Articles | Volume 25, issue 1
Nonlin. Processes Geophys., 25, 55–66, 2018
Nonlin. Processes Geophys., 25, 55–66, 2018

Research article 30 Jan 2018

Research article | 30 Jan 2018

Optimal transport for variational data assimilation

Nelson Feyeux et al.

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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, SIAM, 306 pp., 2016.
Benamou, J.-D. and Brenier, Y.: A computational fluid mechanics solution to the Monge-Kantorovich mass transfer problem, Numer. Math., 84, 375–393, 2000.
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 140, 1521–1535,, 2014.
Bonneel, N., Van De Panne, M., Paris, S., and Heidrich, W.: Displacement interpolation using Lagrangian mass transport, in: ACM Transactions on Graphics (TOG), 30, No. 158, ACM, 2011.
Brenier, Y., Frisch, U., Hénon, M., Loeper, G., Matarrese, S., Mohayaee, R., and Sobolevskiĭ, A.: Reconstruction of the early Universe as a convex optimization problem, Mon. Not. R. Astron. Soc., 346, 501–524, 2003.
Short summary
In geophysics, numerical models are generally initialized through so-called data assimilation methods. They require computation of a distance between model fields and physical observations. The most common choice is the Euclidian distance. However, due to its local nature it is not well suited for capturing position errors. This papers investigates theoretical aspects of the use of the optimal transport-based Wasserstein distance in this context and shows that it is able to capture such errors.