To overcome the curse of dimensionality in large scale state space models, we discuss the localized ensemble Kalman filter, a Monte Carlo state estimation procedure that, unlike the standard particle filter and many other state estimation techniques, remains computationally tractable when the state dimension is large. We give the asymptotic behavior of the localized ensemble Kalman filter, and apply it to time-dependent tomographic imaging of dynamic objects such as the solar corona.