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A new method for error degree estimation in numerical weather prediction via MKDA-based ordinal regression
This item is licensed under:Creative Commons Attribution-NonCommercial-ShareAlike 2.1 Japan
| Title: | A new method for error degree estimation in numerical weather prediction via MKDA-based ordinal regression |
| Authors: | Ogawa, Takahiro Browse this author →KAKEN DB | | Takahashi, Shintaro Browse this author | | Takahashi, Sho Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Issue Date: | 22-Jul-2014 |
| Publisher: | Springer |
| Journal Title: | Eurasip journal on advances in signal processing |
| Volume: | 2014 |
| Start Page: | 115 |
| Publisher DOI: | 10.1186/1687-6180-2014-115 |
| Abstract: | 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. |
| Rights: | http://creativecommons.org/licenses/by-nc-sa/2.1/jp/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/58054 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小川 貴弘
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