SHORT-TERM PRECIPITATION NOWCASTING BASED ON MULTI-SCALE FEATURE DEEP LEARNING
CHEN Sheng
HUANG Qiqiao
TAN Jinkai
LIANG Zhenqing
WU Chong
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.
Keywords：nowcastingdeep learningmulti-scale featuresoptical flowconvLSTM
Publication Date：2023-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 799-806 )