日本語
 
Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細

登録内容を編集ファイル形式で保存
 
 
ダウンロード電子メール
  Non-linear time series analysis of precipitation events using regional climate networks for Germany

Rheinwalt, A., Boers, N., Marwan, N., Kurths, J., Hoffmann, P., Gerstengarbe, F.-W., & Werner, P. C. (2016). Non-linear time series analysis of precipitation events using regional climate networks for Germany. Climate Dynamics, 46(3), 1065-1074. doi:10.1007/s00382-015-2632-z.

Item is

基本情報

表示: 非表示:
資料種別: 学術論文

ファイル

表示: ファイル
非表示: ファイル
:
6995.pdf (全文テキスト(全般)), 6MB
 
ファイルのパーマリンク:
-
ファイル名:
6995.pdf
説明:
-
閲覧制限:
非公開
MIMEタイプ / チェックサム:
application/pdf
技術的なメタデータ:
著作権日付:
-
著作権情報:
-
CCライセンス:
-

関連URL

表示:

作成者

表示:
非表示:
 作成者:
Rheinwalt, Aljoscha1, 著者              
Boers, Niklas1, 著者              
Marwan, Norbert1, 著者              
Kurths, Jürgen1, 著者              
Hoffmann, Peter1, 著者              
Gerstengarbe, Friedrich-Wilhelm1, 著者              
Werner, Peter C.1, 著者              
所属:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

内容説明

表示:
非表示:
キーワード: -
 要旨: Synchronous occurrences of heavy rainfall events and the study of their relation in time and space are of large socio-economical relevance, for instance for the agricultural and insurance sectors, but also for the general well-being of the population. In this study, the spatial synchronization structure is analyzed as a regional climate network constructed from precipitation event series. The similarity between event series is determined by the number of synchronous occurrences. We propose a novel standardization of this number that results in synchronization scores which are not biased by the number of events in the respective time series. Additionally, we introduce a new version of the network measure directionality that measures the spatial directionality of weighted links by also taking account of the effects of the spatial embedding of the network. This measure provides an estimate of heavy precipitation isochrones by pointing out directions along which rainfall events synchronize. We propose a climatological interpretation of this measure in terms of propagating fronts or event traces and confirm it for Germany by comparing our results to known atmospheric circulation patterns.

資料詳細

表示:
非表示:
言語:
 日付: 2016
 出版の状態: Finally published
 ページ: -
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1007/s00382-015-2632-z
PIKDOMAIN: Climate Impacts & Vulnerabilities - Research Domain II
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 6995
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Extremes
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Regional keyword: Germany
Organisational keyword: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Working Group: Hydroclimatic Risks
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
 学位: -

関連イベント

表示:

訴訟

表示:

Project information

表示:

出版物 1

表示:
非表示:
出版物名: Climate Dynamics
種別: 学術雑誌, SCI, Scopus, p3
 著者・編者:
所属:
出版社, 出版地: -
ページ: - 巻号: 46 (3) 通巻号: - 開始・終了ページ: 1065 - 1074 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals77