Abstract
This article presents a novel autoregressive space–time model for ground‐level ozone data, which models not only spatio–temporal dynamics of hourly ozone concentrations, but also relationships between ozone concentrations and meteorological variables. The proposed model has a non‐separable spatio–temporal covariance function that depends on wind speed and wind direction, and hence is non‐stationary in both time and space. Ozone concentration for a given location and time is assumed to be directly influenced by ozone concentrations at neighboring locations at the previous time, via a weight function of space–time dynamics caused by wind speed and wind direction.
To our knowledge, the proposed method is the first one to incorporate the transport effect of ozone into the spatio–temporal covariance structure. Moreover, it uses a computationally efficient space–time Kalman filter and can compute optimal spatio–temporal prediction at any location and at anytime very fast for given meteorological conditions. Ozone data from Taipei are used for illustration, in which the model parameters are estimated by maximum likelihood.