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Estimation of synoptic and mesoscale forecast error covariances in a limited-area model
SMHI, Research Department, Atmospheric remote sensing.
2000 (English)In: Monthly Weather Review, ISSN 0027-0644, E-ISSN 1520-0493, Vol. 128, no 3, 644-667 p.Article in journal (Refereed) Published
Abstract [en]

Statistical and balance features of forecast errors are generally incorporated in the background constraint of variational data assimilation. Forecast error covariances are here estimated with a spectral approach and from a set of forecast differences; autocovariances are calculated with a nonseparable scheme, and multiple linear regressions are used in the formulation of cross covariances. Such an approach was first developed for global models; it is here adapted to ALADIN, a bi-Fourier high-resolution limited-area model, and extended to a multivariate study of humidity forecast errors. Results for autocovariances confirm the importance of nonseparability, in terms of both vertical variability of horizontal correlations and dependence of vertical correlations with horizontal scale; high-resolution spatial correlations are obtained, which should enable a high-resolution analysis. Moreover nonnegligible relationships are found between forecast errors of humidity and those of mass and wind fields.

Place, publisher, year, edition, pages
2000. Vol. 128, no 3, 644-667 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
URN: urn:nbn:se:smhi:diva-1506DOI: 10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2ISI: 000086632400006OAI: oai:DiVA.org:smhi-1506DiVA: diva2:847071
Available from: 2015-08-19 Created: 2015-08-17 Last updated: 2015-08-19Bibliographically approved

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