title: A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling.
abstract: A neural network based ensemble methodology was presented in this study to improve the accuracy of meteorological input fields for regional air quality modeling. Through nonlinear integration of simulation results from two meteorological models (MM5 and WRF), the ensemble approach focused on the optimization of meteorological variable values (temperature, surface air pressure, and wind field) in the vertical layer near ground. To illustrate the proposed approach, a case study in northern China during two selected air pollution events, in 2006, was conducted. The performances of the MM5, the WRF, and the ensemble approach were assessed using different statistical measures. The results indicated that the ensemble approach had a higher simulation accuracy than the MM5 and the WRF model. Performance was improved by more than 12.9% for temperature, 18.7% for surface air pressure field, and 17.7% for wind field. The atmospheric PM(10) concentrations in the study region were also simulated by coupling the air quality model CMAQ with the MM5 model, the WRF model, and the ensemble model. It was found that the modeling accuracy of the ensemble-CMAQ model was improved by more than 7.0% and 17.8% when compared to the MM5-CMAQ and the WRF-CMAQ models, respectively. The proposed neural network based meteorological modeling approach holds great potential for improving the performance of regional air quality modeling.
authors.affiliation: College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, PR China. chengsy@bjut.edu.cn
authors.forename: Shuiyuan
authors.initials: S
authors.lastname: Cheng
authors.forename: Li
authors.initials: L
authors.lastname: Li
authors.forename: Dongsheng
authors.initials: D
authors.lastname: Chen
authors.forename: Jianbing
authors.initials: J
authors.lastname: Li
mesh_terms: D000397:Air Pollution / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer