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An improved methodology for filling missing values in spatiotemporal climate data set

Onglets principaux

Sorjamaa, Antti Lendasse, Amaury Cornet, Yves Deleersnijder, Eric [UCL]

In this paper, an improved methodology for the determination of missing values in a spatiotemporal database is presented. This methodology performs denoising projection in order to accurately fill the missing values in the database. The improved methodology is called empirical orthogonal functions (EOF) pruning, and it is based on an original linear projection method called empirical orthogonal functions (EOF). The experiments demonstrate the performance of the improved methodology and present a comparison with the original EOF and with a widely used optimal interpolation method called objective analysis.

Type de document Article de périodique (Journal article) – Article de recherche
Type d'accès Accès restreint
Année de publication 2010
Langue Anglais
Information sur le périodique "Computational Geosciences : modeling, simulation and data analysis" - Vol. 14, no. 1, p. 55-64 (2010)
Peer reviewed oui
Editeur Springer (Dordrecht)
issn 1420-0597
e-issn 1573-1499
Statut de la publication Publié
Affiliations UCL - SST/IMMC - Institute of Mechanics, Materials and Civil Engineering
Mots-clés Missing value problem ; Empirical orthogonal functions ; EOF ; Selection of singular values ; Tanganyika Lake
Liens
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Référence bibliographique Sorjamaa, Antti ; Lendasse, Amaury ; Cornet, Yves ; Deleersnijder, Eric. An improved methodology for filling missing values in spatiotemporal climate data set. In: Computational Geosciences : modeling, simulation and data analysis, Vol. 14, no. 1, p. 55-64 (2010)
Permalien http://hdl.handle.net/2078.1/34271