An ensemble-based three-dimensional variational data assimilation scheme

A new ensemble-based three-dimensional variational data assimilation method (E-B3DVar) is presented. The analysis solution is obtained by minimization of a cost function that depends on a general nonlinear observation operator. The E B3DVar belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the E-B3DVar produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in E-B3DVar are exploited to efficiently calculate the gradient of the cost function. The E-B3DVar method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the E-B3DVar over other ensemble data assimilation algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the E-B3DVar method is applied to data assimilation indicates potential wider applications of this new E B3DVar method.