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Very large inverse problems in atmosphere and ocean modelling

Johnson, C., Nichols, N. K. ORCID: and Hoskins, B. J. (2005) Very large inverse problems in atmosphere and ocean modelling. International Journal for Numerical Methods in Fluids, 47 (8-9). pp. 759-771. ISSN 0271-2091

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To link to this item DOI: 10.1002/fld.869


For the very large nonlinear dynamical systems that arise in a wide range of physical, biological and environmental problems, the data needed to initialize a numerical forecasting model are seldom available. To generate accurate estimates of the expected states of the system, both current and future, the technique of ‘data assimilation’ is used to combine the numerical model predictions with observations of the system measured over time. Assimilation of data is an inverse problem that for very large-scale systems is generally ill-posed. In four-dimensional variational assimilation schemes, the dynamical model equations provide constraints that act to spread information into data sparse regions, enabling the state of the system to be reconstructed accurately. The mechanism for this is not well understood. Singular value decomposition techniques are applied here to the observability matrix of the system in order to analyse the critical features in this process. Simplified models are used to demonstrate how information is propagated from observed regions into unobserved areas. The impact of the size of the observational noise and the temporal position of the observations is examined. The best signal-to-noise ratio needed to extract the most information from the observations is estimated using Tikhonov regularization theory. Copyright © 2005 John Wiley & Sons, Ltd.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:4942
Uncontrolled Keywords:large-scale inverse problems;variational data assimilation;nonlinear dynamical systems;weather, ocean and climate models;singular vectors;Tikhonov regularization;nonlinear least squares
Publisher:John Wiley & Sons

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