Improving quantitative precipitation estimates by radar-rain gauge merging and an integration algorithm in the Yishu River catchment, China

Abstract

High-precision areal rainfall is crucial for hydrometeorological coupled forecasts. The accuracy of quantitative precipitation estimates (QPE) is improved by merging radar-rain gauge data with an integration approach based on a statistical weight matrix in the Yishu River catchment, China. First, a local Z-R relationship (Z = 85R1.82) is reconstructed using a genetic optimization algorithm to minimize the error from different precipitation patterns and climate zones. Next, based on the local Z-R relationship, six methods of merging radar-rain gauge data are respectively adapted to improve the accuracy of QPE, as follows: mean field bias (MFB), Kalman filter (KLM), optimum interpolation (OPT), variation method (VAR), two-step calibration of KLM and OPT (KOP), and two-step calibration of KLM and VAR (KVR). The results indicate that QPE accuracy is clearly improved, and is in good agreement with rain gauge observations, after the six merging methods are applied. Among these methods, KOP performs the best, reducing the mean relative error from 55.2 to 15.1%. An innovative aspect of this work is the inclusion of an integrated ideology based on a statistical weight matrix, which further improves the accuracy of QPE by incorporating the advantages of each estimation mode. The results further show that the accuracy of QPE derived from the integration approach is higher than that obtained by any individual method; QPE values are similar to those obtained the automatic rain gauge network in both the spatial distribution and location of the intense precipitation centers, and better reflects the precipitation status over the ground surface. This approach could serve as a promising conventional method for QPE in the study region.