ECMWF气象场驱动长三角PM_(2.5)预报与最优集成

Forecast of PM_(2.5) over Yangtze River Delta by ECMWF data driving and optimal integration

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.

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(2)the TS score for PM_(2.5) pollution degree improved by 14%. The application of OCF basically eliminated the overestimation of RAEMS-EC.