[en] An empirical orthogonal function–based technique called Data Interpolating Empirical Orthogonal Functions (DINEOF) is used in a multivariate approach to reconstruct missing data. Sea surface temperature (SST), chlorophyll a concentration, and QuikSCAT winds are used to assess the benefit of a multivariate reconstruction. In particular, the combination of SST plus chlorophyll, SST plus lagged SST plus chlorophyll, and SST plus lagged winds have been studied. To assess the quality of the reconstructions, the reconstructed SST and winds have been compared to in situ data. The combination of SST plus chlorophyll, as well as SST plus lagged SST plus chlorophyll, significantly improves the results obtained by the reconstruction of SST alone. All the experiments correctly represent the SST, and an upwelling/downwelling event in the West Florida Shelf reproduced by the reconstructed data is studied.
Research center :
Centre Interfacultaire de Recherches en Océanologie - MARE - GHER
Disciplines :
Earth sciences & physical geography
Author, co-author :
Alvera Azcarate, Aïda ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER)
Barth, Alexander ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Océanographie physique
Beckers, Jean-Marie ; Université de Liège - ULiège > Département d'astrophys., géophysique et océanographie (AGO) > Océanographie physique
Weisberg, Robert H; University of South Florida > College of Marine Science
Language :
English
Title :
Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields
Publication date :
2007
Journal title :
Journal of Geophysical Research. Oceans
ISSN :
2169-9275
eISSN :
2169-9291
Publisher :
American Geophysical Union (AGU), Washington, United States - District of Columbia
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