Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)
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 back...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-07-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/21/745/2014/npg-21-745-2014.pdf |
| Summary: | 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.
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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. |
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| ISSN: | 1023-5809 1607-7946 |