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Four-dimensional Ensemble Variational (4D-en-var) Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 4 (14/07/2014)

By Gustafsson, N.

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Book Id: WPLBN0003992605
Format Type: PDF Article :
File Size: Pages 18
Reproduction Date: 2015

Title: Four-dimensional Ensemble Variational (4D-en-var) Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 4 (14/07/2014)  
Author: Gustafsson, N.
Volume: Vol. 21, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Bojarova, J., & Gustafsson, N. (2014). Four-dimensional Ensemble Variational (4D-en-var) Data Assimilation for the High Resolution Limited Area Model (Hirlam) : Volume 21, Issue 4 (14/07/2014). Retrieved from http://hawaiilibrary.net/


Description
Description: Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden. A four-dimensional ensemble variational (4D-En-Var) 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.


Summary
Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

Excerpt
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