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 A Fast Non-Empirical Tropical Cyclone Identification Method
Tác giả hoặc Nhóm tác giả: Norihiko Sugimoto, Minh Tuan Pham, Kanta Tachibana, Tomohiro Yoshikawa, and Takeshi Furuhashi
Nơi đăng: Springer-Verlag US
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; Số: 978-0-387-09409-0;Từ->đến trang: 251-263;Năm: 2008
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
We propose a high speed non-empirical method to detect centers of tropical cyclones, which is useful to identify tropical cyclones in huge climatology data. In this method, centers of tropical cyclones are detected automatically by iteration of streamline in down-stream direction from some initial positions. We also bend the path of streamline successively to converge on the center of tropical cyclone rapidly. Since this method is free from empirical conditions used in the conventional method, the accuracy is independent of these conditions. Moreover, because the proposed method does not need to check these at all grid points, computational cost is significantly reduced. We compare the accuracy and effectiveness of the method with those of the conventional one for tropical cyclone identification task in observational data. Our method could find almost all tropical cyclones, some of which were not identified by the conventional method. This method will be useful for future huge climatology data, since computational cost does not depend on the number of grid points.
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ABSTRACT
We propose a high speed non-empirical method to detect centers of tropical cyclones, which is useful to identify tropical cyclones in huge climatology data. In this method, centers of tropical cyclones are detected automatically by iteration of streamline in down-stream direction from some initial positions. We also bend the path of streamline successively to converge on the center of tropical cyclone rapidly. Since this method is free from empirical conditions used in the conventional method, the accuracy is independent of these conditions. Moreover, because the proposed method does not need to check these at all grid points, computational cost is significantly reduced. We compare the accuracy and effectiveness of the method with those of the conventional one for tropical cyclone identification task in observational data. Our method could find almost all tropical cyclones, some of which were not identified by the conventional method. This method will be useful for future huge climatology data, since computational cost does not depend on the number of grid points.
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