Japan Geoscience Union Meeting 2024

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG36] Satellite Earth Environment Observation

Mon. May 27, 2024 3:30 PM - 4:45 PM 105 (International Conference Hall, Makuhari Messe)

convener:Riko Oki(Japan Aerospace Exploration Agency), Yoshiaki HONDA(Center for Environmental Remote Sensing, Chiba University), Tsuneo Matsunaga(Center for Global Environmental Research and Satellite Observation Center, National Institute for Environmental Studies), Nobuhiro Takahashi(Institute for Space-Earth Environmental Research, Nagoya University), Chairperson:Misako Kachi(Earth Observation Research Center, Japan Aerospace Exploration Agency), Nobuhiro Takahashi(Institute for Space-Earth Environmental Research, Nagoya University)

4:00 PM - 4:15 PM

[ACG36-18] Long term evaluation of the latest version of the Global Satellite Mapping of Precipitation (GSMaP)

*Munehisa Yamamoto1, Takuji Kubota1 (1.Earth Observation Research Center, Japan Aerospace Exploration Agency)

Keywords:GSMaP, precipitation, satellite

The Global Satellite Mapping of Precipitation (GSMaP) provides precipitation estimation by combining observations from multiple microwave radiometers (PMW) onboard low Earth orbit satellites and infrared radiometers (IR) onboard geostationary meteorological satellites. The latest version of GSMaP (product version 05 (V05) and algorithm version 8) released in December 2021. This version contains implementation of new scheme such as rainfall normalization module among PMWs and histogram matching between PMW and IR information, extension of data provision period (January 1998 - March 2000, total more than 25 years), and several updates. Some climatology statistics from daily to monthly such as average, extreme rainfall, and drought index are also updated.
In this study, we investigated the performance of GSMaP to compare the previous version (V04) of GSMaP, some other satellite rainfall products, and rain gauges over the whole period. For example, in India, bias of monthly mean rainfall, failure of rain detection, and overestimation of each rain sample in GSMaP V05 is mitigated as compared to those in V04. bias and correlation are better than the Integrated Multi-satellite Retrievals for GPM (IMERG). An underestimation of rainfall is confirmed along the coastal regions of the Western Ghats. However, the underestimation is mitigated for the gauge-calibrated products, even in the near real-time product.