Based on 31 aviation records of low-level wind shear, surface observational data at Shijiazhuang Zhengding international airport of Hebei Province and NCEP/NCAR reanalysis data from 2014 to 2017, the characteristics of low-level wind shear and corresponding weather situations were analyzed. The results show that the low-level wind shear mainly occurred in the afternoon and nightfall, with the peak at 14:00 BST. The majority of low-level wind shear events occurred in spring, and the low-level wind shear events occurred most in April. The number of low-level wind shear events at Zhengding international airport was the highest in 2015. The mainly weather situations which were conducive to occurrence of low-level wind shear events were northwest airflow type, vortex type, west wind trough type and horizontal trough type. Furthermore, low-level wind shear occurred most under the weather situation of northwest airflow type, especially in spring and winter. The convective weather under the vortex weather situation was prone to occurrence of low-level wind shear. In addition, more attention should be paid on 3 hours and 1 hour anallobaric and negative temperature variation under the northwest airflow weather situation, as well as allobaric and variable temperature in a short time under the vortex weather situation for forecasting low-level wind shear.

Based on the NCEP reanalysis data, conventional observation data and microwave radiometer data, the background field, characteristics of the boundary layer and formation cause and maintenance mechanism of a sustained heavy fog process during 11-15 January 2019 in central and southern Hebei Province were analyzed. The results show that the fog generated under the background of stable zonal circulation, during which weak short-wave troughs moved eastward quickly, and two dry short-wave troughs promoted the formation of heavy fog and the strengthening of the fog, respectively. The fog was mainly radiation fog and only a few tens of meters in  height. The two-layer structure of temperature inversion layer, weak water vapor convergence and southwest water vapor transport  on the ground inversion layer provided water vapor source for heavy fog, the cooling of weak cold air caused by northerly wind and cooling of radiation at night were thermodynamic conditions for the development of the fog. The positive vorticity in the lower layer and the divergence and sinking in the middle troposphere were dynamic factors promoting maintainess  and development of the fog, the combination of long-wave radiation in the clear night sky and turbulent mixing promoted cooling and humidification in the boundary layer. During the later period of the heavy fog, the temperature inversion layer changed from two-layer structure to a single-layer structure. The dissipation of the fog was caused by the intrusion of strong cold air and destruction of stationary structure.

The method of road surface temperature prediction for 2022 winter Olympic demonstration station (Beijing Huilongguan station) was discussed based on the METRo model. The artificial thermal parameters of iterative training fitting with a large number of sample data were added to the METRo model as the leading parameters in order to reduce the systematic error of the METRo model and the influence of human production and life on road surface temperature prediction. The results are as follows: (1) After introduction of artificial heat parameters, the simulation ability of the METRo model was improved significantly , especially at night, the root mean square error of road surface temperature prediction could be controlled at about 1 ℃. (2) The effect of artificial heat on road surface temperature presented negative feedback during daytime and positive feedback at night. (3) The simulated road surface temperature still had some errors due to influence of limitation of radiation prediction ability of meteorological models. In the mass, it was feasible to simulate the road surface temperature, especially for low temperature of road surface at the winter Olympic expressway demonstration station by using the METRo model with pre-set anthropogenic thermal parameters, which could support the prediction and early warning ability of highway road surface temperature and road icing in winter.

Based on the hourly minimum visibility, precipitation and relative humidity of 61 national meteorological stations during the period 2016-2017, the characteristics of low visibility along eight main highways in Hebei Province were investigated. The results show that low visibility appeared most in autumn and winter while least in spring. In autumn and winter, the 02:00-03:00, 03:00-07:00, 05:00 were the peak hours for the appearance of  low visibility of 200-500 m, 50-200 m and 0-50 m, respectively. The frequency of  low visibility along Daqing-Guangzhou highway (Beijing-Hengshui section and Hengshui-Daming section), Shijiazhuang-Anyang highway and Qingdao-Yinchuan highway was higher, while it was lower along Beijing-Qinhuangdao highway and Coastal highway. The low visibility of 200-500 m usually appeared from 20:00 to 07:00. The frequency of low visibility of 200-500 m along Hengshui-Daming section, Shijiazhuang-Anyang highway and Qingdao-Yinchuan highway was higher after 00:00, while it was opposite for Beijing-Qinhuangdao highway. The visibility of 50-200 m along Beijing-Hengshui section and Qingdao-Yinchuan highway occured mainly during 04:00-07:00, and it occured maily during 05:00-08:00 along Beijing-Shanghai highway and Shijiazhuang-Huanghua highway. The peak time of frequency of the visibility of 50-200 m occured during 03:00-07:00 along Shijiazhuang-Anyang highway. The frequency of the visibility of 0-50 m along various highways decreased significantly.The peak value of low visibility of 0-50 m occured mainly at 00:00 and 05:00 along Beijing-Hengshui section(Raoyang-Anping section), while it occurred during 03:00-07:00 along Hengshui-Daming section. The low visibility of 0-50 m was easier  to  occure along Botou section of Shijiazhuang-Huanghua highway than other sections  of  that.