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

结合确定性模型输出和监测数据进行时空插值的多元方法

基本信息

批准号：
155802-2013
负责人：
Le, Nhu
金额：
$ 1.09万
依托单位：
University of British Columbia
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2014
资助国家：
加拿大
起止时间：
2014-01-01 至 2015-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=545726
关键词：
Multivariate methods combining deterministic model

项目摘要

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.

本研究计划的总体目标是开发多变量方法，将监测测量和确定性（物理）模型在环境场（空气污染物、温度、降水）的时空预测中的输出相结合。虽然来自监测网络的测量（可能有偏差）是对微观环境的实现，但确定性模型的输出代表宏观环境(例如。MAQSIP: 6公里× 6公里网格单元)。该研究项目的意义在于为环境过程的测量和确定性模型输出相结合提供新的一般统计理论，以改进空间和时间预测。这项研究将有广泛的应用。这些措施包括改进对健康影响研究的接触评估；改善网络监测，以最好地保护人口；加强环境风险管理，例如通过预测干旱和洪水，以及飓风等极端气候条件。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。有几个项目适合博士论文和硕士论文。这将为研究培训提供极好的机会。