Satellite radiance data assimilation for rainfall prediction in Java Region

3rd International Symposium on LAPAN-IPB Satellite For Food Security and Environmental Monitoring 2016

地球科学;轻工业;生态环境科学

Sagita, Novvria^1,2 ; Hidayati, Rini^2 ; Hidayat, Rahmat^2 ; Gustari, Indra^1

Indonesian Agency for Meteorology Climatology and Geophysics (BMKG), Jl Angkasa I No 2 Kemayoran, Jakarta Pusat

10720, Indonesia^1

Department of Geophysics and Meteorology, Bogor Agricultural University (IPB), Gedung FMIPA Kampus IPB, Jl. Meranti Wing 19, Jawa Barat

16680, Indonesia^2

关键词: Data assimilation;    Dimensional variations;    Global forecast systems;    Initial conditions;    Quantitative precipitation forecast;    Rainfall prediction;    Surface observation data;    Weather research and forecasting;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/54/1/012037/pdf
DOI  :  10.1088/1755-1315/54/1/012037

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This study examined the influence of satellite radiance data assimilation for predicting two days of heavy rainfall in the Java region. The first case occurred from 22 to 23 on January 2015 while the second case occurred from 1 to 2 on February 2015. The analysis examined before and after data assimilation in the two cases study. The Global Forecast System (GFS) data were used as initial condition which was assimilated with several data such as surface observation data, radiance data from AMSUA sensor, radiance data from HIRS sensor, and radiance data from MHS sensor. Weather Research and Forecasting Data Assimilation (WRFDA) is a tool which is used in this study for assimilating process with Three Dimensional Variation (3D-Var) method. The Quantitative Precipitation Forecast (QPF) skill was used to evaluate influence data assimilation for rainfall prediction. The result of the study obtained different rainfall prediction with different data assimilation. In general, the surface observation data assimilation has lower QPF skill than the satellite radiance data assimilation. Even thought radiance data assimilation has slightly contribution on rainfall prediction, but it gave better accuracy on rainfall prediction for two heavy rainfall cases.