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Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario

Du, Hailiang; Smith, Leonard A.

Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario Thumbnail


Leonard A. Smith


State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scenario, noisy observations prevent a precise estimate of the current state. In this setting, ensemble Kalman filter approaches are hampered by their foundational assumptions of dynamical linearity, while variational approaches may fail in practice owing to local minima in their cost function. The pseudo-orbit data assimilation approach improves state estimation by enhancing the balance between the information derived from the dynamic equations and that derived from the observations. The potential use of this approach for numerical weather prediction is explored in the perfect model scenario within two deterministic chaotic systems: the two-dimensional Ikeda map and 18-dimensional Lorenz96 flow. Empirical results demonstrate improved performance over that of the two most common traditional approaches of data assimilation (ensemble Kalman filter and four-dimensional variational assimilation).


Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario. Journal of the Atmospheric Sciences, 71(2), 469-482.

Journal Article Type Article
Acceptance Date Oct 28, 2013
Online Publication Date Jan 31, 2014
Publication Date Feb 1, 2014
Deposit Date Jul 31, 2018
Publicly Available Date Aug 21, 2018
Journal Journal of the Atmospheric Sciences
Print ISSN 0022-4928
Electronic ISSN 1520-0469
Publisher American Meteorological Society
Peer Reviewed Peer Reviewed
Volume 71
Issue 2
Pages 469-482
Related Public URLs


Published Journal Article (794 Kb)

Copyright Statement
© 2014 American Meteorological Society.

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