Abstract
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.
Highlights
Since the best estimate of the background error covariance matrix (B) is a key component of data assimilation improvements, various operational meteorological centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the UK Met Office, continue to develop new algorithms, techniques, and tools (Bannister, 2008a, b) to model B within a variational framework
Based on the D-ensemble data set coming from the DART experiment (i.e., Sect. 3 and Romine et al, 2014), we present in Sect. 4.1 the parameters that define the vertical transform Uv by using empirical orthogonal function (EOF) decomposition for WRFDA (Beof) and by using recursive filters for GSI (Brcf)
A larger spread of the V increment along pressure levels is observed for Beof and Brcf compared to the experiment of Bnam
Summary
Since the best estimate of the background error covariance matrix (B) is a key component of data assimilation improvements, various operational meteorological centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), and the UK Met Office, continue to develop new algorithms, techniques, and tools (Bannister, 2008a, b) to model B within a variational framework. The GEN_BE code was developed by Barker et al (2004) as a component of a three-dimensional (3-D) variational data assimilation (3DVAR) method to estimate the background error of the fifth-generation Penn State/NCAR mesoscale model (MM5, Grell et al, 1994) for a limited-area system. The framework of the GEN_BE code version 2.0 has been developed to merge these different efforts using linear regression to model the balance between variables, empirical orthogonal function (EOF) decomposition techniques and the diagnostic of length scales to apply recursive filters (RFs) It allows reading of input from different models and provision of output for different data assimilation platforms. The diagnostic of parameters such as standard deviation and vertical and horizontal length scales are discussed for the chemical species carbon monoxide (CO), nitrogen oxides (NOx) and ozone (O3) in a variational data assimilation framework
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