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NONSTATIONARY SPATIAL MODELING OF ENVIRONMENTAL DATA USING A PROCESS CONVOLUTION APPROACH
Citation:
Swall, J L. NONSTATIONARY SPATIAL MODELING OF ENVIRONMENTAL DATA USING A PROCESS CONVOLUTION APPROACH. Presented at 2004 Joint Statistical Meeting, Toronto, Ontario, Canada, August 8-12, 2004.
Impact/Purpose:
The goal of this task is to thoroughly characterize the performance of the emissions, meteorological and chemical/transport modeling components of the Models-3 system, with an emphasis on the chemical/transport model, CMAQ. Emissions-based models are composed of highly complex scientific hypotheses concerning natural processes that can be evaluated through comparison with observations, but not validated. Both performance and diagnostic evaluation together with sensitivity analyses are needed to establish credibility and build confidence within the client and scientific community in the simulations results for policy and scientific applications. The characterization of the performance of Models-3/CMAQ is also a tool for the model developers to identify aspects of the modeling system that require further improvement.
Description:
Traditional approaches to modeling spatial processes involve the specification of the covariance structure of the field. Although such methods are straightforward to understand and effective in some situations, there are often problems in incorporating non-stationarity and in manipulating the large covariance matrices that result when dealing with large datasets. Our approach takes a different perspective, modeling a process as a convolution of a Gaussian white noise process and suitable kernels. Depending on the particular parameterization, this approach can allow flexibility in modeling non-stationary processes, while avoiding the task of working directly with the covariance matrix. In this talk, we discuss some relevant approaches, and present an application involving environmental monitoring. In particular, we focus on such practical issues as computational efficiency and methods for assimilating data from differing sources.
This is an abstract of a proposed presentation and does not necessarily reflect the United States Environmental Protection Agency (EPA) policy. The actual presentation has not been peer reviewed by EPA. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.