Given daily ozone readings from 512 weather stations in the Eastern United States, we are interested in both predicting future ozone concentrations and in gaining insight into the space-time dependence structure of the data. We model ozone concentration as a process that moves across the region over time and exhibits spatial dependence locally in time. Our hope is to better understand the space-time dependence in ozone, so that this information can be used to assess the effectiveness of new monitoring network configurations. Process convolutions not only provide a framework for incorporating time dependence in spatial modeling, but also remain computationally tractable with large datasets. Standard dynamic linear modeling methods can be used to specify the time dependence allowing efficient posterior exploration. We consider a few variations of these space-time process convolution models that incorporate different levels of space-time dependence.

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