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Evaluation of an Air Pollution Analysis System for Complex Terrain

D. G. RossCentre for Applied Mathematical Modelling, Monash University, Melbourne, Australia

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D. G. FoxRocky Mountain Forest and Range Experiment Station, USDA, Fort Collins, Colorado

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Abstract

This paper describes results from a study to evaluate components of an operational air quality modeling system for complex terrain. In particular, the Cinder Cone Butte (CCB) “modeler's dataset” is used to evaluate the current technique for incorporating terrain influences and atmospheric stability into the system's 3D diagnostic wind-field model.

The wind-field model is used in conjunction with a Gaussian puff model to compare predicted and observed tracer concentrations for different configurations, chosen to highlight the influence of the model's technique for incorporating terrain and atmospheric stability in the final flow field. A quantitative statistical basis, including the use of a bootstrap resampling procedure to estimate confidence limits for the performance measures, is used for the evaluation. The results show that the model's technique for incorporating terrain and atmospheric stability yields a significant improvement in predictive performance. Even when only routinely available input data are used, the performance is shown to be as good as that of models based directly on the CCB dataset itself.

Abstract

This paper describes results from a study to evaluate components of an operational air quality modeling system for complex terrain. In particular, the Cinder Cone Butte (CCB) “modeler's dataset” is used to evaluate the current technique for incorporating terrain influences and atmospheric stability into the system's 3D diagnostic wind-field model.

The wind-field model is used in conjunction with a Gaussian puff model to compare predicted and observed tracer concentrations for different configurations, chosen to highlight the influence of the model's technique for incorporating terrain and atmospheric stability in the final flow field. A quantitative statistical basis, including the use of a bootstrap resampling procedure to estimate confidence limits for the performance measures, is used for the evaluation. The results show that the model's technique for incorporating terrain and atmospheric stability yields a significant improvement in predictive performance. Even when only routinely available input data are used, the performance is shown to be as good as that of models based directly on the CCB dataset itself.

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