Calibration of Air Quality Deterministic Models Using Nonparametric Spatial Density Functions

• Activity Number: 420
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Jingwen Zhou*+ and Montse Fuentes
• Companies: North Carolina State University and North Carolina State University
• Keywords: Model calibration ; Quantile regression ; Nonlinear monotonic regression ; Nonparametric Bayes method

Abstract

The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection.The objective comparison of mean and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for assessment of model performance.One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution,and 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,but on their entire distribution.Our approach also compares the spatial dependence and variability in both models and data. More specifically,we estimate the spatial quantile functions for both models and data,and we apply a nonlinear monotonic regression approach on 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.