GMCP: A Fully Global Multisource Merging-and-Calibration Precipitation Dataset (1-Hourly, 0.1°, Global, 2000-the Present)
GMCP : un ensemble de données de précipitations multisources entièrement mondial, fusionné et étalonné (1 heure, 0,1°, mondial, 2000 à nos jours)
Ma, Ziqiang ; Xu, Jintao ; Dong, Bo ; Hu, Xie ; Hu, Hao ; Yan, Songkun ; Zhu, Siyu ; He, Kang ; Shi, Zhou ; Chen, Yun ; Fang, Xiang ; Zhang, Qinghong ; Gu, Songyan ; Weng, Fuzhong
Année de publication
2025
Current global multisource merged precipitation datasets can facilitate better utilization of the complementary nature of gauge-, satellite-, and reanalysis-based precipitation estimates, particularly for capturing precipitation variability. However, merging these datasets at high resolutions of 1-hourly and 0.1° on a full global scale remains a substantial challenge for the scientific community owing to high spatiotemporal heterogeneities. This study proposes a merging-and-calibration framework to optimally integrate the advantages of gauge-, satellite-, and model-based precipitation estimates, focusing on precipitation occurrences and providing a new fully global multisource merging-and-calibration precipitation (GMCP: 1-hourly, 0.1°, global, 2000-the present) dataset. The main conclusions included 1) GMCP generally outperformed the input datasets, ERA5-Land, GSMaP-moving vector with Kalman filter (MVK), and IMERG-Late, across various spatiotemporal scales, both in regional statistics and extreme precipitation systems; 2) GMCP significantly outperformed IMERG-Final, calibrated by gauge analysis at the monthly scale, with the improvements in correlation coefficient (CC), root-mean-square error (RMSE), and Heidke skill score (HSS) by approximately 66.67%, 39.25%, and 26.83%, respectively, from 2016 to 2020 over the contiguous United States (CONUS); 3) compared to the state-of-the-art multisource merged product with a daily gauge correction scheme, Multisource Weighted-Ensemble Precipitation (MSWEP) V2 (3-hourly and 0.1°), GMCP demonstrated the notable improvements with an approximately 20% enhancement in accurately capturing the precipitation occurrences against approximately 67?000 rain gauges over mainland China in 2016; 4) in comparison to another well-known multisource merged quasi-global daily and 0.05° precipitation product, Climate Hazards Infrared Precipitation with Stations (CHIRPS) integrating the gauge-, satellite-, and reanalysis-based precipitation estimates, GMCP also demonstrated the notable improvements at the daily scale, achieving the increases in CC, RMSE, and HSS by around 57.45%, 38.18%, and 75.76%, respectively, against approximately 67?000 rain gauges over mainland China in 2016; and 5) this framework was suitable for generating the fully global precipitation datasets at 1-hourly and 0.1° scales, significantly mitigating the inherent shortcomings of each input dataset, with GMCP demonstrating the great potential as a valuable resource for worldwide scientific research and societal applications.</div>
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