Fusion of Ground-Based and Spaceborne Radar Precipitation Based on Spatial Domain Regularization  

作　　者：Anfan HUANG Leilei KOU Yanzhi LIANG Ying MAO Haiyang GAO Zhigang CHU 

机构地区：[1]School of Atmospheric Physics,Nanjing University of Information Science&Technology,Nanjing 210044 [2]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044

出　　处：《Journal of Meteorological Research》2024年第2期285-302,共18页气象学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(General Program)(41975027);National Key Research and Development Program(2021YFC2802502)。

摘　　要：High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L_(1) and L_(2) norms were more effective in constraining stratiform and convective precipitation,respectively.As a combination of L_(1) and L_(2) norms,the Huber norm is more suitable for mixed precipitation types.This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar(CDP)and dual-frequency precipitation radar(DPR)on the Global Precipitation Measurement(GPM)mission core satellite.Compared to single-source radar data,the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields.In 27 precipitation cases,the fusion results utilizing the Huber norm achieved a structural similarity index measure(SSIM)and a peak signal-to-noise ratio(PSNR)of 0.8378 and 30.9322,respectively,compared with the CDP data.The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation,with a reduction in the mean absolute error(MAE;16.1%and 22.6%,respectively)and root-mean-square error(RMSE;11.7%and 13.6%,respectively)compared to the 1-norm and 2-norm.Moreover,in contrast to the fusion results of scale recursive estimation(SRE),the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.

关 键 词：dual-frequency precipitation radar(DPR) dual-polarization radar data fusion REGULARIZATION Huber norm 

分 类 号：P412.25[天文地球—大气科学及气象学]