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Analysis of Surface Air Temperature Anomalies
https://ir.soken.ac.jp/records/779
https://ir.soken.ac.jp/records/77930064ad2-d330-4799-90aa-2d045eefad50
| 名前 / ファイル | ライセンス | アクション |
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要旨・審査要旨 (166.9 kB)
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本文 (20.2 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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| 公開日 | 2010-02-22 | |||||
| タイトル | ||||||
| タイトル | Analysis of Surface Air Temperature Anomalies | |||||
| タイトル | ||||||
| タイトル | Analysis of Surface Air Temperature Anomalies | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| 著者名 |
若浦, 雅嗣
× 若浦, 雅嗣 |
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| フリガナ |
ワカウラ, マサツグ
× ワカウラ, マサツグ |
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| 著者 |
WAKAURA, Masatsugu
× WAKAURA, Masatsugu |
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| 学位授与機関 | ||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||
| 学位名 | ||||||
| 学位名 | 博士（統計科学） | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 総研大甲第1041号 | |||||
| 研究科 | ||||||
| 値 | 複合科学研究科 | |||||
| 専攻 | ||||||
| 値 | 15 統計科学専攻 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2007-03-23 | |||||
| 学位授与年度 | ||||||
| 値 | 2006 | |||||
| 要旨 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | This study is analysis of anomalies of surface air temperature in Japan. The surface<br />air temperature anomalies relative to the seasoma1 variations are ofour great concern <br />from a long-term forecasting viewpoint. The result of the amalysis presents us the <br />usefu1 knowledge not oniy for the climatic amalysis but also for the prediction and the <br />weather risk management. <br /> In this paper, to begin witza we investigate seasonal periodicities of the time series <br />and show that the surface air tenrperatue has the intense seasonality and there are <br />seasonal periodicities in not only the mean but also the variance. Under the strategy of <br />detection of the yearly distinctive variablities that is the object ofthe predictio". the <br />means and variances ofthe deterministic seasonal periodicities are removed from the<br />original tenrperature data and the residuals are defined as the anomalies. <br /> The low-pass fikered anomalies represent the yearly distinctive5 variablities <br />quantitatively and analysis ofthe monthly divided dataset suggests seasonality in the <br />anomalies. Then a particular parametric form for a nonstationary autoregressive (AR) <br />model is considered to analyze seasonality in the anomalies and the new knowledge is <br />Shown. The model shows that there are seasoma1 changes in the autocorrelation of <br />surface air temperature and the daily power spectrum transformed from the coefficients <br />ofthe model clarifies the seasonal feature. Applying the model to the high-pass filtered <br />datasets clarifies the infiuence of the Japan Current on the seasonality and it is. <br />expected that the long-tenn prediction might be improved by taking the effect from the <br />Japan Current as an exogenous factor. On the other hand, applying the mOdel to the <br />pricing ofthe weather derivatives shows that we cannot neglect the seasonality in the <br />valuation ofthe weather risk in the future. <br /> Furthermore, the model is extended multivariate model. In analysis using the noise <br />contribution, the relation and causality between stations is shown and the structure of <br />the surface air ternperature in Japan is explained. The daily noise contribution <br />estimated from the Multivariate model can quantitatively grasp the clearly seasonal <br />change and the propagation of the causality, and suggests that there are the local <br />teleconnections between stations. The knowledge Will also be import factors' for the <br />prediction. | |||||
| 所蔵 | ||||||
| 値 | 有 | |||||
