A Local Data Assimilation Method (Local DA v1.0) and its Application in a Simulated Typhoon Case.

A local data assimilation method, Local DA, is introduced. The proposed algorithm aims to perform hybrid and multiscale analyses simultaneously yet independently for each grid, vertical column or column group and aims to flexibly perform analyses with or without ensemble perturbations. To achieve these goals, an error sample matrix is constructed by explicitly computing the localized background error correlation matrix of model variables that are projected onto observation-associated grids (e.g., radar velocity) or columns (e.g., precipitable water vapor). This error sample matrix allows Local DA to apply the conjugate gradient (CG) method to solve the cost function and to perform localization in the model-variable space, the observation-variable space, or both spaces (double-space localization). To assess the Local DA performance, a typhoon case is simulated, and a multiscale observation network comprising sounding, wind profiler, precipitable water vapor, and radar data is built; additionally, a time-lagged ensemble is employed. The results show that experiments using the hybrid covariance and double-space localization yield smaller analysis errors than experiments without the static covariance and experiments without double-space localization. Moreover, the hybrid covariance plays a more important role than does localization when a poor time-lagged ensemble is used. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue, and the wall clock time of Local DA implemented in parallel is halved as the number of cores doubles. [ABSTRACT FROM AUTHOR]