【摘要】 为了提高我国重污染区域PM_{2. 5}浓度预报准确率,基于4套国家级以及区域环境气象业务中心发展和维护的空气质量数值预报模式,通过均值集成、权重集成、多元线性回归集成和BP-ANNs集成分别建立集成预报,在实时预报效果评估基础上,建立了最优多模式集成预报。对2015—2016年预报效果进行评估,结果表明:相对于单个空气质量数值预报模式,均值和权重集成对预报偏差的改进幅度有限,但多元线性回归、BP-ANNs和最优集成能较大幅度降低预报偏差;最优集成预报与观测值间的归一化平均偏差（NMB）和均方根误差（RMSE）分别为-10%～10%和10～70μg/m3,且在更多的站点表现出强相关性,但依然低估了高污染等级的PM_{2. 5}浓度。对2018年2月25日—3月4日京津冀地区污染过程进行评估,结果表明:最优集成能较好预报出该过程中PM_{2. 5}浓度的变化趋势和量级;在北京、石家庄和郑州3个代表城市中,预报和观测值间的NMB和相关系数（R）分别为-26%～-4%和0. 49～0. 77;最优集成对轻度污染及中度污染的TS评分为0. 39～0. 73,重度污染及以上TS评分为0. 13～0. 30,能为预报员提供客观参考,但对污染峰值的预报能力还需进一步改进。更多还原

【Abstract】 To improve the forecast accuracy of PM_{2. 5} concentration in heavily polluted regions of China, ensemble forecasts were built by mean ensemble, weighted ensemble, multiple linear regression ensemble and back propagation artificial neural networks ensemble, respectively, based on four numerical air quality models developed and maintained by national or regional environmental metrological service centers. A best multi-model ensemble forecast was established based on real-time evaluations of performances of single numerical models and ensemble methods. Through evaluation of the forecast results during 2015-2016, compared with single numerical air quality forecast models, improvements on forecast biases due to mean and weighted ensembles were limited, but multiple linear regression, back propagation artificial neural networks and best ensembles could largely reduce the forecast biases. The NMB and RMSE values between best ensemble forecast and observation were from-10% to 10% and from 10 to 70 μg/m3, respectively. Best ensemble showed strong correlation with observations at more sites compared with other ensemble methods, but also underestimated PM_{2. 5} concentrations in high pollution level.During the pollution process occurred in Jing-Jin-Ji region from February 25 to March 4, 2018, best ensemble had the ability to forecast the trend and magnitude of PM_{2. 5} concentrations. In three representative cities of Beijing,Shijiazhuang and Zhengzhou, the NMB and R values between best ensemble and observations varied from 26% to4% and from 0. 49 to 0. 77, respectively. The TS scores of best ensemble for mild and moderate pollution ranged from 0. 39 to 0. 73, and that of severe and above pollution ranged from 0. 13 to 0. 30. These indicate that best ensemble can provide a strong objective reference to forecaster, but its forecast ability of peak values needs to be further improved.更多还原