{"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":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"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":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"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
air temperature anomalies relative to the seasoma1 variations are ofour great concern
from a long-term forecasting viewpoint. The result of the amalysis presents us the
usefu1 knowledge not oniy for the climatic amalysis but also for the prediction and the
weather risk management.
 In this paper, to begin witza we investigate seasonal periodicities of the time series
and show that the surface air tenrperatue has the intense seasonality and there are
seasonal periodicities in not only the mean but also the variance. Under the strategy of
detection of the yearly distinctive variablities that is the object ofthe predictio\". the
means and variances ofthe deterministic seasonal periodicities are removed from the
original tenrperature data and the residuals are defined as the anomalies.
 The low-pass fikered anomalies represent the yearly distinctive5 variablities
quantitatively and analysis ofthe monthly divided dataset suggests seasonality in the
anomalies. Then a particular parametric form for a nonstationary autoregressive (AR)
model is considered to analyze seasonality in the anomalies and the new knowledge is
Shown. The model shows that there are seasoma1 changes in the autocorrelation of
surface air temperature and the daily power spectrum transformed from the coefficients
ofthe model clarifies the seasonal feature. Applying the model to the high-pass filtered
datasets clarifies the infiuence of the Japan Current on the seasonality and it is.
expected that the long-tenn prediction might be improved by taking the effect from the
Japan Current as an exogenous factor. On the other hand, applying the mOdel to the
pricing ofthe weather derivatives shows that we cannot neglect the seasonality in the
valuation ofthe weather risk in the future.
 Furthermore, the model is extended multivariate model. In analysis using the noise
contribution, the relation and causality between stations is shown and the structure of
the surface air ternperature in Japan is explained. The daily noise contribution
estimated from the Multivariate model can quantitatively grasp the clearly seasonal
change and the propagation of the causality, and suggests that there are the local
teleconnections between stations. The knowledge Will also be import factors' for the
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":[{"nameIdentifier":"0","nameIdentifierScheme":"WEKO"}]}]},"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"}