Reference : divand-1.0: n-dimensional variational data analysis for ocean observations
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/160913
divand-1.0: n-dimensional variational data analysis for ocean observations
English
[en] divand
Barth, Alexander mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Beckers, Jean-Marie mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Troupin, Charles mailto [IMEDEA > > > >]
Alvera Azcarate, Aïda mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Vandenbulcke, Luc mailto [University of Porto > CIIMAR > > >]
2014
Geoscientific Model Development
Copernicus GmbH
7
225-241
Yes (verified by ORBi)
International
1991-959X
1991-9603
[en] data analysis ; oceanography ; variational analysis ; DIVA
[en] A tool for multidimensional variational analysis (divand) is presented. It allows the interpolation and analysis of observations on curvilinear orthogonal grids in an arbitrary high dimensional space by minimizing a cost function. This cost function penalizes the deviation from the observations, the deviation from a first guess and abruptly varying fields based on a given correlation length (potentially varying in space and time). Additional constraints can be added to this cost function such as an advection constraint which forces the analysed field to align with the ocean current. The method decouples naturally disconnected areas based on topography and topology. This is useful in oceanography where disconnected water masses often have different physical properties. Individual elements of the a priori and a posteriori error covariance matrix can also be computed, in particular expected error variances of the analysis. A multidimensional approach (as opposed to stacking 2-dimensional analysis) has the benefit of providing a smooth analysis in all dimensions, although the computational cost is increased.

Primal (problem solved in the grid space) and dual formulations (problem solved in the observational space) are implemented using either direct solvers (based on Cholesky factorization) or iterative solvers (conjugate gradient method). In most applications the primal formulation with the direct solver is the fastest, especially if an a posteriori error estimate is needed. However, for correlated observation errors the dual formulation with an iterative solver is more efficient.

The method is tested by using pseudo observations from a global model. The distribution of the observations is based on the position of the ARGO floats. The benefit of the 3-dimensional analysis (longitude, latitude and time) compared to 2-dimensional analysis (longitude and latitude) and the role of the advection constraint are highlighted. The tool divand is free software, and is distributed under the terms of the GPL license (http://modb.oce.ulg.ac.be/mediawiki/index.php/divand).
GHER
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy ; Commission européenne : Direction générale des Affaires maritimes et de la Pêche ; CECI
PREDANTAR, EMODNET Chemistry 2, SeaDataNet II
Researchers
http://hdl.handle.net/2268/160913
10.5194/gmd-7-225-2014
http://www.geosci-model-dev.net/7/225/2014/
Code available at http://modb.oce.ulg.ac.be/mediawiki/index.php/Divand
FP7 ; 283607 - SEADATANET II - SeaDataNet II: Pan-European infrastructure for ocean and marine data management

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