Abstract
As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.
Highlights
Precipitation is one of the most important components of atmospheric, hydrological, and energy cycles [1,2]
Quasiglobal satellite-based precipitation products (SPPs) with high spatiotemporal resolutions have been available to the public; these SPPs include Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) [4], precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) [5], Climate Prediction Center (CPC) morphing method (CMORPH) [6], global satellite mapping of precipitation (GSMaP) [7], and integrated multisatellite retrievals for Global Precipitation Measurement (GPM) (IMERG) [4]
Considering that the available integrated multisatellite retrievals for GPM (IMERG)-E, IMERG-L, and IMERG-F SPPs have different time periods, this study selected overlapping time periods (1 June 2014 to 31 December 2018) as the evaluation period for IMERG SPPs, and the evaluation was conducted in terms of statistical indices at multiple temporal scales, precipitation error distribution, and precipitation frequency distribution
Summary
Precipitation is one of the most important components of atmospheric, hydrological, and energy cycles [1,2]. Quasiglobal satellite-based precipitation products (SPPs) with high spatiotemporal resolutions have been available to the public; these SPPs include Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) [4], precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) [5], Climate Prediction Center (CPC) morphing method (CMORPH) [6], global satellite mapping of precipitation (GSMaP) [7], and integrated multisatellite retrievals for Global Precipitation Measurement (GPM) (IMERG) [4].
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