GMCP: A Fully Global Multisource Merging-and-Calibration Precipitation Dataset (1-Hourly, 0.1°, Global, 2000–the Present)

Abstract

Abstract 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. Significance Statement Highly accurate global gridded precipitation datasets for precipitation occurrences and volumes are essential for understanding the water, energy, and carbon cycles on Earth in the context of a changing climate. This study aimed to introduce a new fully global multisource merged precipitation dataset with high quality and resolutions of 1-hourly and 0.1° from 2000 to the present. This dataset integrated the advantages of ground gauge-, satellite-, and model-based precipitation estimates, particularly regarding precipitation occurrence, which can benefit scientific research communities and societal applications worldwide, including hydrological, climatological, meteorological, and water resource management.

Topics & Concepts