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Pseudo-orbit data assimilation. part II: the perfect model scenario

Du, Hailiang and Smith, Leonard A. (2014) Pseudo-orbit data assimilation. part II: the perfect model scenario. Journal of the Atmospheric Sciences, 71 (2). pp. 483-495. ISSN 0022-4928

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Identification Number: 10.1175/JAS-D-13-033.1


Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system that generated the observations (if such a thing exists) is not a member of the available model class (i.e., the set of mathematical structures admitted as potential models). To the extent that traditional approaches address structural model error at all, most fail to produce consistent treatments. This results in questionable estimates both of the model state and of its uncertainty. A promising alternative approach is proposed to produce more consistent estimates of the model state and to estimate the (state dependent) model error simultaneously. This alternative consists of pseudo-orbit data assimilation with a stopping criterion. It is argued to be more efficient and more coherent than one alternative variational approach [a version of weak-constraint four-dimensional variational data assimilation (4DVAR)]. Results that demonstrate the pseudo-orbit data assimilation approach can also outperform an ensemble Kalman filter approach are presented. Both comparisons are made in the context of the 18-dimensional Lorenz96 flow and the two-dimensional Ikeda map. Many challenges remain outside the perfect model scenario, both in defining the goals of data assimilation and in achieving high-quality state estimation. The pseudo-orbit data assimilation approach provides a new tool for approaching this open problem.

Item Type: Article
Official URL:
Additional Information: © 2013 American Meteorological Society
Divisions: Statistics
Centre for Analysis of Time Series
Subjects: Q Science > Q Science (General)
Date Deposited: 25 Feb 2014 09:21
Last Modified: 20 Jul 2021 01:01
Funders: Economic and Social Research Council, Munich Re. L. A. Smith

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