Deep learning-based multi-source precipitation merging for the Tibetan Plateau

Nan, Tianyi; Chen, Jie; Ding, Zhiwei; Li, Wei; Chen, Hua

doi:10.1007/s11430-022-1050-2

Cited by 10 publications

(1 citation statement)

References 59 publications

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“…Firstly, the inverse distance-weighted interpolation method is used to interpolate the spatial resolutions of the gridded precipitation data to 0.1 • , and the data in a day are aggregated to unify all the precipitation data to the daily scale. In this way, the spatiotemporal characteristics of the original data are retained, and more errors not be introduced to affect the merging experiment [31][32][33][34]. In addition, in order to ensure the validity of the evaluation experiment, we randomly divide the gauge data into two parts according to a ratio of 7:3.…”

Section: Datamentioning

confidence: 99%

Multi-Source Precipitation Data Merging for High-Resolution Daily Rainfall in Complex Terrain

Li,

Wang,

Zhang

et al. 2023

Remote Sensing

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This study developed a satellite, reanalysis, and gauge data merging model for daily-scale analysis using a random forest algorithm in Sichuan province, characterized by complex terrain. A high-precision daily precipitation merging dataset (MSMP) with a spatial resolution of 0.1° was successfully generated. Through a comprehensive evaluation of the MSMP dataset using various indices across different periods and regions, the following findings were obtained: (1) GPM-IMERG satellite observation data exhibited the highest performance in the region and proved suitable for inclusion as the initial background field in the merging experiment; (2) the merging experiment significantly enhanced dataset accuracy, resulting in a spatiotemporal distribution of precipitation that better aligned with gauge data; (3) topographic factors exerted certain influences on the merging test, with greater accuracy improvements observed in the plain region, while the merging test demonstrated unstable effects in higher elevated areas. The results of this study present a practical approach for merging multi-source precipitation data and provide a novel research perspective to address the challenge of constructing high-precision daily precipitation datasets in regions characterized by complex terrain and limited observational coverage.

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Section: Datamentioning

confidence: 99%