Statistical Modelling in Climate Science

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F16%3A00462914" target="_blank" >RIV/67985807:_____/16:00462914 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/16:10335131

Výsledek na webu

<a href="http://ceur-ws.org/Vol-1649/102.pdf" target="_blank" >http://ceur-ws.org/Vol-1649/102.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Modelling in Climate Science

Popis výsledku v původním jazyce

When it comes to modelling in atmospheric and climate science, the two main types of models are taken into account - dynamical and statistical models. The former ones have a physical basis: they utilize discretized differential equations with a set of conditions (boundary conditions + present state as an initial condition) and model the system’s state by integrating the equations forward in time. Models of this type are currently used e.g. as a numerical weather prediction models. The statistical models are considerably different: they are not based on physical mechanisms underlying the dynamics of the modelled system, but rather derived from the analysis of past weather patterns. An example of such a statistical model based on the idea of linear inverse modelling, is examined for modelling the El Nino - Southern Oscillation phenomenon with a focus on modelling cross-scale interactions in the temporal sense. Various noise parameterizations and the possibility of using a multi-variable model is discussed among other characteristics of the statistical model. The prospect of using statistical models with low complexity as a surrogate model for statistical testing of null hypotheses is also discussed.

Název v anglickém jazyce

Statistical Modelling in Climate Science

Popis výsledku anglicky

When it comes to modelling in atmospheric and climate science, the two main types of models are taken into account - dynamical and statistical models. The former ones have a physical basis: they utilize discretized differential equations with a set of conditions (boundary conditions + present state as an initial condition) and model the system’s state by integrating the equations forward in time. Models of this type are currently used e.g. as a numerical weather prediction models. The statistical models are considerably different: they are not based on physical mechanisms underlying the dynamics of the modelled system, but rather derived from the analysis of past weather patterns. An example of such a statistical model based on the idea of linear inverse modelling, is examined for modelling the El Nino - Southern Oscillation phenomenon with a focus on modelling cross-scale interactions in the temporal sense. Various noise parameterizations and the possibility of using a multi-variable model is discussed among other characteristics of the statistical model. The prospect of using statistical models with low complexity as a surrogate model for statistical testing of null hypotheses is also discussed.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings ITAT 2016: Information Technologies - Applications and Theory

ISBN

978-1-5370-1674-0

ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

102-109

Název nakladatele

Technical University & CreateSpace Independent Publishing Platform

Místo vydání

Aachen & Charleston

Místo konání akce

Tatranské Matliare

Datum konání akce

15. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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