{"created":"2023-06-20T13:20:44.576108+00:00","id":779,"links":{},"metadata":{"_buckets":{"deposit":"1cfd6021-0b14-4053-9dbf-0167c7dc0b64"},"_deposit":{"created_by":1,"id":"779","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"779"},"status":"published"},"_oai":{"id":"oai:ir.soken.ac.jp:00000779","sets":["2:429:17"]},"author_link":["0","0","0"],"item_1_creator_2":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"若浦, 雅嗣"}],"nameIdentifiers":[{}]}]},"item_1_creator_3":{"attribute_name":"フリガナ","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"ワカウラ, マサツグ"}],"nameIdentifiers":[{}]}]},"item_1_date_granted_11":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2007-03-23"}]},"item_1_degree_grantor_5":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_name":"総合研究大学院大学"}]}]},"item_1_degree_name_6":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士（統計科学）"}]},"item_1_description_12":{"attribute_name":"要旨","attribute_value_mlt":[{"subitem_description":"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.","subitem_description_type":"Other"}]},"item_1_description_7":{"attribute_name":"学位記番号","attribute_value_mlt":[{"subitem_description":"総研大甲第1041号","subitem_description_type":"Other"}]},"item_1_select_14":{"attribute_name":"所蔵","attribute_value_mlt":[{"subitem_select_item":"有"}]},"item_1_select_8":{"attribute_name":"研究科","attribute_value_mlt":[{"subitem_select_item":"複合科学研究科"}]},"item_1_select_9":{"attribute_name":"専攻","attribute_value_mlt":[{"subitem_select_item":"15 統計科学専攻"}]},"item_1_text_10":{"attribute_name":"学位授与年度","attribute_value_mlt":[{"subitem_text_value":"2006"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"WAKAURA, Masatsugu","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-02-17"}],"displaytype":"simple","filename":"甲1041_要旨.pdf","filesize":[{"value":"166.9 kB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"要旨・審査要旨","url":"https://ir.soken.ac.jp/record/779/files/甲1041_要旨.pdf"},"version_id":"c3c21931-cade-4e36-a403-a6e46f83b064"},{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2016-02-17"}],"displaytype":"simple","filename":"甲1041_本文.pdf","filesize":[{"value":"20.2 MB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"本文","url":"https://ir.soken.ac.jp/record/779/files/甲1041_本文.pdf"},"version_id":"c26d26bc-e4d6-40da-9d5e-b1b44405177e"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"Analysis of Surface Air Temperature Anomalies","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Analysis of Surface Air Temperature Anomalies"},{"subitem_title":"Analysis of Surface Air Temperature Anomalies","subitem_title_language":"en"}]},"item_type_id":"1","owner":"1","path":["17"],"pubdate":{"attribute_name":"公開日","attribute_value":"2010-02-22"},"publish_date":"2010-02-22","publish_status":"0","recid":"779","relation_version_is_last":true,"title":["Analysis of Surface Air Temperature Anomalies"],"weko_creator_id":"1","weko_shared_id":1},"updated":"2023-06-20T15:59:59.542603+00:00"}