Error Analysis and Modeling of GPM Dual-Frequency Precipitation Radar Near-Surface Rainfall Product

Abstract

Abstract The error characterization of rainfall products of spaceborne radar is essential for better applications of radar data, such as multisource precipitation data fusion and hydrological modeling. In this study, we analyzed the error of the near-surface rainfall product of the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement Mission (GPM) and modeled it based on ground C-band dual-polarization radar (CDP) data with optimization rainfall retrieval. The comparison results show that the near-surface rainfall data were overestimated by light rain and slightly underestimated by heavy rain. The error of near-surface rainfall of the DPR was modeled as an additive model according to the comparison results. The systematic error of near-surface rainfall was in the form of a quadratic polynomial, while the systematic error of stratiform precipitation was smaller than that of convective precipitation. The random error was modeled as a Gaussian distribution centered from −1 to 0 mm h−1. The standard deviation of the Gaussian distribution of convective precipitation was 1.71 mm h−1, and the standard deviation of stratiform precipitation was 1.18 mm h−1, which is smaller than that of convective precipitation. In view of the precipitation retrieval algorithm of DPR, the error causes were analyzed from the reflectivity factor (Z) and the drop size distribution (DSD) parameters (Dm, Nw). The high accuracy of the reflectivity factor measurement results in a small systematic error. Importantly, the negative bias of Nw was very obvious when the rain type was convective precipitation, resulting in a large random error. Significance Statement This study first compares the total and different rain types of near-surface rainfall measured by DPR and ground-based radar CDP, then separates the error of DPR near-surface rainfall into systematic and random errors and analyzes the possible causes of the error. The purpose of this study is to better apply the error model to applications such as optimal data fusion and hydrological modeling, and the analysis of the error can also provide a basis for improving the spaceborne precipitation retrieval algorithm.