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• W2951052614 endingPage "1252" @default.
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• W2951052614 abstract "Point-scale gauge observations have inherent limitations, making it difficult to study the spatial and temporal distributions of precipitation in alpine regions due to gauge undercatch and complex terrains. The Global Precipitation Measurement (GPM) mission provides new-generation satellite precipitation products that are promising alternative data sources in mountainous areas. However, quality evaluations and bias corrections should be conducted prior to the application of satellite data. In this study, an unprecedentedly dense ground station network composed of more than 1000 automatic weather stations (AWSs) over the Tianshan Mountains in China are used for bias correction of the Integrated Multisatellite Retrievals for GPM (IMERG) product. First, universal kriging interpolation is used to downscale IMERG from 0.1° to 500 m to ensure a fair comparison with the gauge observations. Then, the downscaled IMERG precipitation data over this region are corrected by two methods, i.e., stepwise regression (STEP) and geographically weighted regression (GWR). Both methods are established on various terrain factors and vegetation indexes that have strong relations with precipitation. The results show that (1) GWR outperform the conventional STEP method as well as the original IMERG; (2) the original IMERG performs best over the plain region (less than 1000 m), while the best correction effect was found in middle and low-elevation region (1000–1500 m); and (3) the performance of the GWR model is largely dependent on the number of available training stations in mountainous areas. Overall, the methods and results presented in this study provide insight into the correction of satellite precipitation data in mountainous areas with scarce ground observations." @default.
• W2951052614 created "2019-06-27" @default.
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• W2951052614 date "2019-08-01" @default.
• W2951052614 modified "2023-10-14" @default.
• W2951052614 title "Correcting GPM IMERG precipitation data over the Tianshan Mountains in China" @default.
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• W2951052614 doi "https://doi.org/10.1016/j.jhydrol.2019.06.019" @default.
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