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High-resolution climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (Diva)
Troupin, C.; Machín, F.; Ouberdous, M.; Sirjacobs, D.; Barth, A.; Beckers, J.-M. (2010). High-resolution climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (Diva). J. Geophys. Res. 115(C08005): 20 pp.
In: Journal of Geophysical Research. American Geophysical Union: Richmond. ISSN 0148-0227; e-ISSN 2156-2202, meer
Peer reviewed article  

Beschikbaar in  Auteurs 
    VLIZ: Open Repository 213302 [ OMA ]


Auteurs  Top 
  • Troupin, C., meer
  • Machín, F.
  • Ouberdous, M., meer

    Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods for their construction are not able to take into account the influence of physical effects (topographic constraints, boundary conditions, advection, etc.). A high-resolution atlas for temperature and salinity is developed for the northeast Atlantic Ocean on 33 depth levels. The originality of this climatology is twofold: (1) For the data set, data are collected on all major databases and aggregated to lead to an original data collection without duplicates, richer than the World Ocean Database 2005, for the same region of interest. (2) For the method, climatological fields are constructed using the variational method Data-Interpolating Variational Analysis. The formulation of the latter allows the consideration of coastlines and bottom topography, and has a numerical cost almost independent on the number of observations. Moreover, only a few parameters, determined in an objective way, are necessary to perform an analysis. The results show overall good agreement with the widely used World Ocean Atlas, but also reveal significant improvements in coastal areas. Error maps are generated according to different theories and emphasize the importance of data coverage for the creation of such climatological fields. Automatic outlier detection is performed, and its effects on the analysis are examined. The method presented here is very general and not dependent on the region, hence it may be applied for creating other regional atlas in different zones of the global ocean.

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