Estimating Uncertainties in High-Resolution Satellite Precipitation Products: Systematic or Random Error?

Abstract

Abstract This study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both satellite products, which suggests that future satellite bias adjustment procedures should account for this dependence. The resulting error estimates and their random and systematic components allow inferences about the accuracy of these datasets and will enhance their deployment in numerous applications, from hydrological modeling and hazard mitigation to climate change studies and water management policy.