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

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 L1 and L2 norms were more effective in constraining stratiform and convective precipitation, respectively. As a combination of L1 and L2 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.