Multivariate methods for combining deterministic model output and monitoring data for spatial-temporal interpolation

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The overall objective of this research program is to develop multivariate methods for combining monitoring measurements and output from deterministic (physical) models in the spatial and temporal prediction of environmental fields (air pollutants, temperature, precipitation). While the measurements from monitoring networks are (possibly biased) realizations of the micro environment, the output from deterministic models represents the macro environment (eg. MAQSIP: 6km x 6km grid cells). The significance of the research program would be new general statistical theories for combining measurements and deterministic model outputs of environmental processes, to improve spatial and temporal prediction. The research will have a wide variety of applications. These include improved exposure assessment for health impact studies; improved network monitoring to best protect human population; and enhanced environmental risk management through, for examples, forecasting drought and flood, as well as extreme climate conditions such as hurricanes. It is also possible to provide different scales of resolution for the deterministic model output, thus avoiding the computational burden. Several projects would be suitable for PhD dissertations and MSc theses. That would provide excellent opportunities for research training.