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Doppler Radar Wind Data Assimilation with HIRLAM 3DVAR
Stockholm University, Faculty of Science, Department of Meteorology.
2004 In: Monthly Weather Review, ISSN 0027-0644, Vol. 132, no 5, 1081-1092 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2004. Vol. 132, no 5, 1081-1092 p.
Identifiers
URN: urn:nbn:se:su:diva-24608OAI: oai:DiVA.org:su-24608DiVA: diva2:197923
Note
Part of urn:nbn:se:su:diva-7258Available from: 2007-12-20 Created: 2007-12-07Bibliographically approved
In thesis
1. On errors in meteorological data assimilation
Open this publication in new window or tab >>On errors in meteorological data assimilation
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data assimilation in Numerical Weather Prediction (NWP) optimally blends observations with atmospheric model data in order to obtain the best possible initial state for an atmospheric model prediction. Specification of error characteristics is an important part of data assimilation. This thesis is concerned with representation of background error standard deviations, with handling of observations, and with observation error characteristics. The research includes both the study of basic assimilation problems within the framework of an idealised quasi-geostrophic model and the development of assimilation algorithms for a full scale limited area high resolution forecasting system.

It is shown in this thesis that an accurate representation of background error standard deviations is important for the quality of NWP forecasts. In particular the effect of introducing a time-dependency is investigated and a novel approach to relate the flow-dependency of background error standard deviations to an Eady baroclinic instability measure is developed. The Eady based flow-dependent background error representation is demonstrated to have a positive impact on NWP, as compared to horizontally and temporally independent background error statistics. An alternative method, based on on-line error estimation and maximum likelihood theory, is proven to be able to represent the flow-dependency of background error standard deviations and encouraging results are obtained within the quasi-geostrophic model framework. Furthermore, it is shown that a proper observation handling is an important part of data assimilation. The treatment of error characteristics is specifically shown to be of major importance when exploiting the potential benefit of radar radial wind observations within data assimilation.

Place, publisher, year, edition, pages
Stockholm: Meteorologiska institutionen (MISU), 2007. 27 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:su:diva-7258 (URN)978-91-7155-555-7 (ISBN)
Public defence
2008-01-17, sal C609, Arrheniuslaboratorierna, Svante Arrhenius väg 14-18, Stockholm, 10:00
Opponent
Supervisors
Available from: 2007-12-20 Created: 2007-12-07Bibliographically approved

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