 |
| Titel |
Generalized background error covariance matrix model (GEN_BE v2.0) |
| VerfasserIn |
G. Descombes, T. Auligné, F. Vandenberghe, D. M. Barker, J. Barré |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1991-959X
|
| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 3 ; Nr. 8, no. 3 (2015-03-20), S.669-696 |
| Datensatznummer |
250116182
|
| Publikation (Nr.) |
 copernicus.org/gmd-8-669-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
The specification of state background error statistics is a key component of
data assimilation since it affects the impact observations will have on the
analysis. In the variational data assimilation approach, applied in
geophysical sciences, the dimensions of the background error covariance
matrix (B) are usually too large to be explicitly determined and
B needs to be modeled. Recent efforts to include new variables in
the analysis such as cloud parameters and chemical species have required the
development of the code to GENerate the Background Errors (GEN_BE) version
2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE
allows for a simpler, flexible, robust, and community-oriented framework that
gathers methods used by some meteorological operational centers and
researchers.
We present the advantages of this new design for the data assimilation
community by performing benchmarks of different modeling of B and
showing some of the new features in data assimilation test cases. As data
assimilation for clouds remains a challenge, we present a multivariate
approach that includes hydrometeors in the control variables and new
correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose
error parameter statistics for chemical species, which shows that it is a
tool flexible enough to implement new control variables. While the generation
of the background errors statistics code was first developed for atmospheric
research, the new version (GEN_BE v2.0) can be easily applied to other
domains of science and chosen to diagnose and model B. Initially
developed for variational data assimilation, the model of the B
matrix may be useful for variational ensemble hybrid methods as well. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|