An assessment of gridded precipitation products over High Mountain Asia

• Published in: Journal of hydrology. Regional studies Vol. 52; p. 101675
• Main Authors: Dollan, Ishrat J., Maina, Fadji Z., Kumar, Sujay V., Nikolopoulos, Efthymios I., Maggioni, Viviana
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024; Elsevier
• Subjects: Asia; climate; Ensemble products; High Mountain Asia; hydrology; Precipitation products; probability; satellites; Seasons; Validation; weather; Asia; Validation; High Mountain Asia; Precipitation products; Ensemble products; Seasons
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2024.101675

High Mountain Asia The study assesses five high-resolution gridded precipitation products against observations taken at twenty-seven weather stations across High Mountain Asia (HMA), one of the world's most complex and data scarce regions. Estimating spatiotemporal patterns of average and extreme precipitation in HMA is challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Evaluating the quality and reliability of multi-source precipitation products before analyzing their trends and patterns is fundamental in such a region. A suite of high-resolution satellite-based product (the Climate Hazards Group Infrared Precipitation with Stations, CHIRPS, and the Integrated MultisatellitE Retrievals for Global Precipitation Measurement, IMERG), model reanalysis precipitation estimates (European Centre for Medium-Range Weather Forecasts, fifth generation atmospheric reanalysis product, ERA5), and their blended product (ensemble mean EM and a localized probability matched mean, LPM) are evaluated on a daily scale from 2001 to 2008. The performance of each product is analyzed in relation to elevation, seasonal accumulation, and extreme indices (e.g., frequency of wet days and consecutive wet days). Results show that reanalysis products present the largest overestimation, satellite retrievals the lowest, and ensemble products’ performance is encapsulated in between. LPM carries a high bias especially during winter when satellite estimates struggle to capture solid precipitation. The two ensemble products show higher correlations with the ground observations and smaller uncertainties in the error metrics than the original products. [Display omitted] •Inconsistencies among precipitation data in HMA can be reduced through an ensemble product.•Ensemble products show smaller error variance and higher correlation than the original products.•Ensemble products carry high biases in winter when satellites cannot capture solid precipitation•Reanalysis and ensemble products tend to overestimate the fraction of wet days.