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Tracing the Error Sources of Global Satellite Mapping of Precipitation for GPM (GPM-GSMaP) Over the Tibetan Plateau, China

Ziwei Zhu,B. Yong,L. Ke,Guoqing Wang,L. Ren,Xi Chen

Published 2018 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ABSTRACT

This study focuses on tracing the error sources of the latest global satellite mapping of precipitation for global precipitation measurement (GPM-GSMaP) over the Tibetan Plateau by the corresponding satellite information flag provided by the retrieval system. We investigated the characteristics of GPM-GSMaP estimates from 11 passive microwave sensors (including 7 imagers and 4 sounders) and 1 geo-infrared data source integrating the morphing technique (i.e., morph). Assessment results show that imagers are generally superior to sounders and morph. Among the seven types of imagers, the Tropical Rainfall Measuring Mission Microwave Imager, which rarely underestimates heavy rain events, exhibits the best performance with total bias of −8.8%. In contrast, the worst performance is found in the special sensor microwave imager/sounder on defense meteorological satellite program (DMSP)-F19 with the largest bias of approximately 39.8%. The GPM Microwave Imager shows acceptable accuracy in detecting heavy rain, but it tends to overestimate light and moderate rain. Compared to the imagers, all the sounders produced larger biases (>120%), although they show lower probability of missing rainfall events. Particularly, the AMSU-A/MHS (i.e., Advanced Microwave Sounding Unit-A; Microwave Humidity Sounder) on national oceanic and atmospheric administration (NOAA) satellites (i.e., NOAA-18 and −19) display higher biases than those on meteorological operational satellite (MetOp) satellites (i.e., MetOp-A and −B), with dramatic overestimation up to 168.4%. Additionally, the error components of morph show a similar pattern to those of sounders, except for substantial underestimation of heavy rain events. The approach of tracing error reported here can enable both GPM-GSMaP users and developers to better understand the error sources of precipitation retrievals.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Publication date

    2018-05-15

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/JSTARS.2018.2825336
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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