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

Abstract

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 extratropical 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.

Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada

“…The analyses were obtained from ECCC archives (Buehner et al, 2013Caron et al, 2015), in order to prevent chaotic drift of the model meteorology from observations. Consequently, our simulation setup comprises simulations on the North American domain in 30 h cycles starting at 12:00 UTC, and the oil sands domain in 24 h cycles starting at 18:00 UTC (the 6 h lag being required to allow meteorological spinup of the lower resolution model).…”

Scale-dependent background-error covariance localisation

“…In 4DEnVar (e.g. Buehner et al, 2013), a temporal localisation function could be introduced that was also dependent on the horizontal scale such that smallscale errors could be forced to decorrelate more rapidly through time than large-scale errors.…”

“…Houtekamer et al, 2014) and the more recent variational-ensemble (EnVar; e.g. Buehner et al, 2013) approaches. The computational cost of producing an ensemble of forecasts large enough to estimate sufficiently accurate and high-rank covariances is prohibitive.…”

Comparison between Four-Dimensional LETKF and Ensemble-Based Variational Data Assimilation with Observation Localization

“…It has been shown that the result of 4D-EnVAR is closer to the true value than that of 4D-VAR, because the background covariance matrix includes the information of ensemble forecasts as well as the climatology (e.g., Buehner 2005;Liu et al 2008;Buehner et al 2013). However, it is difficult to make clean comparisons between 4D-EnVAR and EnKF because generic variational data assimilation systems and EnKF systems differ in many respects of solution algorithm, which are optimized for each system to improve efficiency of the calculation, dynamical balance of the analysis, and so on (Buehner et al 2010).…”