Revised manuscript submitted to Monthly Weather Review

Strategies to improve covariance estimates for ensemble-based assimilation of near-surface observations in atmospheric models are explored. It is known that lo-calization of covariance estimates can improve conditioning of covariance matrices in the assimilation process by removing spurious elements and increasing the rank of the matrix. Vertical covariance localization is the focus of this work, and two basic ap-proaches are compared: (1) a recently proposed hierarchical filter approach based on sampling theory, and (2) a more-commonly used fifth-order piecewise rational func-tion. The hierarchical filter allows for dynamic estimates of localization functions, and does not place any restrictions on their form. The rational function is optimized for every analysis time of day, and for every possible observation and state variable combination. The methods are tested with a column model containing PBL and land-surface parameterization schemes that are available in current mesoscale modeling systems. The results are expected to provide context for assimilation of near-surface observations in mesoscale models, which will benefit short range mesoscale NWP ap