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Metamodels for Ozone: Comparison of Three Estimation Techniques
Citation:
Porter, P., S. Rao, C. Hogrefe, E. Gego, AND R. Mathur. Metamodels for Ozone: Comparison of Three Estimation Techniques. Chapter 86, Air Pollution Modeling and its Application XXIV. Springer International Publishing AG, Cham (ZG), Switzerland, , 537-542, (2016).
Impact/Purpose:
The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment. AMAD’s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation’s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
A metamodel for ozone is a mathematical relationship between the inputs and outputs of an air quality modeling experiment, permitting calculation of outputs for scenarios of interest without having to run the model again. In this study we compare three metamodel estimation techniques applied to an 18-year long CMAQ simulation covering the Northeastern US (NEUS). The estimation methods considered here include projection onto latent structures, stochastic kriging and a combination of principal components and stochastic kriging.
