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Correlated observation errors in data assimilation

Stewart, L.M., Dance, S. L. and Nichols, N.K. (2008) Correlated observation errors in data assimilation. International Journal for Numerical Methods in Fluids, 56 (8). pp. 1521-1527. ISSN 0271-2091

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To link to this article DOI: 10.1002/fld.1636

Abstract/Summary

Data assimilation provides techniques for combining observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting assigned to each observation in the analysis is determined by its associated error. Remote sensing data usually has correlated errors, but the correlations are typically ignored in NWP. Here, we describe three approaches to the treatment of observation error correlations. For an idealized data set, the information content under each simplified assumption is compared with that under correct correlation specification. Treating the errors as uncorrelated results in a significant loss of information. However, retention of an approximated correlation gives clear benefits.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics
ID Code:1275
Publisher:John Wiley & Sons

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