主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2020, Vol. 36 ›› Issue (6): 130-136.doi: 10.3969/j.issn.1673-503X.2020.06.016

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东北区域空气质量数值预报模式应用研究进展

王扬锋1(),马雁军1,权维俊2,李荣平1   

  1. 1. 中国气象局沈阳大气环境研究所, 辽宁 沈阳 110166
    2. 京津冀环境气象预报预警中心, 北京 100089
  • 收稿日期:2020-08-07 出版日期:2020-12-30 发布日期:2021-01-06
  • 作者简介:王扬锋, 男, 1977年生, 副研究员, 主要从事大气环境分析与模拟研究, E-mail:wyf_7818@163.com
  • 基金资助:
    辽宁省科技厅重点研发指导计划项目(2019JH8/10300094);国家自然科学重点基金项目(41730647);国家重点研发计划项目(2017YFC0212301);中国气象局沈阳大气环境研究所基本科研业务费项目(2018SYIAEYJ1)

Progress of the application research of air quality models in Northeast China

Yang-feng WANG1(),Yan-jun MA1,Wei-jun QUAN2,Rong-ping LI1   

  1. 1. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
    2. Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, Beijing 100089, China
  • Received:2020-08-07 Online:2020-12-30 Published:2021-01-06

摘要:

中国气象局沈阳大气环境研究在2014年基于CUACE和CMAQ建立了东北区域空气质量数值预报业务系统,本文介绍了空气质量数值模式的研究进展以及业务现状,为东北区域空气质量和雾霾预报提供了技术支撑。然而,随着预报精细化和更长预报时效的业务发展需求,存在预报准确率不高、计算资源短缺、科技创新能力不足等问题。本文基于存在的问题提出了东北区域空气质量数值预报未来发展建议与展望,包括加强大气污染源清单研究与技术规范制定、观测资料同化技术研究与业务应用、物理过程参数化方案的改进优化、发展数值预报产品订正技术、开发高分辨率7—10 d数值预报产品、加强人才引进和科技创新等。

关键词: 空气质量模式, 业务应用, 发展建议

Abstract:

Since 2014, the Institute of Atmospheric Environment, China Meteorological Administration, Shenyang has built a forecasting system of air quality for operation in Northeast China based on CUACE and CMAQ models.The research progress of air quality models and the current status were introduced in this paper to provide technical support for air quality and fog-haze forecasting in Northeast China.With the development of operational requirements for refinement and longer forecasting timelines, there are some problems, such as low forecasting accuracy, shortage of computing resources, and lack of technological innovation ability, exiting in the current operating system.The counter-measures are proposed to improve the forecasting level of air quality models in Northeast China, including strengthening the inventory research of air pollution source and formulating technical guidelines, the research and operation application of observational data assimilation technology, the improvement and optimization of physical process parameterization schemes, methods for error correction of numerical forecast products, the development of high-resolution models and maximum prediction time for 7-10 days, and introducing of talents and strengthening technological innovation.

Key words: Air quality models, Operation application, Proposal for development

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