Abstract

Performance of four satellite precipitation products, namely, the China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing technique (CMORPH), as well as 3B42 calibrated and 3B42-RT dataset, which are derived from the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA), were evaluated from daily to annual temporal scales over Beijing, using observations from 36 ground meteorological stations. Five statistical properties and three categorical metrics were used to test the results. The assessment showed that all four satellite precipitation products captured the temporal variability of precipitation. Although four satellite precipitation products captured the trend of more precipitation in the northeastern regions, all four products showed different distribution from the observations for 2001–2015 over Beijing. All precipitation products tended to overestimate moderate precipitation events and underestimate heavy precipitation events over Beijing, except for 3B42RT, which tended to overestimate most precipitation events. By comparison, the CMFD performed better than the CMORPH, 3B42 calibrated, and 3B42-RT datasets, having the higher correlation coefficient and low root mean squared difference, and mean absolute difference at all temporal scales. The average correlation coefficient of the CMFD, CMORPH, 3B42 calibrated, and 3B42-RT products for all 36 stations were 0.70, 0.60, 0.59, and 0.54 for daily precipitation and 0.78, 0.32, 0.74, and 0.44 for monthly precipitation. Overall, the CMFD was the most reliable for the Beijing region.

Highlights

  • Precipitation plays an important role in global water cycles, linking the atmosphere and the land surface, and affecting meteorology, climatology, and hydrology [1,2,3]

  • The China Meteorological Forcing Dataset (CMFD) had the highest value of CC and the lowest values of root mean squared difference (RMSD) and mean absolute difference (MAD), with average CC, RMSD, and MAD values for all 36 stations of 0.70, 4.64 mm, and 1.19 mm, respectively, followed by the Center morphing technique (CMORPH) and 3B42 calibrated datasets

  • The 3B42-RT dataset presented the lowest value of CC and the highest values of RMSD, MAD, and relative bias (RBS)

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Summary

Introduction

Precipitation plays an important role in global water cycles, linking the atmosphere and the land surface, and affecting meteorology, climatology, and hydrology [1,2,3]. Previous studies have showed that global land precipitation has increased by about 2% since the beginning of the 20th century. The distribution of regional precipitation and its variability can significantly affect flash flood hazards and regional water resources management. As one of the largest cities in the world, has experienced severe urban flooding from heavy precipitation [12,13]. Detecting spatial and temporal changes in long-term urban precipitation is one of the highest priorities in urban water resources management

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