How well do 13 satellite-derived precipitation products capture the temporal and spatial variations of precipitation over Chinese mainland?

ABSTRACT

Satellite observations of precipitation have greatly improved our understanding of its temporal and spatial distribution. In view of the high spatiotemporal heterogeneity and severely skewed distribution characteristics of precipitation, it is necessary and of considerable application value to understand the ability of satellite-derived precipitation products (SPPs) to characterize the variations of precipitation in the time and space dimensions separately. However, to date, research concerning this is scarce. In this study, we explore the ability of 13 SPPs to characterize the temporal and spatial variations of precipitation based on observations from more than 2400 meteorological stations in Chinese mainland from 2001 to 2018. The results show that (1) SPPs tend to perform better in identifying the temporal than the spatial variation of precipitation. Most SPPs can reliably monitor the temporal and spatial variation of monthly and seasonal precipitation in Chinese mainland, although the identification of the spatial variation of daily precipitation involves larger uncertainty; (2) CMORPH-B, IMERG-F, 3B42, and MSWEP generally had better performance in identifying the temporal and spatial variations of precipitation, while PERSIANN and GSMaP-N performed poorly. However, no single SPP outperformed others in all scenarios; (3) with respect to monitoring the temporal variation of daily precipitation, SPPs performed better in southern China than in the north, with three-quarters and half of median KGE values above 0.5 In comparison, no significant spatial difference was observed in their ability to monitor the spatial variation of daily precipitation; and (4) SPPs had large uncertainties in capturing the temporal and spatial variations of precipitation in winter, while they performed best in identifying the temporal and spatial variations of daily precipitation in summer. The results of this paper provide an important reference for data users needing to select suitable SPPs and for satellite developers desiring to further improve data quality.

KEYWORDS:

• Satellite-derived precipitation products
• uncertainty analysis
• temporal variation
• spatial variation

Acknowledgements

This work was supported by the Dabieshan National Observation and Research Field Station of Forest Ecosystem at Henan, the High Resolution Satellite Project of the State Administration of Science, Technology and Industry for National Defense of PRC [80Y50G19-9001-22/23], the Natural Science Foundation of Henan Province [222300420419], and the Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake area [DTH Key Lab.2022-02].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2023.2277165

Additional information

Funding

This work was supported by the High Resolution Satellite Project of the State Administration of Science, Technology and Industry for National Defense of PRC [80Y50G19-9001-22/23], the Natural Science Foundation of Henan Province [222300420419], and the Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake area [DTH Key Lab.2022-02].