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A new method for error degree estimation in numerical weather prediction via MKDA-based ordinal regression

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タイトル: A new method for error degree estimation in numerical weather prediction via MKDA-based ordinal regression
著者: Ogawa, Takahiro 著作を一覧する
Takahashi, Shintaro 著作を一覧する
Takahashi, Sho 著作を一覧する
Haseyama, Miki 著作を一覧する
発行日: 2014年 7月22日
出版者: Springer
誌名: Eurasip journal on advances in signal processing
開始ページ: 115
出版社 DOI: 10.1186/1687-6180-2014-115
抄録: This paper presents a new method for estimating error degrees in numerical weather prediction via multiple kernel discriminant analysis (MKDA)-based ordinal regression. The proposed method tries to estimate how large prediction errors will occur in each area from known observed data. Therefore, ordinal regression based on KDA is used for estimating the prediction error degrees. Furthermore, the following points are introduced into the proposed approach. Since several meteorological elements are related to each other based on atmospheric movements, the proposed method merges such heterogeneous features in the target and neighboring areas based on a multiple kernel algorithm. This approach is based on the characteristics of actual meteorological data. Then, MKDA-based ordinal regression for estimating the prediction error degree of a target meteorological element in each area becomes feasible. Since the amount of training data obtained from known observed data becomes very large in the training stage of MKDA, the proposed method performs simple sampling of those training data to reduce the number of samples. We effectively use the remaining training data for determining the parameters of MKDA to realize successful estimation of the prediction error degree.
資料タイプ: article
URI: http://hdl.handle.net/2115/58054
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 小川 貴弘

 

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