1 National Center for Hydro – Meteorological Forecasting; email@example.com; firstname.lastname@example.org; email@example.com
2 Japan Meteorological Business Support Center, Japan; k–firstname.lastname@example.org
3 Atmosphere and Ocean Research Institute, University of Tokyo, Japan; email@example.com
4 Meteorological Research Institute, Japan Meteorological Agency, Japan; ksaito@mri–jam.go.jp
5 Aero Meteorological Observatory; firstname.lastname@example.org
The GSMaP Rainfall (Global Satellite Mapping of Precipitation) data (GSMaP_NOW and GSMaP_MVK) have been used for precipitation analysis at Vietnamese National Center for Hydro–Meteorological Forecasting (NCHMF) since October 2019. This study verified the quality of rainfall estimates of GSMaP_NOW, GSMaP _MVK and Himawari–8 based on 6 hourly rain gauge data from 184 SYNOP stations for a 4–month period from October 2019 to January 2020. The results show that GSMaP_MVK has the best rainfall estimate among the three data types in terms of RMSE, correlation and other categorical statistics except the probabilty of detection (POD). GSMaP_NOW was better than Himawari–8 for RMSE, correlation, and flase alarm rate, whilethe threat scores of GSMaP_NOW and Himawari–8 were in the same level. Himawari–8 tended to overestimate intense rains, and its bias scores were very large. This overestimation is significant when the cloud top temperature of prerecipitation system is very low. GSMaP_NOW can be used in parallel with Himawari–8 rainfall estimates to provide realtime information to the forecasters in forecasting and warning on the heavy rainfall, flash flood and landslide.
Cite this paper
Hung, M.K.; Saito, K.; Khiem, M.V.; Tien, D.D.; Hung, N.V. Application of GSMaP Satellite data in precipitation estimation and nowcasting: evaluations for October 2019 to January 2020 period for Vietnam. VN J. Hydrometeorol. 2020, 5, 80-94.
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