Reference : Derivation of high resolution TSM data by merging geostationary and polar-orbiting sa...
Scientific congresses and symposiums : Unpublished conference/Abstract
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/160891
Derivation of high resolution TSM data by merging geostationary and polar-orbiting satellite data in the North Sea.
English
Alvera Azcarate, Aïda mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Barth, Alexander mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Vanhellemont, Quinten []
Ruddick, Kevin []
Beckers, Jean-Marie mailto [Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
9-Sep-2013
Yes
No
International
European Space Agency Living Planet Symposium
from 9-9-2013 to 13-9-2013
ESA
Edinburgh
UK
[en] Total suspended matter ; Geostationary ; Merging
[en] There is a need for high resolution ocean colour data, both in space and time, for a better assessment of the variability of these data and their influence in the environment, specially at shallow areas where factors as tides and wind play a role in their dynamics. High spatial resolution is achieved by polar-orbiting satellites, but at a low temporal resolution. The opposite is true for geostationary satellites.

In order to exploit the complementary nature of geostationary and polar data, a merging methodology has been developed to obtain a unique estimate of the North Sea Total Suspended Matter (TSM). The largest difficulty in developing a merging methodology is the correct estimation of the error covariance matrix, which can be specially complex for variables like TSM. In this work, the error covariance is not parametrized a priori using an analytical expression, but expressed using a truncated spatial EOF basis calculated by analysing MODIS data using DINEOF (Data INterpolating Empirical Orthogonal Functions). This EOF basis represents more realistically the complex variability of the TSM data sets than the parametric covariance used in most optimal interpolation applications. This EOF basis is subsequently used to merge MODIS and SEVIRI TSM data using an optimal interpolation approach.

Results for the North Sea 2009 TSM will be shown, demonstrating the possibilities of this technique. The influence of including variables like winds or tides in the analysis, through multivariate approaches, will be assessed.
Centre Interfacultaire de Recherches en Océanologie - MARE
Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy
Geocolour
Researchers
http://hdl.handle.net/2268/160891

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
alvera_ESALP2013.pdfAuthor postprint1.32 MBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.