Dynamic models for spatiotemporal data

We propose a model for non-stationary spatiotemporal data. To account for s patial variability, we model the mean function at each time period as a loc ally weighted mixture of linear regressions. To incorporate temporal variat ion, we allow the regression coefficients to change through time, The model is cast In a Gaussian state space framework, which allows us to include te mporal components such as trends, seasonal effects and autoregressions, and permits a fast implementation and full probabilistic inference for the par ameters, interpolations and forecasts. To illustrate the model, we apply it to two large environmental data sets: tropical rainfall levels and Atlanti c Ocean temperatures.