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研究论文

Forecast of PM_2.5 over Yangtze River Delta by ECMWF data driving and optimal integration

摘要：采用华东区域大气环境数值预报业务系统（RAEMS-GFS）的整体框架和欧洲中期天气预报中心（ECMWF）高分辨率数值天气预报数据，建立了ECMWF气象场驱动的区域空气质量数值预报系统（RAEMS-EC）.评估结果显示：RAEMS-EC对2019年秋冬季长三角城市PM_2.5浓度和污染程度具有良好的预报准确性，其性能与RAEMS-GFS具有高度可比性的同时也存在一定的差异，数值上则有较明显的系统性偏高.RAEMS-EC与RAEMS-GFS双模式最优集成预报（OCF）可以大幅提升预报效果，长三角各城市PM_2.5浓度总体预报效果指标提升12%~83%，各指标在80%以上城市为正效果，PM_2.5污染预报TS评分也得到明显提升（14%），OCF基本消除了数值预报系统性偏高的不足.

Abstract：In this paper, the Regional Atmospheric Environmental Modeling System for eastern China driven by ECMWF high resolution numerical prediction data (RAEMS-EC) was established based on the framework of RAEMS driven by GFS data (RAEMS-GFS). The evaluation results showed that RAEMS-EC had high prediction accuracy for both PM_2.5 concentration and pollution degree over the Yangtze River Delta region in autumn and winter of 2019. The performance of RAEMS-EC indicated some difference from that of RAEMS-GFS although they had high similarity. Moreover, RAEMS-EC obviously overestimated the concentration of PM_2.5. The optimal consensus forecast (OCF) was applied to assemble RAEMS-EC and RAEMS-GFS and it significantly improved the forecast capability: ① the forecast capability indexes for city PM_2.5 concentration were improved by 12%~83%, which gave positive effect for each index over 80% cities;and ②the TS score for PM_2.5 pollution degree improved by 14%. The application of OCF basically eliminated the overestimation of RAEMS-EC.