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 High speed non-empirical cyclone detection method
Tác giả hoặc Nhóm tác giả: Norihiko Sugimoto, Minh Tuan Pham, Kanta Tachibana, Ryo Mizuta, Tomohiro Yoshikawa and Takeshi Furuhashi
Nơi đăng: 8th EMS Annual Meeting, 7th European Conference on Applications of Meteorology (EMS2008)
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; Số: 5;Từ->đến trang: EMS2008-A-00325;Năm: 2008
Lĩnh vực: Chưa xác định; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
We propose a high speed non-empirical method to detect centers of vortices, which isuseful to identify cyclones in huge climatology data. In this method, centers of vorticesare detected automatically by iteration of streamline in down-stream directionfrom some initial positions. We also bend the path of streamline successively to convergeon the center of vortex rapidly. Since this method is independent of empiricalconditions used in the conventional method, one can be free from tuning these conditions.Moreover, because the proposed method does not need to check these at allgrid points, computational cost is significantly reduced. First, we compare the accuracyand effectiveness of the method with those of the conventional one for tropicalcyclone identification task in observational data. Our method could find almost alltropical cyclones, some of which were not identified by the conventional method. Atpresent we apply this method to huge climatology data to check the accuracy. It willbe thought that computational cost does not depend on the number of grid points.For future, our method would be useful to study climatology of cyclones or tropicalcyclones in warmer climate.
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ABSTRACT
We propose a high speed non-empirical method to detect centers of vortices, which isuseful to identify cyclones in huge climatology data. In this method, centers of vorticesare detected automatically by iteration of streamline in down-stream directionfrom some initial positions. We also bend the path of streamline successively to convergeon the center of vortex rapidly. Since this method is independent of empiricalconditions used in the conventional method, one can be free from tuning these conditions.Moreover, because the proposed method does not need to check these at allgrid points, computational cost is significantly reduced. First, we compare the accuracyand effectiveness of the method with those of the conventional one for tropicalcyclone identification task in observational data. Our method could find almost alltropical cyclones, some of which were not identified by the conventional method. Atpresent we apply this method to huge climatology data to check the accuracy. It willbe thought that computational cost does not depend on the number of grid points.For future, our method would be useful to study climatology of cyclones or tropicalcyclones in warmer climate.
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