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

这项研究计划的总体目标是开发多变量方法，用于在环境领域(空气污染物、温度、降水)的空间和时间预测中结合监测测量和确定性(物理)模型的输出。虽然来自监测网络的测量是(可能有偏差的)微观环境的实现，但确定性模型的输出代表了宏观环境(例如。MAQSIP：6千米x 6千米网格单元)。该研究计划的意义将是新的通用统计理论，用于结合环境过程的测量和确定性模型输出，以改进空间和时间预测。这项研究将有广泛的应用。这些措施包括改进健康影响研究的暴露评估；改进网络监测以最好地保护人类人口；以及通过预报干旱和洪水以及飓风等极端气候条件来加强环境风险管理。还可以为确定性模型输出提供不同尺度的分辨率，从而避免计算负担。有几个项目适用于博士学位论文和硕士学位论文。这将为研究培训提供极好的机会。

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.