4 Kalman Filters for Nonlinear Systems

Abstract. The finite-size ensemble Kalman filter (EnKF-N) is an ensemble Kalman filter (EnKF) which, in perfect model condition, does not require inflation because it partially accounts for the ensemble sampling errors. For the Lorenz '63 and '95 toy-models, it was so far shown to perform as well or better than the EnKF with an optimally tuned inflation. The iterative ensemble Kalman filter (IEnKF) is an EnKF which was shown to perform much better than the EnKF in strongly nonlinear conditions, such as with the Lorenz '63 and '95 models, at the cost of iteratively updating the trajectories of the ensemble members. This article aims at further exploring the two filters and at combining both into an EnKF that does not require inflation in perfect model condition, and which is as efficient as the IEnKF in very nonlinear conditions. In this study, EnKF-N is first introduced and a new implementation is developed. It decomposes EnKF-N into a cheap two-step algorithm that amounts to computing an optimal inflation factor. This offers a justification of the use of the inflation technique in the traditional EnKF and why it can often be efficient. Secondly, the IEnKF is introduced following a new implementation based on the Levenberg-Marquardt optimisation algorithm. Then, the two approaches are combined to obtain the finite-size iterative ensemble Kalman filter (IEnKF-N). Several numerical experiments are performed on IEnKF-N with the Lorenz '95 model. These experiments demonstrate its numerical efficiency as well as its performance that offer, at least, the best of both filters. We have also selected a demanding case based on the Lorenz '63 model that points to ways to improve the finite-size ensemble Kalman filters. Eventually, IEnKF-N could be seen as the first brick of an efficient ensemble Kalman smoother for strongly nonlinear systems.

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Forecasting: it is not about statistics, it is about dynamics

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2009.0195 ◽

2010 ◽

Vol 368 (1910) ◽

pp. 263-271 ◽

Cited By ~ 5

Author(s):

Kevin Judd ◽

Thomas Stemler

Keyword(s):

Nonlinear Dynamics ◽

Nonlinear Systems ◽

Kalman Filters ◽

Initial Conditions ◽

Mechanical Systems ◽

Trans Asme ◽

Sensitivity To Initial Conditions ◽

The Way

In 1963, the mathematician and meteorologist Edward Lorenz published a paper (Lorenz 1963 J. Atmos. Sci. 20 , 130–141) that changed the way scientists think about the prediction of geophysical systems, by introducing the ideas of chaos, attractors, sensitivity to initial conditions and the limitations to forecasting nonlinear systems. Three years earlier, the mathematician and engineer Rudolf Kalman had published a paper (Kalman 1960 Trans. ASME Ser. D, J. Basic Eng. 82 , 35–45) that changed the way engineers thought about prediction of electronic and mechanical systems. Ironically, in recent years, geophysicists have become increasingly interested in Kalman filters, whereas engineers have become increasingly interested in chaos. It is argued that more often than not the tracking and forecasting of nonlinear systems has more to do with the nonlinear dynamics that Lorenz considered than it has to do with statistics that Kalman considered. A position with which both Lorenz and Kalman would appear to agree.

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