京津冀及周边地区冬季能见度与PM2.5浓度和环境湿度的多元回归分析

2013年至今，中国冬季与雾霾相伴的低能见度事件频发，京津冀及周边地区尤为严重。PM_2.5浓度与环境湿度是导致低能见度的最关键影响因素。为了深入研究PM_2.5浓度与环境湿度对大气能见度的影响，利用2017年1月京津冀及周边地区MICAPS气象数据与PM_2.5观测数据，运用天气学诊断分析方法讨论了不同相对湿度下PM_2.5浓度、环境湿度对冬季能见度变化的相对贡献，按照地理环境与污染程度差异将京津冀及周边地区划分为北京-天津地区与河北-山东地区，建立了PM_2.5浓度与环境湿度（由露点温度、温度代表）对能见度的多元回归方程，并对2015、2016、2018、2019年冬季能见度进行了回算检验。结果显示:相对湿度低于70%、PM_2.5浓度低于75 μg/m3时，北京-天津地区与河北-山东地区能见度多高于10 km，PM_2.5浓度升高是此时能见度迅速降低的主导因素；相对湿度从70%上升至85%和PM_2.5浓度从75 μg/m3升高200 μg/m3的共同作用导致了能见度降低到10 km至5 km；能见度进一步从5 km下降至2 km则更多依赖于相对湿度进一步从85%升高至95%，PM_2.5浓度与此时能见度相关减弱；能见度降低至2 km甚至更低主要是由于水汽近饱和状态下（相对湿度95%以上）的雾滴消光引起，与PM_2.5浓度的变化关系不大。与不分组直接拟合相比，以相对湿度85%为界线，分别拟合能见度能够很大程度优化多元回归模型，相对湿度高于85%时能见度拟合值的均方根误差从9.2和5.2 km下降至0.5和0.7 km，5 km以下拟合能见度的误差大幅度减小。按相对湿度85%将数据分组所得的拟合方程对2015、2016、2018、2019年1月能见度估算结果较好，观测值与拟合值相关系数均高于0.91，为雾-霾数值预报系统提供了新的能见度参数化算法。 

Multiple regression analysis of winter visibility，PM2.5 concentration and humidity in Beijing-Tianjin-Hebei and its surrounding regions

Since 2013, low visibility events have been repeatedly observed in Beijing-Tianjin-Hebei and its surrounding regions. PM_2.5 concentration and humidity are considered to be key factors leading to low visibility. Using surface meteorological data from MICAPS and PM_2.5 concentration observation data from the China Environmental Monitoring Center, the influences of PM_2.5 and humidity on visibility under different relative humidity (RH) and pollution levels are investigated. According to the differences in geographical environment and pollution degree, the study region was divided into Beijing-Tianjin and Hebei-Shandong regions. The multiple regression equations of visibility, PM_2.5 concentration, temperature and dew point temperature are established based on data of January 2017, and these equations are tested using the data of January 2015, 2016, 2018 and 2019. Results show that when RH＜70% and PM_2.5 concentration＜75 μg/m3, the visibility in Beijing-Tianjin region and Hebei-Shandong region is usually higher than 10 km. The increase in PM_2.5 concentration is the dominant factor for the rapid decrease in visibility. The combination of increase in RH (70%—85%) and increase in PM_2.5 concentration (75—200 μg/m3) can result in further decrease of visibility (10—5 km). The decrease in visibility (5—2 km) is mostly depended on further increase in RH (85%—95%), while the correlation between PM_2.5 concentration and visibility becomes weaker in this situation. The decrease in visibility to 2 km or even lower is mainly due to the extinction of droplets under the near saturation of water vapor (RH＞95%), and has little relation with changes in PM_2.5 concentration. Compared with establishing the visibility fitting equation directly without grouping, establishing the visibility fitting equation according to the RH above or below 85% respectively can greatly optimize the multivariate regression models. The RMSEs for visibility fittings with RH＞85% decreases from 9.2 and 5.2 km to 0.5 and 0.7 km. The visibility in January of 2015, 2016, 2018 and 2019 are well reproduced by these fitting models. Correlation coefficients between the observed visibility and the calculated visibility all are higher than 0.91. This study provides a new visibility parameterization for the haze-fog numerical prediction system.