| Item type |
学位論文 / Thesis or Dissertation(1) |
| 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
|
| フリガナ |
ワカウラ, マサツグ
|
| 著者 |
WAKAURA, Masatsugu
|
| 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 |
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|
値 |
有 |
要旨・審査要旨 (166.9 kB)
