정지기상위성자료를 이용한 중규모 바람장 산출 알고리즘 최적화

Abstract

The Atmospheric motion vectors (AMVs) derived using infrared (IR) channel imagery of geostationary satellites have been utilized widely for real-time weather analysis and data assimilation into global numerical prediction model. As the horizontal resolution of sensors on-board satellites gets higher, it becomes possible to identify atmospheric motions induced by convective clouds (meso-β meso-γ scales). The National Institute of Meteorological Research (NIMR) developed the high resolution visible (HRV) AMV algorithm to detect mesoscale atmospheric motions including ageostrophic flows. To retrieve atmospheric motions smaller than meso-β scale effectively, the target size is reduced and the visible channel imagery of geostationary satellite with 1 km resolution is used. For the accurate AMVs, optimal conditions are decided by investigating sensitivity of algorithm to target selection and correction method of height assignment. The results show that the optimal conditions are target size of 32km x 32 km, the grid interval as same as target size, and the optimal target selection method. The HRV AMVs derived with these conditions depict more effectively tropical cyclone OMAIIS than IR AMVs and the mean speed of HRV AMVs in OMAIS is slightly faster than that of IR AMVs. Optimized mesoscale AMVs are derived for 6 months (Feb. 2010-Jun. 2010) and validated with radiosonde observations, which indicated NIMRs HRV AMV algorithm can retrieve successfully mesoscale atmospheric motions.