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Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)
SMHI, Research Department, Meteorology.ORCID iD: 0000-0001-8376-2729
2014 (English)In: Nonlinear processes in geophysics, ISSN 1023-5809, E-ISSN 1607-7946, Vol. 21, no 4, 745-762 p.Article in journal (Refereed) Published
Abstract [en]

A four-dimensional ensemble variational (4D-EnVar) data assimilation has been developed for a limited area model. The integration of tangent linear and adjoint models, as applied in standard 4D-Var, is replaced with the use of an ensemble of non-linear model states to estimate four-dimensional background error covariances over the assimilation time window. The computational costs for 4D-En-Var are therefore significantly reduced in comparison with standard 4D-Var and the scalability of the algorithm is improved. The flow dependency of 4D-En-Var assimilation increments is demonstrated in single simulated observation experiments and compared with corresponding increments from standard 4D-Var and Hybrid 4D-Var ensemble assimilation experiments. Real observation data assimilation experiments carried out over a 6-week period show that 4D-En-Var outperforms standard 4D-Var as well as Hybrid 4D-Var ensemble data assimilation with regard to forecast quality measured by forecast verification scores.

Place, publisher, year, edition, pages
2014. Vol. 21, no 4, 745-762 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
URN: urn:nbn:se:smhi:diva-150DOI: 10.5194/npg-21-745-2014ISI: 000340106000002OAI: diva2:800911
Available from: 2015-04-08 Created: 2015-03-26 Last updated: 2016-05-30Bibliographically approved

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