Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

Author:

Cao Weihua1,Nie Suping234ORCID,Ma Lijuan5,Zhao Liang6ORCID

Affiliation:

1. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China

2. Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China

3. Key Laboratory of Earth System Modeling and Prediction, China Meteorological Administration, Beijing 100081, China

4. State Key Laboratory of Severe Weather, Beijing 100081, China

5. National Climate Center, China Meteorological Administration, Beijing 100081, China

6. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Guangdong Major Project of Basic and Applied Basic Research

Strategic Priority Research Program of the Chinese Academy of Sciences

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference40 articles.

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