Abstract
Understanding the error distribution of satellite precipitation products is conducive to obtaining accurate precipitation data, which is a very important input parameter in hydrological models and climate models. The error characteristics of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) uncalibrated products on quasi-global land and six continents are evaluated, and the effects of latitude, elevation, and season on satellite precipitation product accuracy are analyzed. In order to be consistent with the Climate Prediction Center (CPC), the selected products are resampled at 0.5° and daily resolutions from 1 January 2015 to 31 August 2018. We find out that (1) GSMaP performs worse than IMERG mainly due to systematic errors and poor performance at high latitudes; (2) overestimation is obvious in high latitude areas of the northern hemisphere and also in areas with low rainfall intensity; (3) IMERG and GSMaP show good performance in summer and poor performance in winter; (4) where elevation is lower than 1500 m, the error metrics are highly correlated with the elevation; (5) the correlation coefficient is relatively high in areas with high rainfall, and the dispersion of satellite data and gauge data is also high. IMERG is a high-quality satellite precipitation product in the GPM era, but some uncertainties mentioned above are still worthy of attention by product developers and users.
Highlights
Precipitation is a highly variable environmental parameter of which distribution varies a lot spatially and temporally
Found that Global Satellite Mapping of Precipitation (GSMaP) has a better ability to capture the spatial distribution of summer precipitation, and its estimation of the total precipitation in the eastern area of the United States is better than that in the western are; Guo et al (2017) [12] evaluated four different satellite precipitation products’ (3B42, CMORPH, GSMaP, and PERSIANN) ability to capture the precipitation over central Asia, and found that the gauge-calibrated GSMaP performs better than others; and Prakash et al (2016) [13] found that the consistency of Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and ground-based observations is better than that of TMPA, IMERG’s estimation of precipitation in the southwest monsoon season shows notable improvements over TMPA
Our research focuses on the uncalibrated data of the two most mainstream satellite precipitation products in the Global Precipitation Measurement (GPM) era, and is aimed at analyzing their error performance in the near global scale from the aspects of latitude, season, and elevation, as well as comparative analysis of six continents
Summary
Precipitation is a highly variable environmental parameter of which distribution varies a lot spatially and temporally. Found that GSMaP has a better ability to capture the spatial distribution of summer precipitation, and its estimation of the total precipitation in the eastern area of the United States is better than that in the western are; Guo et al (2017) [12] evaluated four different satellite precipitation products’ (3B42, CMORPH, GSMaP, and PERSIANN) ability to capture the precipitation over central Asia, and found that the gauge-calibrated GSMaP performs better than others; and Prakash et al (2016) [13] found that the consistency of IMERG and ground-based observations is better than that of TMPA, IMERG’s estimation of precipitation in the southwest monsoon season shows notable improvements over TMPA in obtaining heavy rainfall over India, and IMERG helps to optimize the simulation of hydrological extreme values They found that IMERG performs to TMPA in terms of the volume of hit, missed, and false precipitation, and that IMERG underestimates the frequency of heavy rainfall in parts of northeast India; Tang et al (2016) [14] compared the TMPA and IMERG in southeast. This paper is organized as follows: in Section 2, the data and the metrics for comparing satellite products against gauge data are introduced; in Section 3, the focus is the results and discussion; in Section 4, a brief summary and conclusions are given
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