Procedia Computer Science
Error analysis (Mathematics), Analysis of covariance, Kalman filtering
This article presents a framework for performing ensemble and hybrid data assimilation in a weak-constraint four-dimensional variational data assimilation system (w4D-Var). A practical approach is considered that relies on an ensemble of w4D-Var systems solved by the incremental algorithm to obtain flow-dependent estimates to the model error statistics. A proof-of-concept is presented in an idealized context using the Lorenz multi-scale model. A comparative analysis is performed between the weak- and strong-constraint ensemble-based methods. The importance of the weight coefficients assigned to the static and ensemble-based components of the error covariances is also investigated. Our preliminary numerical experiments indicate that an ensemble-based model error covariance specification may significantly improve the quality of the analysis.
Shaw, J., & Daescu, D. (2016). An Ensemble Approach to Weak-Constraint Four-Dimensional Variational Data Assimilation. Procedia Computer Science, 80, 496–506. http://doi.org/10.1016/j.procs.2016.05.329