A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts

Abstract

Knowledge of the probability distribution of initial conditions is central to almost all practical studies of predictability and to improvements in stochastic prediction of the atmosphere. Traditionally, data assimilation for atmospheric predictability or prediction experiments has attempted to find a single ?best? estimate of the initial state. Additional information about the initial condition probability distribution is then obtained primarily through heuristic techniques that attempt to generate representative perturbations around the best estimate. However, a classical theory for generating an estimate of the complete probability distribution of an initial state given a set of observations exists. This nonlinear filtering theory can be applied to unify the data assimilation and ensemble generation problem and to produce superior estimates of the probability distribution of the initial state of the atmosphere (or ocean) on regional or global scales. A Monte Carlo implementation of the fully nonlinear filter has been developed and applied to several low-order models. The method is able to produce assimilations with small ensemble mean errors while also providing random samples of the initial condition probability distribution. The Monte Carlo method can be applied in models that traditionally require the application of initialization techniques without any explicit initialization. Initial application to larger models is promising, but a number of challenges remain before the method can be extended to large realistic forecast models.