Comparative Study of O3 Forecast Performance Using Multiple Models in Beijing–Tianjin–Hebei and Surrounding Regions

Abstract

In order to systematically understand the operational forecast performance of current numerical, statistical, and ensemble models for O3 in Beijing–Tianjin–Hebei and surrounding regions, a comprehensive evaluation was conducted for the 30 model sets regarding O3 forecasts in June–July 2023. The evaluation parameters for O3 forecasts in the next 1–3 days were found to be more reasonable and practically meaningful than those for longer lead times. When the daily maximum 8 h average concentration of O3 was below 100 μg/m3 or above 200 μg/m3, a significant decrease in the percentage of accurate models was observed. As the number of polluted days in cities increased, the overall percentage of accurate models exhibited a decreasing trend. Statistical models demonstrated better overall performance in terms of metrics such as root mean square error, standard mean bias, and correlation coefficient compared to numerical and ensemble models. Numerical models exhibited significant performance variations, with the best-performing numerical model reaching a level comparable to that of statistical models. This finding suggests that the continuous tuning of operational numerical models has a more pronounced practical effect. Although the best statistical model had higher accuracy than numerical and ensemble models, it showed a significant overestimation when O3 concentrations were low and a significant underestimation when concentrations were high. In particular, the underestimation rate for heavy polluted days was significantly higher than that for numerical and ensemble models. This implies that statistical models may be more prone to missing high-concentration O3 pollution events.