Computational Geosciences

, Volume 14, Issue 1, pp 55–64 | Cite as

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

Application to Tanganyika Lake data set
  • Antti SorjamaaEmail author
  • Amaury Lendasse
  • Yves Cornet
  • Eric Deleersnijder
Original paper


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.


Missing value problem Empirical orthogonal functions EOF Selection of singular values Tanganyika Lake 


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Antti Sorjamaa
    • 1
    Email author
  • Amaury Lendasse
    • 1
  • Yves Cornet
    • 2
  • Eric Deleersnijder
    • 3
  1. 1.Adaptive Informatics Research CentreHelsinki University of TechnologyEspooFinland
  2. 2.Unit of GeomaticsUniversity of LiegeLiegeBelgium
  3. 3.Louvain School of Engineering, Centre for Systems Engineering and Applied Mechanics (CESAME)Universite catholique de LouvainLouvain-la-NeuveBelgium

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