This study presents the first application of a localized particle filter (PF) for data assimilation in a high-dimensional geophysical model. Particle filters form Monte Carlo approximations of model probability densities conditioned on observations, while making no assumptions about the underlying error distribution. Unlike standard PFs, the local PF uses a localization function to reduce the influence of distant observations on state variables, which significantly decreases the number of particles required to maintain the filter’s stability. Because the local PF operates effectively using small numbers of particles, it provides a possible alternative to Gaussian filters, such as ensemble Kalman filters, for large geophysical models. In the current study, the local PF is compared with stochastic and deterministic ensemble Kalman filters using a simplified atmospheric general circulation model. The local PF is found to provide stable filtering results over yearlong data assimilation experiments using only 25 particles. The local PF also outperforms the Gaussian filters when observation networks include measurements that have non-Gaussian errors or relate nonlinearly to the model state, like remotely sensed data used frequently in atmospheric analyses. Results from this study encourage further testing of the local PF on more complex geophysical systems, such as weather prediction models.