Abstract
The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of the means and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for the assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution. Improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management.
In this work we introduce an innovative framework to assess model performance, not only based on the two first moments of the model outputs and field data, but also on their entire distributions. Our approach also compares the spatial dependence and variability in two data sources. More specifically, we estimate the spatial-quantile functions for both the model output and field data, and we apply a nonlinear monotonic regression approach to the quantile functions taking into account the spatial dependence to compare the density functions of numerical models and field data. We use a Bayesian approach for estimation and fitting to characterize uncertainties in data and statistical models.
We apply our methodology to assess the performance of the US Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model to characterize ozone ambient concentrations. Our approach shows a 50.23% reduction in the root mean square error (RMSE) compared to the default approach based on the first 2 moments of the model output and field data.
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Zhou, J., Fuentes, M. & Davis, J. Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions. JABES 16, 531–553 (2011). https://doi.org/10.1007/s13253-011-0076-4
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DOI: https://doi.org/10.1007/s13253-011-0076-4