Ensemble regional data assimilation using joint states

TL;DR

This paper introduces a joint data assimilation method for global and regional models that enhances analysis accuracy and forecast skill by integrating information from both models through a constrained optimization approach.

Contribution

It presents a novel joint analysis scheme that simultaneously assimilates data for global and regional models, improving regional boundary conditions and large-scale flow consistency.

Findings

Improved regional and global analysis accuracy.

Enhanced forecast skill for both models.

Effective in idealized Lorenz model experiments.

Abstract

We propose a data assimilation scheme that produces the analyses for a global and an embedded limited area model simultaneously, considering forecast information from both models. The purpose of the proposed approach is twofold. First, we expect that the global analysis will benefit from incorporation of information from the higher resolution limited area model. Second, our method is expected to produce a limited area analysis that is more strongly constrained by the large scale flow than a conventional limited area analysis. The proposed scheme minimizes a cost function in which the control variable is the joint state of the global and the limited area models. In addition, the cost function includes a constraint term that penalizes large differences between the global and the limited area state estimates. The proposed approach is tested by idealized experiments, using `toy' models introduced by Lorenz in 2005. The results of these experiments suggest that the proposed approach improves the global analysis within and near the limited area domain and the regional analysis near the lateral boundaries. These analysis improvements lead to forecast improvements in both the global and the limited area models.