Triple collocation-based error estimation and data fusion of global gridded precipitation products over the Yangtze River basin

Abstract

Error estimation and data fusion are critical to improving the accuracy of global model- and satellite-based precipitation products for practical applications. However, they face challenges over vast areas of the world due to limited ground observations. Triple collocation (TC) method can overcome this limitation and provide an efficient way for error estimation without the “ground truth” and thus also for data fusion, by leveraging multi-source observations and model outputs, which have been increasingly available in recent years. In this work, we conducted a comprehensive study on error estimation and data fusion of a number of global gridded precipitation products over the Yangtze River basin from 2015 to 2018 using TC and multiplicative TC (MTC) methods. We use three satellite-based precipitation products such as the IMERG Final (IMERG-F), PERSIANN-CDR (PCDR) and SM2RAIN-ASCAT (SM2R), and one reanalysis dataset ERA5 which contains precipitation estimates. They were grouped into two TC triplets based on different combinations: IMERG-F + SM2R + ERA5 and PCDR + SM2R + ERA5. For performance evaluation, the TC-based error estimation methods were compared to the traditional method using rain gauge data, and the TC-based data fusion methods were compared with two widely-used data fusion methods Bayesian Model Averaging (BMA) and Random Forest based MErging Procedure (RF-MEP). Results showed that ERA5 had the best performance with the largest correlation coefficient (CC, 0.435), while PCDR had the worst accuracy with the smallest CC (0.304) and the largest absolute relative bias (RB, 0.365). TC tended to underestimate the root mean square error (RMSE) with respect to the traditional gauged-based method, but MTC showed a consistent result owing to the employment of a multiplicative error model. The performance of TC-based data fusion methods had no significant difference from BMA and RF-MEP. All data fusion results were better than the original triplets, as the mean CC value increased from 0.38 to 0.47 and the mean RMSE decreased from 15.0 to 13.5 mm/day. In addition, we found that the zero value replacement in MTC had great influence on error estimation, while had limited impacts on data fusion.