Improving CHIRPS Daily Satellite-Precipitation Products Using Coarser Ground Observations

Abstract

A clear bias exists in the widely used gridded precipitation products (GPPs) that result from factors about topography, climate, and retrieval algorithms. Many existing optimization works have a deficiency in validation and domain sizes, which makes the evaluation and corrections of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) product still challenging. In this letter, we propose a bias-correction approach that combines coarser-resolution gauge-based precipitation with a probability distribution function (PDF) to improve the accuracy of CHIRPS. The data from 27 local precipitation gauges in Shanghai are utilized to testify the performance of our method. Results explain that daily corrected CHIRPS (Cor-CHIRPS) product has higher accuracy than CHIRPS compared with ground truths (GrTs) in terms of both error statistics and detection capability, particularly in spring, autumn, and winter. Moreover, Cor-CHIRPS better captures the frequencies of precipitation events and well depicts the spatial characteristics of the annual precipitation.