Analysis of fog at Xianyang Airport based on multi-source ground-based detection data

被引:9
|
作者
Ming, Hu [1 ,3 ]
Wei, Ming [1 ]
Wang, Minzhong [2 ]
Gao, Lianhui [3 ]
Chen, Lijie [3 ]
Wang, Xiucheng [3 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Jiangsu, Peoples R China
[2] Chinese Meteorol Adm, Urumqi Inst Desert Meteorol, Urumqi, Peoples R China
[3] CAAC, Northwest Reg Air Traff Adm Bur, Xian, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Fog; Temperature and humidity profile; Wind profile; Variation characteristics; Xianyang airport; LARGE-EDDY SIMULATION; WIND PROFILER DATA; LONG-LASTING HAZE; RADIATION FOG; AIR-POLLUTION; LIQUID WATER; IMPACT; ABSORPTION; MODELS; EVENT;
D O I
10.1016/j.atmosres.2019.01.012
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
In order to improve the accuracy of fog forecast at Xianyang Airport, microwave radiometer, wind profiling radar and other equipment are utilized to detect fog. With the long term detection data from the equipment mentioned above, this paper analyzes the altitudinal-temporal variation characteristics of wind, temperature, and relative humidity during fog weather. It also tallies up the variation characteristics of the three meteorological factors at different heights as visibility and time changes. The findings are listed as follows. During autumn and winter, fog usually appears between 22:00 BT and 13:00 BT next day at Xianyang Airport, with relatively stable atmospheric structure. During fog, when the height is below 300 m, the average relative humidity is > 75%, and the average horizontal wind speed is < 3 m/s, along with a weak downdraft. In September and October, there is mainly southwest wind at low levels during fog weather, the height of radiation inversion layer within 300 m. In November, December and January, it is mainly northeast wind at low levels during fog, with rather thick radiation inversion layer. When the visibility is 1500 m in mist, if the temperature drops by 3 degrees C and the relative humidity rises by 9% in September and October, the visibility decreases to < 1000 m accordingly; if the temperature drops by 6 degrees C in November (or it drops 8 degrees C in December and January), the inversion layer thickens, the relative humidity increases by 15%, then the visibility reduces to < 1000 m. These findings can provide scientific basis to improve the accuracy of fog forecast at Xianyang Airport.
引用
收藏
页码:34 / 45
页数:12
相关论文
共 50 条
  • [1] Study on the Boundary Layer of the Haze at Xianyang Airport Based on Multi-Source Detection Data
    Ming, Hu
    Wang, Minzhong
    Gao, Lianhui
    Qian, Yijia
    Gao, Mingliang
    Chehri, Abdellah
    REMOTE SENSING, 2023, 15 (03)
  • [2] Three-Dimensional Lightning Characteristics Analysis over the Tibetan Plateau Based on Satellite-Based and Ground-Based Multi-Source Data
    Zhu, Jie
    Zhi, Shulin
    Zheng, Dong
    Yuan, Zhengguo
    ATMOSPHERE, 2024, 15 (07)
  • [3] On the analysis of ground-based microwave radiometer data during fog conditions
    Temimi, Marouane
    Fonseca, Ricardo Morais
    Nelli, Narendra Reddy
    Valappil, Vineeth Krishnan
    Weston, Michael John
    Thota, Mohana Satyanarayana
    Wehbe, Youssef
    Yousef, Latifa
    ATMOSPHERIC RESEARCH, 2020, 231 (231)
  • [4] REQUIREMENTS FOR AN AIRPORT, GROUND-BASED, WIND SHEAR DETECTION SYSTEM
    KALAFUS, RM
    HALLOCK, JN
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 1976, 57 (08) : 1074 - 1074
  • [5] Boundary layer characteristics of fog-haze in Tianjin based on multi-source data
    Meng, Li-Hong
    Liu, Hai-Ling
    Wang, Wei
    Cai, Zi-Ying
    Liu, Li-Li
    Qu, Ping
    Hao, Jian
    Zhongguo Huanjing Kexue/China Environmental Science, 2022, 42 (09): : 4018 - 4025
  • [6] The role of fog on the PM2.5 based on multi-source data and numerical model
    Meng, Li-Hong
    Yang, Er-Hui
    Cai, Zi-Ying
    Hao, Jian
    Yang, Jian-Bo
    Zhongguo Huanjing Kexue/China Environmental Science, 2023, 43 (04): : 1510 - 1518
  • [7] Multi-source data ingestion for IRI-2020 model: a combination of ground-based and space-borne observations
    Tianyang Hu
    Xiaohua Xu
    Jia Luo
    GPS Solutions, 2024, 28
  • [8] Multi-source data ingestion for IRI-2020 model: a combination of ground-based and space-borne observations
    Hu, Tianyang
    Xu, Xiaohua
    Luo, Jia
    GPS SOLUTIONS, 2024, 28 (02)
  • [9] Network threat detection based on correlation analysis of multi-platform multi-source alert data
    Xindai Lu
    Jiajia Han
    Qianbo Ren
    Hua Dai
    Jiyuan Li
    Jing Ou
    Multimedia Tools and Applications, 2020, 79 : 33349 - 33363
  • [10] Network threat detection based on correlation analysis of multi-platform multi-source alert data
    Lu, Xindai
    Han, Jiajia
    Ren, Qianbo
    Dai, Hua
    Li, Jiyuan
    Ou, Jing
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (45-46) : 33349 - 33363