Advancing the Use of Satellite and Reanalysis Precipitation Data in Data-Scarce High-Altitude Regions: A Comprehensive Review

Abstract

Accurate precipitation observation in high-altitude regions is of paramount importance for understanding climate variability and mitigating extreme hazards. However, obtaining reliable precipitation measurements in these areas remains a formidable challenge due to sparse gauge networks, complex topography, and intricate meteorological conditions. With the advent of remote sensing and numerical simulation techniques, satellite precipitation products (SPPs) and reanalysis datasets have emerged as effective alternatives to in-situ rain gauges. This review systematically examines the progress, challenges, and prospects of applying these precipitation products to address data scarcity, focusing specifically on high-altitude regions, with the Tibetan Plateau (TP) as the primary case study. The retrieval principles, advantages and limitations, applicability, and error characteristics of these products are first outlined. These precipitation products generally demonstrate significant conditional biases across multiple spatiotemporal scales and often lack the fidelity to accurately capture extreme events. Accordingly, the theoretical foundations, implementation procedures, and case studies of bias correction and product merging methods are analyzed to evaluate their potential for accuracy improvement in alpine regions. The results indicate that these methods have been effective in reducing errors in both precipitation estimates and hydrological simulations. Nevertheless, further efforts are needed to address key challenges related to the products themselves, the accuracy improvement algorithms, and their operational application. This review offers comprehensive guidance for leveraging SPPs and reanalysis datasets to obtain more reliable precipitation estimates in high-altitude regions.