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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 3
Geosci. Model Dev., 8, 669-696, 2015
https://doi.org/10.5194/gmd-8-669-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: The community version of the Weather Research and Forecasting...

Geosci. Model Dev., 8, 669-696, 2015
https://doi.org/10.5194/gmd-8-669-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 20 Mar 2015

Development and technical paper | 20 Mar 2015

Generalized background error covariance matrix model (GEN_BE v2.0)

G. Descombes1, T. Auligné1, F. Vandenberghe2, D. M. Barker3, and J. Barré4 G. Descombes et al.
  • 1National Center for Atmospheric Research/MMM, Boulder, Colorado, USA
  • 2National Center for Atmospheric Research/RAL, Boulder, Colorado, USA
  • 3Met Office, Exeter, UK
  • 4National Center for Atmospheric Research/ACD, Boulder, Colorado, USA

Abstract. 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.

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