo data,the fusion experiment was carried out under three different precipitation intensities,and compared with two mainstream algorithms,i.e.,ConvLSTM and optical flow.The experimental results show that MSF2 performs best in terms of all evaluation indexes under the conditions of precipitation rates 5 mm/h,10 mm/h and 25 mm/h.It can be concluded that the introduction of a multi-scale mechanism can improve the feature extraction ability of the nowcasting model.Compared with the current radar echo extrapolation algorithm ConvLSTM and optical flow,the proposed model MSF2 has better potentials in operational applications and higher forecast accuracy for precipitation nowcasting.关键词
短临预报/深度学习/多尺度特征/光流法/ConvLSTMKey words
nowcasting/deep learning/multi-scale features/optical flow/convLSTM分类
天文与地球科学引用本文复制引用
陈生,黄启桥,谭金凯,梁振清,吴翀..基于多尺度特征深度学习的短临降水预报[J].热带气象学报,2023,39(6):799-806,8.基金项目
中国科学院高层次人才计划项目(E2290702) （E2290702）
广西重点研发项目(2021AB40108、2021AB40137) （2021AB40108、2021AB40137）
国家自然科学基金项目(41875182) （41875182）
广东省基础与应用基础研究基金项目(2020A1515110457) （2020A1515110457）
中国博士后科学基金面上资助(2021M693584) （2021M693584）
北部湾环境演变与资源利用教育部重点实验室(南宁师范大学)开放基金(NNNU-KLOP-K2103)共同资助 （南宁师范大学）
