Abstract: The sensitivity of the solution of an optimization problem with respect to general parameter perturbation (e.g., sensitivity in calculus of variations) is addressed. First, a total variation of the optimal solution is ...

Abstract: A preconditioning method suitable for use in four-dimensional variational (4DVAR) data assimilation is proposed. The method is a generalization of the preconditioning previously developed by the author, now designed to ...

Abstract: A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by ...

Abstract: Four-dimensional variational data assimilation is applied to a regional forecast model as part of the development of a new data assimilation system at the National Meteorological Center (NMC). The assimilation employs an ...

Abstract: A methodology for model error estimation is proposed and examined in this study. It provides estimates of the dynamical model state, the bias, and the empirical parameters by combining three approaches: 1) ensemble data ...

Abstract: No Abstract available.

Abstract: regional hybrid variational?ensemble data assimilation system (HVEDAS), the maximum likelihood ensemble filter (MLEF), is applied to the 2011 version of the NOAA operational Hurricane Weather Research and Forecasting (HWRF) ...

Abstract: A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional ...