An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters

Abstract

We develop an optimal experimental design framework for adapting the covariance inflation and localization in data assimilation problems. Covariance inflation and localization are ubiquitously employed to alleviate the effect of using ensembles of finite sizes in all practical data assimilation systems. The choice of both the inflation factor and the localization radius can have a significant impact on the performance of the assimilation scheme. These parameters are generally tuned by trial and error, rendering them expensive to optimize in practice. Spatially and temporally varying inflation parameter and localization radii have been recently proposed and have been empirically proven to enhance the performance of the employed assimilation filter. In this study, we present a variational framework for adaptive tuning of the inflation and localization parameters. Each of these parameters is optimized independently, with an objective to minimize the uncertainty in the posterior state. The proposed framework does not assume uncorrelated observations or prior errors and can in principle be applied without expert knowledge about the model and the observations. Thus, it is adequate for handling dense as well as sparse observational networks. We present the mathematical formulation, algorithmic description of the approach, and numerical experiments using the two-layer Lorenz-96 model.