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The Impact of Spatial Correlation and Incommensurability on Model Evaluation
Citation:
SWALL, J. AND K. FOLEY. The Impact of Spatial Correlation and Incommensurability on Model Evaluation. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 43(6):1159-1376, (2009).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling Division (AMD) conducts research in support of EPA′s mission to protect human health and the environment. AMD′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. AMD 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 AMD 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:
Standard evaluations of air quality models rely heavily on a direct comparison of monitoring data matched with the model output for the grid cell containing the monitor’s location. While such techniques may be adequate for some applications, conclusions are limited by such factors as the sparseness of the available observations and the incommensurability between volume-average and pointwise observations. We examine several sets of simulations to illustrate the effect of incommensurability in a variety of cases distinguished by the type and extent of spatial correlation present. Lastly, we apply this method to actual data and discuss the practical importance of understanding the impact of spatial correlation structure and incommensurability.
