The Application Of Hierarchical Bayesian Space-time Model On Meteorological Data

Space-time data are ubiquitous in the environmental sciences. These data contain many different scales of spatial and temporal variability. Such data are often non-stationary in space and time and may involve many observation/prediction locations. These factors can limit the effectiveness of traditional space-time statistical models and methods. In this article, we propose the use of hierarchical space-time models to get more flexible models and methods for the analysis of environmental data distributed in space and time. The first stage of the hierarchical model specifies a measurement-error process for the observational data in terms of some'state'process. The second stage allows for site-specific time series models for this state variable. This stage in-cludes large-scale (e.g. seasonal) variability plus a space-time dynamic process for the 'anomalies'. Much of our interest is with this anomaly process. In the third stage, the parameters of these time series model, which are distributed in space, are them-selves given a joint distribution with spatial dependence (Markov random fields). The Bayesian formulation is completed in the last two stages by specifying priors on param-eters. We implement the model in a Markov chain Monte Carlo framework and apply it to an atmospheric data set of monthly maximum temperature of Anhui province.