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one publication added to basket [238091]

divand-1.0: n-dimensional variational data analysis for ocean observations Barth, A.; Beckers, J.-M.; Troupin, C.; Alvera-Azcárate, A.; Vandenbulcke, L. (2014). divand-1.0: n-dimensional variational data analysis for ocean observations. Geosci. Model Dev. 7(1): 225-241. https://dx.doi.org/10.5194/gmd-7-225-2014 In: Geoscientific Model Development. Copernicus Publications: Göttingen. ISSN 1991-959X; e-ISSN 1991-9603, meer

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VLIZ: Open access 257747 [ download pdf ]

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Barth, A., meer Beckers, J.-M., meer Troupin, C., meer	Alvera-Azcárate, A., meer Vandenbulcke, L., meer

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Abstract

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 two-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 three-dimensional analysis (longitude, latitude and time) compared to two-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 General Public Licence (GPL) (http://modb.oce.ulg.ac.be/mediawiki/index.php/divand).

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Datasets (3)

Barth, A.; Herman, P.M.J.; (2018): Neural network modelling of Baltic zooplankton abundances. Marine Data Archive, meer Beauchard, O.; Troupin, C.; (2018): Distribution of benthic macroinvertebrate living modes in European seas. Marine Data Archive., meer Beauchard, O.; Troupin, C.; (2018): Distribution of fish living modes in European seas. Marine Data Archive., meer

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