5:15 PM - 6:30 PM
[ACG36-P17] Evolution of Global Satellite Mapping of Precipitation (GSMaP) Product version 05
Keywords:Global Precipitation Measurement, Global Satellite Mapping of Precipitation
Global Satellite Mapping of Precipitation (GSMaP) has been developed as a standard product of Japan's Global Precipitation Measurement (GPM) mission. Merged satellite precipitation products such as GSMaP are particularly effective for monitoring in regions where ground-based observation is not available. The GSMaP has been continuously improved, and the product version 03 (algorithm version 6) was released in September 2014, and the product version 04 (algorithm version 7) in January 2017. Product version 05 (algorithm version 8) is scheduled to be released in May 2021. Product version 05 will be available for reprocessing from March 2020 onwards, and we are currently investigating whether it will be available for reprocessing from January 1998 onwards. In this presentation, we will introduce contents of the product version 05.
The new sensors, MHS(Metop-C) & ATMS (Suomi-NPP/ATMS, NOAA-20/ATMS), will be added to the product version 05 to improve the observation coverage of the passive microwave radiometer (PMW). In addition, the PMW precipitation algorithm will be improved to extend the estimation range to the poles. However, this does not include the application of the combined PMW-IR algorithm at this time, and only PMW sensor estimations are planned for regions of the polar to 60 degrees latitude.
We will also improve the database of precipitation physical models. We will introduce the use of longer term GPM/DPR data and the use of dual frequency information from DPR. We will also incorporate improvements to the orographic heavy rainfall estimation method (Yamamoto et al. 2017). We will improve the scattering algorithm based on frozen precipitation depths (Aonashi et al. 2021). Improvements to the ground rain gauge adjustment method (Mega et al. 2019) were developed to correct artificial patterns found in the previous version.
In addition, one of the major improvements in the product version 05 is to improve the sensor-induced heterogeneity of GSMaP. The heterogeneity often occurs at the boundary between the PMW observation area and the geostationary infrared radiometer (IR) estimation area. The heterogeneity among different types of PMWs is corrected based on Yamamoto and Kubota (2020), based upon cumulative distributions of precipitation for PMW sensors. For the heterogeneity between PMW and IR, the histogram matching method of Hirose et al. (2021) is applied. Furthermore, a gap reduction method based upon an image processing is also expected to be effective in reducing the heterogeneity between PMW and IR.
The new sensors, MHS(Metop-C) & ATMS (Suomi-NPP/ATMS, NOAA-20/ATMS), will be added to the product version 05 to improve the observation coverage of the passive microwave radiometer (PMW). In addition, the PMW precipitation algorithm will be improved to extend the estimation range to the poles. However, this does not include the application of the combined PMW-IR algorithm at this time, and only PMW sensor estimations are planned for regions of the polar to 60 degrees latitude.
We will also improve the database of precipitation physical models. We will introduce the use of longer term GPM/DPR data and the use of dual frequency information from DPR. We will also incorporate improvements to the orographic heavy rainfall estimation method (Yamamoto et al. 2017). We will improve the scattering algorithm based on frozen precipitation depths (Aonashi et al. 2021). Improvements to the ground rain gauge adjustment method (Mega et al. 2019) were developed to correct artificial patterns found in the previous version.
In addition, one of the major improvements in the product version 05 is to improve the sensor-induced heterogeneity of GSMaP. The heterogeneity often occurs at the boundary between the PMW observation area and the geostationary infrared radiometer (IR) estimation area. The heterogeneity among different types of PMWs is corrected based on Yamamoto and Kubota (2020), based upon cumulative distributions of precipitation for PMW sensors. For the heterogeneity between PMW and IR, the histogram matching method of Hirose et al. (2021) is applied. Furthermore, a gap reduction method based upon an image processing is also expected to be effective in reducing the heterogeneity between PMW and IR.