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崔驰潇, 包云轩, 袁成松, 周林义, 焦圣明, 宗晨. 不同边界层参数化方案对江苏地区一次平流雾过程的模拟影响[J]. 大气科学, 2018, 42(6): 1344-1362. DOI: 10.3878/j.issn.1006-9895.1801.17212
引用本文: 崔驰潇, 包云轩, 袁成松, 周林义, 焦圣明, 宗晨. 不同边界层参数化方案对江苏地区一次平流雾过程的模拟影响[J]. 大气科学, 2018, 42(6): 1344-1362. DOI: 10.3878/j.issn.1006-9895.1801.17212
Chixiao CUI, Yunxuan BAO, Chengson YUAN, Linyi ZHOU, Shenming JIAO, Chen ZONG. Influence of Different Boundary Layer Parameterization Schemes on the Simulation of an Advection Fog Process in Jiangsu[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1344-1362. DOI: 10.3878/j.issn.1006-9895.1801.17212
Citation: Chixiao CUI, Yunxuan BAO, Chengson YUAN, Linyi ZHOU, Shenming JIAO, Chen ZONG. Influence of Different Boundary Layer Parameterization Schemes on the Simulation of an Advection Fog Process in Jiangsu[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1344-1362. DOI: 10.3878/j.issn.1006-9895.1801.17212

不同边界层参数化方案对江苏地区一次平流雾过程的模拟影响

Influence of Different Boundary Layer Parameterization Schemes on the Simulation of an Advection Fog Process in Jiangsu

  • 摘要: 边界层参数化方案的选取在平流雾的预报准确度上起着决定性的作用。本文利用WRF模式对2013年3月18~19日发生在江苏地区的一次平流雾过程进行数值模拟试验,对耦合不同闭合方式边界层参数化方案的试验结果与实测气象数据进行对比分析,评估了他们对此次平流雾的模拟效果,探讨了边界层高度对此次平流雾的生成和发展的影响。研究结果表明:(1)耦合不同边界层方案的WRF模式对地面气象要素的模拟结果均呈现气温偏低、湿度和风速偏大的特征。(2)QNSE方案对气温的模拟能力最强;ACM2方案对相对湿度的模拟性能最好;YSU方案对风速的模拟效果最佳。不同边界层方案的模拟结果在垂直方向上的差别主要表现在低空相对湿度上:QNSE方案预报的湿度更大。(3)综合TS(Threat Score)和BS(Bias Score)两个评分指标来看,ACM2方案对雾区分布的模拟效果最好。三个边界层方案对此次平流雾的模拟结果在江苏沿海站点的预报评分较高,在距海较远站点的预报评分表现较差。YSU方案对东南沿海地区的雾区预报评分较高;QNSE方案对长江沿江区域的雾区预报评分较高;ACM2方案对沿海地区、尤其对沿海北部地区的有较好的预报效果。(4)QNSE方案对此次平流雾的生成时间、出现地点预报比较准确。(5)平流雾的生成与发展阶段模拟雾区覆盖范围与边界层高度关系十分紧密,适当强度的湍流混合作用有助于平流雾在地面的生成与发展;但是过强的湍流混合作用会导致大雾过早的消散。

     

    Abstract: The simulation and prediction accuracy of advection fog depends on the selection of boundary layer parameterization scheme. Coupled with different schemes of boundary layer parameterization, the Weather Research and Forecasting (WRF) model was used in this paper to simulate an advection fog process occurred in Jiangsu during the period from March 18th to 19th in 2013 and the simulation results are compared with observed meteorological data to analyze and evaluate the simulation of the advection fog. The results of the experiments are as follows. (1) The surface air temperature is underestimated but the relative humidity and wind speed are overestimated. (2) Among the schemes of Quasi-Scale Elimination (QNSE), Asymmetrical Convective Model version 2 (ACM2) and Youngstown State University Scheme (YSU), the QNSE scheme has the best ability to simulate air temperature, the ACM2 scheme has the best performance for the simulation of relative humidity and the YSU scheme performs best in the the simulation of wind speed. The main difference between the three boundary layer parameterization schemes for the simulation of meteorological elements in the vertical direction is the difference in the simulated relative humidity at low levels and the QNSE scheme was the most humid. (3) According to the Scoring Standards TS (Threat Score) and BS (Bias Score), the simulation effect of the ACM2 scheme is the best one among the three schemes. The simulation results of the three boundary layer parameterization schemes for this advection fog process are better at the coastal sites of Jiangsu, and the prediction scores at the sites far away from the sea are worse. The YSU scheme has higher score on the fog area forecast in the southeastern coastal area, the QNSE scheme has higher score on the fog area forecast in the Yangtze River Valley and the ACM2 scheme has good simulation effect on the fog area forecast in the coastal areas, especially in the northern coastal areas. (4) The QNSE scheme is more accurate in the prediction of the formation time and occurrence location of the advection fog. (5) The simulated fog coverage in the formation and development stages of the advection fog is closely related to the height of the boundary layer, and the turbulent mixing with an appropriate intensity would contribute to the formation and development of advection fog on the ground, but too strong turbulence mixing would lead to premature fog dissipation.

     

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