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

High-quality and accurate precipitation estimations can be obtained by integrating precipitation information meas-ures using ground-based and spaceborne radars in the same target area.Estimating the true precipitation state is a typ-ical inverse problem for a given set of noisy radar precipitation observations.The regularization method can appro-priately constrain the inverse problem to obtain a unique and stable solution.For different types of precipitation with different prior distributions,the L1 and L2 norms were more effective in constraining stratiform and convective pre-cipitation,respectively.As a combination of L1 and L2 norms,the Huber norm is more suitable for mixed precipita-tion types.This study uses different regularization norms to combine precipitation data from the C-band dual-polariz-ation 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 precipita-tion 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 res-ults 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.