Multi-model Evaluation and Bayesian Model Averaging in Quantitative Air Quality Forecasting in Central China

Abstract

There has been much interest in air pollution and the forecasting skill of air quality models in China since winter 2013. Different air quality models use different parameters (e.g., meteorological fields, emission sources and the initial concentrations of pollutants) and therefore their forecast results tend to have large systematic and random errors. We evaluated the concentrations of six pollutants in Henan Province predicted by three air quality models—the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, the Nested Air Quality Prediction (NAQP) model and the Community Multiscale Air Quality (CMAQ) model. We then established multi-model ensemble Bayesian model averaging (BMA). The prediction effect for PM2.5 and O3 was ranked as CUACE > CMAQ > NAQP and the prediction effect for SO2, NO2 and CO was CMAQ > NAQP > CUACE. All the models systematically underestimated O3 and heavy PM2.5 pollution events. For PM2.5 concentrations with a 24-h lead time, the root-mean-square error of BMA decreased by 35, 37, 68 and 50%, respectively, in winter, spring, summer and autumn relative to the CUACE model, whereas the normalized mean bias of BMA decreased by 67, 83, 94 and 55%, respectively, for O3 in the four seasons. Compared with the CMAQ model, the root-mean-square error of the SO2, NO2 and CO forecasts by BMA were reduced by 29, 33 and 39%, respectively. The evolution of the concentrations of the six pollutants during a heavy pollution event predicted by BMA was consistent with the observations.