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

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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.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès restreint
Publication date 2010
Language Anglais
Journal information "Computational Geosciences : modeling, simulation and data analysis" - Vol. 14, no. 1, p. 55-64 (2010)
Peer reviewed yes
Publisher Springer (Dordrecht)
issn 1420-0597
e-issn 1573-1499
Publication status Publié
Affiliation UCL - SST/IMMC - Institute of Mechanics, Materials and Civil Engineering
Keywords Missing value problem ; Empirical orthogonal functions ; EOF ; Selection of singular values ; Tanganyika Lake
Links
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Bibliographic reference 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)
Permanent URL http://hdl.handle.net/2078.1/34271