Most climatological processes involve variability over both space and time.
The extension of traditional geostatistical methods, such as kriging, to the spatio-temporal domain is one possible approach to characterize this variability. Due to difficulties caused by large data sets and the modeling of space, time, and spatio-temporal interactions, this approach is limited. In this article, we present an approach to space-time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive. We use this Kalman filter to predict at times and locations for which we do not have data. The method is applied to a data set of near-surface winds, obtained from a blending of observations and a deterministic atmospheric model, and is shown to perform better than independently applying simple kriging to each time slice of the spatial field.