Alicia R. Karspeck
February 24, 2010
Mesa Laboratory 2, Main Seminar Room
A "Typical" and "Atypical" Application of Ensemble Filtering
In typical applications of ensemble Kalman filtering, a numerical model is used to evolve the probability distribution though time by operating on an ensemble of state vectors. The covariance structures on which one draws during the update-step arise naturally from the dynamics of the numerical model. Here we present a text-book application of the ensemble Kalman filter, in which we assimilate observations of SST in the tropical Pacific to make state-estimates of the interannual (ENSO-related) modes of ocean/atmosphere variability. Our results illuminate some pitfalls of naively chosen localization functions, in particular how the dynamical structure of the numerical model must be taken into consideration when applying localization.