Ensemble variational assimilation as a probabilistic estimator – Part 2: The fully non-linear case

Abstract

The method of ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is implemented in fully non-linear conditions on the Lorenz-96 chaotic 40-parameter model. In the case of strong-constraint assimilation, it requires association with the method of quasi-static variational assimilation (QSVA). It then produces ensembles which possess as much reliability and resolution as in the linear case, and its performance is at least as good as that of ensemble Kalman filter (EnKF) and particle filter (PF). On the other hand, ensembles consisting of solutions that correspond to the absolute minimum of the objective function (as identified from the minimizations without QSVA) are significantly biased. In the case of weak-constraint assimilation, EnsVAR is fully successful without need for QSVA.