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2019年江苏省PM2.5和O3多模式集合预报算法效果评估
Evaluation of the Multi-model Ensemble Forecasting Algorithm for PM2.5 and O3 in Jiangsu Province in 2019
投稿时间:2021-07-07  修订日期:2021-10-08
DOI:DOI:10.19316/j.issn.1002-6002.2022.04.21
中文关键词:  空气质量  数值模式  偏差分析  集合预报
英文关键词:air quality  numerical model  deviation analysis  ensemble forecasting
基金项目:江苏省PM2.5与臭氧污染协同控制重大专项(2019023)
作者单位
杨文夷 中国科学院大气物理研究所, 北京 100029 
皮冬勤 中国科学院大气物理研究所, 北京 100029 
汪琦 江苏省环境监测中心, 江苏 南京 210019 
晏平仲* 中国科学院大气物理研究所, 北京 100029 
余进海 江苏省环境监测中心, 江苏 南京 210019 
肖林鸿 中国科学院大气物理研究所, 北京 100029 
通讯作者:晏平仲*  中国科学院大气物理研究所, 北京 100029  
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中文摘要:
      基于江苏省重污染天气监测预报预警系统多模式预报结果,分析了不同数值模式对江苏省13个城市细颗粒物(PM2.5)和臭氧(O3)的预报偏差特征,发展了多模式集合预报算法,并对其进行了评估。结果表明,相较于单一数值模式,集合预报算法显著改善了PM2.5和O3预报的准确率,其对江苏省PM2.5和O3空气质量分指数等级的预报准确率超过了80%。就江苏省整体而言,PM2.5集合预报的准确率相比最优单一数值模式提升了6%。O3浓度较低时,集合预报能有效改善各模式存在的高估现象。但受限于目前的校正策略,出现高浓度O3污染时,集合预报对预报效果的提升相对有限。
英文摘要:
      Based on the results of the multi-model operational forecast system in Jiangsu Province,the characteristics of prediction bias of fine particulate matter (PM2.5) and ozone (O3) from different air quality models were analyzed.In addition,an ensemble algorithm was developed and the performance of this new algorithm was evaluated.The results showed that,compared with each model,the ensemble algorithm greatly improved the prediction accuracy of PM2.5 and O3 levels.The prediction accuracy of pollution levels for both PM2.5 and O3 in Jiangsu Province reached more than 80%.Overall,the ensemble forecasting algorithm improved the prediction accuracy of PM2.5 by approximately 6% relative to the single optimization numerical model.The overestimation of each air quality model was effectively reduced by the ensemble forecasting under low O3 concentration.However,limited by current correction strategy,there was no significant improvement by using the ensemble forecasting under high O3 concentration.
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