Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

By M. Buehner, J. Morneau, C. Charette. 2013

134 citations recorded by the source corpus.

Abstract. The goal of this study is to evaluate a version of the ensemble-variational data assimilation approach (EnVar) for possible replacement of 4D-Var at Environment Canada for global deterministic weather prediction. This implementation of EnVar relies on 4-D ensemble covariances, obtained from an ensemble Kalman filter, that are combined in a vertically dependent weighted average with simple static covariances. Verification results are presented from a set of data assimilation experiments over two separate 6-week periods that used assimilated observations and model configuration very similar to the currently operational system. To help interpret the comparison of EnVar versus 4D-Var, additional experiments using 3D-Var and a version of EnVar with only 3-D ensemble covariances are also evaluated. To improve the rate of convergence for all approaches evaluated (including EnVar), an estimate of the cost function Hessian generated by the quasi-Newton minimization algorithm is cycled from one analysis to the next. Analyses from EnVar (with 4-D ensemble covariances) nearly always produce improved, and never degraded, forecasts when compared with 3D-Var. Comparisons with 4D-Var show that forecasts from EnVar analyses have either similar or better scores in the troposphere of the tropics and the winter extra-tropical region. However, in the summer extra-tropical region the medium-range forecasts from EnVar have either similar or worse scores than 4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are significantly better than 4D-Var relative to radiosonde observations for both periods and in all regions. The use of 4-D versus 3-D ensemble covariances only results in small improvements in forecast quality. By contrast, the improvements from using 4D-Var versus 3D-Var are much larger. Measurement of the fit of the background and analyzed states to the observations suggests that EnVar and 4D-Var can both make better use of observations distributed over time than 3D-Var. In summary, the results from this study suggest that the EnVar approach is a viable alternative to 4D-Var, especially when the simplicity and computational efficiency of EnVar are considered. Additional research is required to understand the seasonal dependence of the difference in forecast quality between EnVar and 4D-Var in the extra-tropics.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/abbdfa6eae6bd611c3a9bc1361a7013684c6a8b0

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Citations in the corpus

Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “Full 4DEnVar operational systems are now implemented or are in the course of being so (Bowler et al., 2017; Buehner et al., 2015a; Buehner et al., 2013; Desroziers, Camino, & Berre, 2014; Gustafsson, Bojarova, & Vignes, 2014; Kleist & Ide, 2015; Lorenc, Bowler, Clayton, Pring, & Fairbairn, 2015).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, background): “‖It is possible to allow the β to vary with vertical level, as is done by Buehner et al. (2013).”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, background): “Furthermore, the combination of both approaches into so-called hybrid systems is an active field of research particularly for operational applications (e.g., Buehner et al. 2013; Lorenc et al. 2015).”