Initialization of Ensemble Data Assimilation
Milija Zupanski, Steven Fletcher, I. Michael Navon, Bahri Uzunoglu, Ross P. Heikes, David A. Randall, Todd D. Ringler, Dacian N. Daescu
A specification of the initial ensemble in ensemble data is addressed. The presented work examines the impact of ensemble initiation in the Maximum Likelihood Ensemble Filter (MLEF) framework, but it is applicable to other ensemble data assimilation algorithms as well. Two new methods are considered: first, based on the use of the Kardar-Parisi-Zhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, and second, based on spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallow-water model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in ensemble data assimilation is beneficial. The root-mean-square error rate of convergence of data assimilation is improved, and the positive impact of initial correlations is noticeable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of ensemble data assimilation.