Abstract
Abstract. Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.
Highlights
30 High-quality precipitation data is fundamental to understand the regional and global hydrological processes
4.1 Bias adjustment of multi-Satellite Precipitation Estimates (SPE) 155 Compared to the gauge references, the original multi-SPE including PERCDR, 3B42 Version 7 (3B42V7), CMORPH and IMERG show significant biases at the independent validation sites over the NETP during the warm season of 2014 (Table 2)
Their statistical error metrics range from 6.59-8.07 mm/d, 63.2-83.5%, and 0.40-0.57, in terms of Root Mean Square Errors (RMSE), Normalized Mean Absolute Errors (NMAE), and CC, respectively. 3B42V7 performs the worst with the highest RMSE and NMAE at 8.07 mm/d and 83.5%, and the lowest CC of 0.40
Summary
30 High-quality precipitation data is fundamental to understand the regional and global hydrological processes. Discussion started: 17 February 2020 c Author(s) 2020. Ground sensors (Ma et al, 2015). The satellite sensors are capable of providing precipitation estimates at a large scale (Hou et al, 2014), but performances of available satellite products vary among different retrieval methods and climatic areas (Yong et al, 2015; Prat and Nelson, 2015; Ma et al, 2016). It is suggested to incorporate precipitation estimates from multiple 35 sources into a fusion procedure with fully consideration of the strength of individual members and associated uncertainty
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