Non-Linear Dependence and Teleconnections in Climate Data: Sources, Relevance, Nonstationarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00393071" target="_blank" >RIV/67985807:_____/14:00393071 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68378289:_____/14:00393071

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s00382-013-1780-2" target="_blank" >http://dx.doi.org/10.1007/s00382-013-1780-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00382-013-1780-2" target="_blank" >10.1007/s00382-013-1780-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-Linear Dependence and Teleconnections in Climate Data: Sources, Relevance, Nonstationarity

Popis výsledku v původním jazyce

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation.The use of linear Pearson?s correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patte

Název v anglickém jazyce

Non-Linear Dependence and Teleconnections in Climate Data: Sources, Relevance, Nonstationarity

Popis výsledku anglicky

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation.The use of linear Pearson?s correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patte

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GCP103%2F11%2FJ068" target="_blank" >GCP103/11/J068: Interakce, přenos informace a složité struktury v dynamice měnícího se klimatu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Climate Dynamics

ISSN

0930-7575

e-ISSN

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Svazek periodika

42

Číslo periodika v rámci svazku

7-8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1873-1886

Kód UT WoS článku

000334068100011

EID výsledku v databázi Scopus

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