Počet záznamů: 1
Non-Random Correlation Structures and Dimensionality Reduction in Multivariate Climate Data
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SYSNO ASEP 0435055 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Non-Random Correlation Structures and Dimensionality Reduction in Multivariate Climate Data Tvůrce(i) Vejmelka, Martin (UIVT-O) SAI, RID, ORCID
Pokorná, Lucie (UIVT-O)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCID
Hartman, David (UIVT-O) RID, SAI, ORCID
Jajcay, Nikola (UIVT-O) RID, ORCID, SAI
Paluš, Milan (UIVT-O) RID, SAI, ORCIDZdroj.dok. Climate Dynamics. - : Springer - ISSN 0930-7575
Roč. 44, 9-10 (2015), s. 2663-2682Poč.str. 20 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova climate dynamics ; sea level pressure ; surface air temperature ; principal component analysis ; Varimax Complex networks ; modes of variability Vědní obor RIV BB - Aplikovaná statistika, operační výzkum CEP GCP103/11/J068 GA ČR - Grantová agentura ČR GA13-17187S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000351459800019 EID SCOPUS 84939888879 DOI https://doi.org/10.1007/s00382-014-2244-z Anotace It is well established that the global climate is a complex phenomenon with dynamics driven by the interaction of a multitude of identifiable but intertwined subsystems. The identification, at some level, of these subsystems is an important step towards understanding climate dynamics. We present a method to determine the number of principal components representing non-random correlation structures in climate data, or components that cannot be generated by a surrogate model of independent stochastic processes replicating the auto-correlation structure of each time series. The purpose of the method is to automatically reduce the dimensionality of large climate datasets into spatially localised components suitable for further interpretation or, for example, for use as nodes in a complex network analysis of large-scale climate dynamics. We apply the method to two 2.5° resolution NCEP/NCAR reanalysis global datasets of monthly means: the sea level pressure (SLP) and the surface air temperature (SAT), and extract 60 components explaining 87 % variance and 68 components explaining 72 % variance, respectively. The obtained components are in agreement with previous results in that they recover many well-known climate modes previously identified using other approaches including regionally constrained principal component analysis. Selected SLP components are discussed in more detail with respect to their correlation with important climate indices and their relationship to other SLP and SAT components. Finally, we consider a subset of the obtained components that have not yet been explicitly identified by other authors but seem plausible in the context of regional climate observations discussed in literature. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2015
Počet záznamů: 1