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Extreme precipitation monitoring capability of the multi-satellite jointly retrieval precipitation products of Global Precipitation Measurement（GPM） mission

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

    College of Hydrology and Water resources, Hohai University, Nanjing 210098, China

  • Email：dingmz1997@163.com
  • Introduction：丁明泽，研究方向为GIS在水文气象中的应用。E-mail： dingmz1997@163.com
DING Mingze12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

    College of Hydrology and Water resources, Hohai University, Nanjing 210098, China

  • Email：yongbin@hhu.edu.cn
  • Introduction：雍斌，研究方向为GIS在水文气象中的应用。E-mail： yongbin@hhu.edu.cn
YONG Bin12*,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China

YANG Zekang3

resumen

This study aims to comprehensively evaluate the four sets of pure satellite precipitation data of IMERG and GSMaP under Global Precipitation Measurement (GPM). The evaluation content is divided into three aspects: extreme precipitation index, extreme precipitation detection ability, and accuracy evaluation of extreme precipitation events with different durations.Based on the grid data set of automatic stations and CMORPH, the study area is divided into three regions according to the threshold of extreme precipitation events. Five evaluation indexes, namely, CC, BIAS, RMSE, POD, and FAR, are used to quantitatively study the performance of satellite precipitation in extreme precipitation. The precipitation products of IMERG and GSMaP are affected by factors such as terrain, precipitation intensity, and inversion algorithm, showing similar error characteristics and obvious accuracy differences. The correction of similar error characteristics will be the focus and direction of extreme precipitation inversion in the future. The discussion of accuracy difference can provide a reference for the improvement of the precipitation satellite inversion algorithm. (1) In the RX1 extreme precipitation index, IMERG and GSMaP data are obviously overestimated in the complex terrain area affected by complex terrain and underestimated in other areas. In the R95pTOT index, four sets of satellite data perform well and have a high correlation with ground-based datasets. (2) In terms of detection capability of extreme precipitation events, the performance of four pure satellite products in Northeast China is better than that of other regions. GSMaP performs better than does IMERG data with a lower false alarm rate, but the retrieval accuracy for extreme precipitation is low. (3) In the accuracy evaluation of extreme precipitation events with different durations, IMERG and GSMaP satellite precipitation products have better performance and higher precision in long-term extreme precipitation events. For extreme daily precipitation events, the error of satellite precipitation products under high rain intensity is very obvious, which is much higher than that of complex terrain on the accuracy of satellite precipitation retrieval, resulting in the performance of satellite precipitation in complex terrain area Ⅲ is better than other regions. Overall, the IMERG products have the better ability to monitor extreme precipitation in the study area than GSMaP products, and IMERG_Late data performs best. The retrieval error of extreme precipitation from IMERG and GSMaP satellite precipitation products has obvious regional characteristics in China, and the error characteristic of underestimation of high rainfall intensity is significant. The four types of satellite precipitation products can show the extreme precipitation region characteristics in study area but underestimate the precipitation in most parts of the study area. The error correction of satellite precipitation products for rainfall intensity remains one of the key and difficult points in future extreme precipitation retrieval.

palabra clave

remote sensing;IMERG;GSMaP;satellite precipitation;extreme precipitation;GPM;error characteristics

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