摘要:本文在嵌套网格空气质量预报模式系统（NAQPMS）的基础上，结合蒙特卡罗模拟方法搭建了多扰动的空气质量集合预报系统。利用该系统对京津冀及其周边地区“2+26”城市的PM2.5浓度进行预报试验，试验时段为2017年9～12月，模式水平分辨率为15 km。研究发现，基于蒙特卡罗集合预报系统，采用“集合样本优选”均值集成法能显著提升PM2.5预报精度，大幅减小预报偏差。与所有集合样本的均值集成法相比，该方法将PM2.5预报均方根误差（RMSE）由58.0 μg m?3降低至34.7 μg m?3，将模拟—观测两倍因子百分比（FAC2）由67%提升至87%。此外，“集合样本优选”均值集成法对各污染等级的整体预报效果优于均值集成法。本文结果可为改进城市PM2.5预报效果和减小PM2.5预报偏差提供参考。
关键词:集合预报/
蒙特卡罗方法/
“2+26”城市/
“集合样本优选”/
均值集成
Abstract:In this study, a multi-perturbation air quality ensemble prediction system is developed, based on the Nested Air Quality Prediction Modeling System (NAQPMS), by the Monte Carlo simulation method. The system is used to predict the PM2.5 concentration of the "2+26" cities in Beijing–Tianjin–Hebei. Further, the period of the experiment is from September to December 2017, with a horizontal resolution of 15 km. This study found that the method of ensemble mean after selecting ensemble samples can significantly improve the accuracy of PM2.5 forecasting and greatly reduce the forecasting bias in the system, based on the Monte Carlo ensemble forecasting system. Compared with the ensemble mean method of all the samples, this method reduces the RMSE of PM2.5 forecast from 58.0 μg m?3 to 34.7 μg m?3, increasing the fraction of prediction within a factor of two of observations from 67% to 87%. Furthermore, the ensemble mean method after selecting ensemble samples is better than the ensemble mean method for the overall forecast of each pollution level. The results of this study can provide a reference for improving the impact of the urban PM2.5 forecast and reducing the bias of the PM2.5 forecast.
Key words:Ensemble forecast/
Monte Carlo method/
"2+26" cities/
Select ensemble sample/
Ensemble mean
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