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

Abstract

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.