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A Hybrid Variational Ensemble Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 1 (26/02/2014)

By Gustafsson, N.

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Book Id: WPLBN0003991942
Format Type: PDF Article :
File Size: Pages 21
Reproduction Date: 2015

Title: A Hybrid Variational Ensemble Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 1 (26/02/2014)  
Author: Gustafsson, N.
Volume: Vol. 21, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Vignes, O., Bojarova, J., & Gustafsson, N. (2014). A Hybrid Variational Ensemble Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 1 (26/02/2014). Retrieved from

Description: Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden. A hybrid variational ensemble data assimilation has been developed on top of the HIRLAM variational data assimilation. It provides the possibility of applying a flow-dependent background error covariance model during the data assimilation at the same time as full rank characteristics of the variational data assimilation are preserved. The hybrid formulation is based on an augmentation of the assimilation control variable with localised weights to be assigned to a set of ensemble member perturbations (deviations from the ensemble mean). The flow-dependency of the hybrid assimilation is demonstrated in single simulated observation impact studies and the improved performance of the hybrid assimilation in comparison with pure 3-dimensional variational as well as pure ensemble assimilation is also proven in real observation assimilation experiments. The performance of the hybrid assimilation is comparable to the performance of the 4-dimensional variational data assimilation. The sensitivity to various parameters of the hybrid assimilation scheme and the sensitivity to the applied ensemble generation techniques are also examined. In particular, the inclusion of ensemble perturbations with a lagged validity time has been examined with encouraging results.

A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

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