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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-2017-44
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
27 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).
The Onsager–Machlup functional for data assimilation
Nozomi Sugiura Research Institute for Global Change, JAMSTEC, Yokosuka, Japan
Abstract. When taking the model error into account in data assimilation, one needs to evaluate the prior distribution represented by the Onsager–Machlup functional. Numerical experiments have clarified how one should put it into discrete form in the maximum a posteriori estimates and in the assignment of probability to each path. In the maximum a posteriori estimates, the divergence of the drift term is essential, but for the path probability assignments in combination with the Euler time-discretization scheme, it is not necessary. The latter property will help simplify the implementation of nonlinear data assimilation for large systems with sampling methods such as the Metropolis-adjusted Langevin algorithm.

Citation: Sugiura, N.: The Onsager–Machlup functional for data assimilation, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2017-44, in review, 2017.
Nozomi Sugiura
Nozomi Sugiura
Nozomi Sugiura

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Short summary
When you ask how often an event happens, you should be careful how many states the event represents. The event the sum of the pips of two dice being 2 is rarer than being 6. This should be taken into account in finding the most probable event. With this point in mind, this paper clarifies how to set the objective function to be minimized.
When you ask how often an event happens, you should be careful how many states the event...
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