Correlations of control variables in variational data assimilation

Katz, D., Lawless, A. S. ORCID: https://orcid.org/0000-0002-3016-6568, Nichols, N. K. ORCID: https://orcid.org/0000-0003-1133-5220, Cullen, M. J. P. and Bannister, R. N. ORCID: https://orcid.org/0000-0002-6846-8297 (2011) Correlations of control variables in variational data assimilation. Quarterly Journal of the Royal Meteorological Society, 137 (656). pp. 620-630. ISSN 1477-870X doi: 10.1002/qj.798 (Part A)

Abstract/Summary

Variational data assimilation systems for numerical weather prediction rely on a transformation of model variables to a set of control variables that are assumed to be uncorrelated. Most implementations of this transformation are based on the assumption that the balanced part of the flow can be represented by the vorticity. However, this assumption is likely to break down in dynamical regimes characterized by low Burger number. It has recently been proposed that a variable transformation based on potential vorticity should lead to control variables that are uncorrelated over a wider range of regimes. In this paper we test the assumption that a transform based on vorticity and one based on potential vorticity produce an uncorrelated set of control variables. Using a shallow-water model we calculate the correlations between the transformed variables in the different methods. We show that the control variables resulting from a vorticity-based transformation may retain large correlations in some dynamical regimes, whereas a potential vorticity based transformation successfully produces a set of uncorrelated control variables. Calculations of spatial correlations show that the benefit of the potential vorticity transformation is linked to its ability to capture more accurately the balanced component of the flow.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/19982
Identification Number/DOI	10.1002/qj.798
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Royal Meteorological Society
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