Open AccessComprehensive Evaluations on the Error Characteristics of the State‐of‐the‐Art Gridded Precipitation Products Over Jiangxi Province in 2019
Author(s) -
Hong Tulin,
Li Hongyi,
Chen Meiqiu
Publication year - 2021
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2021ea001787
Subject(s) - precipitation , environmental science , satellite , global precipitation measurement , climatology , meteorology , quantitative precipitation estimation , rain gauge , quantitative precipitation forecast , geology , geography , aerospace engineering , engineering
Accurate knowledge of the precipitation estimates with high quality and fine spatiotemporal resolutions is crucial to the precipitation science communities, especially over poorly gauged regions. In this study, we apply various validation metrics to comprehensively evaluate three latest model‐based and satellite‐based precipitation products, namely the fifth‐generation global atmospheric analysis data set of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), ERA5‐Land and Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG‐Final), against hourly rain gauge observations over Jiangxi province, south central China, in 2019. The main conclusions of this study include but not limited to: (a) considering the precipitation amount, both reanalysis precipitation data and satellite‐based precipitation products have similar spatial patterns and show overestimations; (b) IMERG‐Final generally outperforms ERA5 and ERA5‐Land at multiple temporal scales, especially in terms of CC, FAR and MFI; (c) ERA5 and ERA5‐Land precipitation products have similar spatiotemporal error characteristics, and ERA5‐Land performs better than ERA5; (d) in the respect of the capabilities of capturing precipitation events, the spatial characteristics of IMERG‐Final are the closest to those of rain gauges, while ERA5 and ERA5‐Land significantly overestimate the event duration and underestimate the mean event precipitation rate. These findings could help the state‐of‐the‐art model‐based and satellite‐based precipitation products improve the data quality in the future generations.