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To enhance the independence and controllability of regional atmospheric environment numerical prediction and its capability to support air quality forecast and air pollution warning, a CMA-GFS driving numerical prediction system (RAEMS-CMA) was established and operationally implemented. This system was based on the NCEP-GFS driving Regional Atmospheric Environmental Modeling System for eastern China (RAEMS-NCEP). The forecast effects evaluation on the major air pollutants over the YRD region from July 4^th, 2023, to July 3^rd, 2024, was executed. Evaluation results showed that RAEMS-CMA had good performance in forecasting the major air pollutants, e.g. correlation coefficients were generally over 0.7 and root mean square errors were 30%~70% of corresponding observation means. The performance was consistent in different forecast lengths of 24h ~ 96h. Further analysis indicated that the performance differs from air pollutants, with better skills in forecasting PM_2.5 and O₃. The prediction of PM₁₀ was underestimated, while the others were overestimated. It also showed better forecast capability over the north region than over the south. The intercomparison indicated that RAEMS-CMA had slightly superior performance to that of RAEMS-EC (which was driven by ECMWF) and accordant to RAEMS-NCEP, while their differences were usually less than 5%. These results proved the applicability of CMA-GFS in numerical air quality prediction over the YRD region. At the same time, the divergent performance in pollutants and regions suggests further efforts on technological improvements and application approaches to numerical air quality prediction.

, authors=Guang-qiang ZHOU^*, Jian-ming XU, Chang-qin YIN, Zhuo-cheng MAO, Zhong-qi YU, Yuan-hao QU, authorsList=Guang-qiang ZHOU, Jian-ming XU, Chang-qin YIN, Zhuo-cheng MAO, Zhong-qi YU, Yuan-hao QU, authorCompany=null, correspAuthors=Guang-qiang ZHOU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314917477822333653, articleId=1314917477319017171, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=CMA-GFS驱动长三角主要大气污染物数值预报, columnId=1241049969449292093, journalTitle=中国环境科学, columnName=臭氧污染与控制, runingTitle=null, highlight=null, articleAbstract=

为强化区域大气环境数值预报的自主可控和对空气质量预报预警业务的支撑能力，基于华东区域大气环境数值预报系统研制了CMA-GFS驱动数值预报系统，分析了2023年7月4日~2024年7月3日长三角地区主要大气污染物的预报效果及其与NCEP-GFS和ECMWF驱动预报效果的差异．结果显示，CMA-GFS驱动预报对主要污染物具有良好的预报效果，相关系数一般超过0.7、均方根误差约为观测均值的30%~70%，不同时效的预报能力接近；不同污染物的预报效果之间存在性能差异和空间不同，北部优于南部，PM_2.5和O₃预报效果较好，PM₁₀系统性低报而其他要素不同程度高报．CMA-GFS驱动的预报效果略优于ECMWF驱动而与NCEP-GFS驱动相当，但相互之间的差异一般不超过5%．评估结果证实了CMA-GFS应用于长三角地区空气质量数值预报的适用性，但不同表现的预报偏差表明还需要开展针对性的数值预报技术改进和应用方法研究．

, authors=周广强^*, 许建明, 殷长秦, 毛卓成, 余钟奇, 瞿元昊, authorsList=周广强, 许建明, 殷长秦, 毛卓成, 余钟奇, 瞿元昊, authorCompany=null, correspAuthors=周广强, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=DlDFYoq3to3aIe1rj2S0iQ==, magXml=0tKQsxT7QlonvMkz4Jijzg==, pdfUrl=null, pdf=oRJousXDbtC4ULKAFJXw3w==, pdfFileSize=2241562, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=wAChXDROcZWX0y665YKfEw==, mapNumber=null, fund=null)}, authors=[Author(id=1314917478094963418, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917477319017171, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=zhougq21@163.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1314917478170460892, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917477319017171, authorId=1314917478094963418, language=EN, stringName=Guang-qiang ZHOU, firstName=Guang-qiang, middleName=null, lastName=ZHOU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^*, address=Yangtze River Delta Center for Environmental Meteorology Prediction and Warning, Shanghai 200030, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314917478233375453, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917477319017171, authorId=1314917478094963418, language=CN, stringName=周广强, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^*, address=长三角环境气象预报预警中心，上海 200030, bio={"content":"

周广强（1976-），男，四川安岳人，教授级高级工程师，博士，主要从事大气环境研究与数值预报工作．发表论文60余篇．zhougq21@163.com.

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周广强（1976-），男，四川安岳人，教授级高级工程师，博士，主要从事大气环境研究与数值预报工作．发表论文60余篇．zhougq21@163.com.

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