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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/npg-2017-46
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
07 Aug 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Nonlinear Processes in Geophysics (NPG).
Optimal Transport for Variational Data Assimilation
Nelson Feyeux, Arthur Vidard, and Maëlle Nodet Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Abstract. Variational data assimilation methods are designed to estimate an unknown initial condition of a model using observations. To do so, one needs to compare model outputs and observations. This is generally performed using Euclidean distances. This paper investigates another distance choice: the Wasserstein distance, stemming from optimal transport theory. We develop a variational data assimilation method using this distance. We investigate the impact of the scalar product, the 5 gradient choice as well as the minimization algorithm. With appropriate choices, we show successful results on preliminary experiments. Optimal-transport-based optimization seems to be promising to preserve the geometrical properties of the estimated initial condition.

Citation: Feyeux, N., Vidard, A., and Nodet, M.: Optimal Transport for Variational Data Assimilation, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2017-46, in review, 2017.
Nelson Feyeux et al.
Nelson Feyeux et al.
Nelson Feyeux et al.

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