| 京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究 |
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| 引用本文： | 侯梦玲,王宏,赵天良,车慧正.京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究[J].大气科学,2017,41(6):1177-1190. |
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| 作者姓名： | 侯梦玲 王宏 赵天良 车慧正 |
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| 作者单位： | 1.南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心/中国气象局气溶胶与云降水重点开放实验室, 南京 210044 |
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| 基金项目： | 科技部大气污染专项项目2016YFC0203306，国家重点基础研究发展计划（973计划）项目2014CB441201，国家自然科学基金项目41590874 |
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| 摘 要： | 本文利用GRAPES_CUACE大气化学模式对京津冀地区2015年12月重度雾霾过程进行了模拟和评估。京津冀地区能见度和PM2.5模拟值与观测值的对比表明:该模式能较好地模拟京津冀地区能见度和PM2.5的逐日变化情况，但模式存在对伴随着重污染发生的低能见度模拟偏高的问题。以12月5~10日的重度雾霾过程为重点，针对地面风速、边界层高度、相对湿度、PM2.5及其对能见度的影响进行了详细分析，研究结果表明:污染过程中大部分地区过程平均风速低于2 m s-1，边界层平均高度低于600 m，相对湿度较高。模式低能见度模拟偏高可能因为:（1）模式模拟重雾霾时段的PM2.5极大值浓度偏低。（2）模拟相对湿度存在系统性偏低的误差，这一误差对能见度的影响表现为两方面，一是相对湿度会通过影响可溶性气溶胶的吸湿增长过程影响气溶胶质量浓度，导致气溶胶消光系数的计算偏低；二是目前模式中采用的能见度的参数化公式考虑了相对湿度对气溶胶吸湿增长的影响，没有考虑雾滴的直接消光作用。
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| 关 键 词： | 雾霾 PM2.5 能见度 相对湿度 GRAPES_CUACE模式 |
| 收稿时间： | 2016/8/5 0:00:00 |
A Modeling Study of the Visibility and PBL Key Meteorological Elements during a Heavy Fog-Haze Episode in Beijing-Tianjin-Hebei of China |
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| HOU Mengling,WANG Hong,ZHAO Tianliang and CHE Huizheng.A Modeling Study of the Visibility and PBL Key Meteorological Elements during a Heavy Fog-Haze Episode in Beijing-Tianjin-Hebei of China[J].Chinese Journal of Atmospheric Sciences,2017,41(6):1177-1190. |
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| Authors: | HOU Mengling WANG Hong ZHAO Tianliang and CHE Huizheng |
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| Institution: | Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-CloudPrecipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044,State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081,Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory for Aerosol-CloudPrecipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044 and State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| Abstract: | The heavy fog-haze episode occurred in Beijing-Tianjin-Hebei region during December 5-10, 2015 was simulated using GRAPES_CUACE (Global/Regional Assimilation and PrEdiction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environment). Characteristics of wind speeds at the surface, PBLH (Planetary boundary layer height), relative humidity and PM2.5 concentration as well as their impacts on visibility were analyzed in detail. The results show that the diurnal variation of visibility and PM2.5 mass concentration in Beijing-Tianjin-Hebei region could be well simulated while the low visibility during the heavy fog-haze pollution was over estimated. The average wind speed was lower than 2 m s-1 and the average PBLH was lower than 600 m in most areas during the fog-haze episode. The overestimation of the low visibility by the model are due to the following reasons according to the simulation analysis. First, the maximum concentration of PM2.5 during the heavy fog haze episode was underestimated; second, the simulated relative humidity was lower than observations, which affected the hygroscopic growth process of soluble aerosols and the concentration of aerosols, leading to changes in aerosol extinction; and the current parametric formula doesn''t take into consideration the direct extinction of fog-drops. These results indicate that the simulation and prediction of surface relative humidity have important impacts on accurate simulation of low visibility during heavy fog-haze episodes. |
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| Keywords: | Fog-haze PM2 5 Visibility Relative humidity GRAPES_CUACE model |
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