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Reconstruction of Total Suspended Matter data over the North Sea using DINEOF: use of the Gaussian anamorphosis transformation
Alvera-Azcárate, A.; Neukermans, G.; Barth, A.; Ruddick, K.; Beckers, J.-M. (2012). Reconstruction of Total Suspended Matter data over the North Sea using DINEOF: use of the Gaussian anamorphosis transformation, in: 44th international Liège colloquium on ocean dynamics "Remote sensing of colour, temperature and salinity – new challenges and opportunities" - May 7-11, 2012. pp. 1
In: (2012). 44th international Liège colloquium on ocean dynamics "Remote sensing of colour, temperature and salinity – new challenges and opportunities" - May 7-11, 2012. GHER, Université de Liège: Liège. 126 pp., more

Available in Authors 
    VLIZ: Open Repository 237311 [ OMA ]

Keyword
    Marine

Authors  Top 
  • Alvera-Azcárate, A., more
  • Neukermans, G., more
  • Barth, A., more
  • Ruddick, K., more
  • Beckers, J.-M., more

Abstract
    Total Suspended Matter (TSM) from the SEVIRI sensor in the North Sea will be analysed using DINEOF (Data INterpolating Empirical Orthogonal Functions), an EOFbased technique to reconstruct missing data. The information needed to reconstruct the missing data is computed internally based on a truncated EOF basis, so no assumptions about the statistics of the data have to be made.DINEOF uses the mean and covariance of the original data to calculate the EOF basis. If the data are normally distributed, then the probability density distribution can be completely described by their mean and the eigenvectors of the covariance matrix (the EOFs). Variables such as TSM, however, do not have a Gaussian distribution, since TSM is never smaller than zero. DINEOF typically does not take this into account. To overcome this, a logarithmic transformation is usually performed to non-Gaussian variables, although the exponential transformation needed to retrieve the original variable units after using DINEOF leads sometimes to unrealistic high values in the reconstruction. An empirical transformation, which allows to obtain a normally distributed variable based solely on the data themselves, will be applied. This procedure, called Gaussian anamorphosis, is sometimes used in data assimilation.A Gaussian anamorphosis transformation will be applied to the TSM data of the North Sea prior to their reconstruction. The high spatial and temporal dynamics of the gapfree geostationary TSM data set will be analysed, focusing on tidal dynamics and sub-daily variability.

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