Yan Shen, Shi Xiaomeng, Fu Gang, et al. Wind lidar applicability in low visibility weather in Qingdao. J Appl Meteor Sci, 2024, 35(1): 33-44. DOI:  10.11898/1001-7313.20240103.
Citation: Yan Shen, Shi Xiaomeng, Fu Gang, et al. Wind lidar applicability in low visibility weather in Qingdao. J Appl Meteor Sci, 2024, 35(1): 33-44. DOI:  10.11898/1001-7313.20240103.

Wind Lidar Applicability in Low Visibility Weather in Qingdao

DOI: 10.11898/1001-7313.20240103
  • Received Date: 2023-11-15
  • Rev Recd Date: 2023-12-19
  • Publish Date: 2024-01-31
  • Utilizing data of Doppler wind lidar and L-band radiosonde system installed at Qingdao National Basic Meteorological Observing Station from April 2021 to December 2022, their detection capability and accuracy under low visibility weather conditions are compared and evaluated, specifically in terms of detection height, horizontal wind speed and wind direction, using data obtained from L-band radiosonde system as the reference standard. During non-precipitation weather, when visibility exceeds 10000 m, the wind lidar demonstrates a stable average maximum detection height of approximately 1200 m. The root mean square errors of the wind speed and direction fluctuate around 1.2 m·s-1 and 25°, respectively. However, when the visibility drops below 10000 m, the detection height and accuracy of the wind lidar are influenced by the level of interference, particularly in different visibility and relative humidity ranges. In situations where visibility is below 1000 m, the decrease in atmospheric visibility is attributed to increased water vapor content in the air, with relative humidity consistently exceeding 95%. This high humidity significantly interferes with laser transmission in the atmosphere, resulting in an average maximum detection height of less than 400 m. The correlation between horizontal wind speed and direction decreases to 0.91 and 0.94, and the root mean square error increases to 1.4 m·s-1and 42.7°, respectively. When the visibility ranges between 1000 m and 10000 m, the wind lidar's detection capability varies with the water vapor content in the atmosphere. In cases where relative humidity is below 90%, meeting the criteria for hazy days, the decrease in atmospheric visibility is primarily due to increased aerosol particle content. Under these conditions, the average maximum detection height remains stable above 1200 m. The correlation coefficients for horizontal wind speed and direction are as high as 0.97 and 0.98, with root mean square errors of about 1.1 m·s-1 and 22.3°, respectively. These results are comparable to the detection capability demonstrated under high visibility weather conditions. As relative humidity increases, the impact of water vapor attenuation on laser transmission starts to affect the detection height and accuracy of the wind lidar to varying degrees. When the relative humidity exceeds 95%, the average maximum detection height influenced by water vapor decreases to below 400 m. The correlation coefficient of wind speed decreases to 0.94, with a corresponding increase in the root mean square error to 1.5 m·s-1, and the accuracy of wind direction remains relatively stable under these conditions.
  • Fig. 1  Probability of maximum detection height distribution(the shaded) and average maximum detection height(the black line)of wind lidar under visibility conditions of 0-30000 m(a) and 0-2000 m(b)

    Fig. 2  Root mean square errors of horizontal wind speed and wind direction of wind lidar under different visibilities

    Fig. 3  Probability of maximum detection height distribution(the shaded) and average maximum detection height(the black line) of wind lidar varing with relative humidity under low-visibility conditions

    Fig. 4  Root mean square errors of horizontal wind speed and wind direction of wind lidar varing with relative humidity under low-visibility conditions

    Fig. 5  Scatter plots and linear fitting of horizontal wind speed under different conditions

    Fig. 6  Scatter plots and linear fitting of horizontal wind direction under different conditions

    Fig. 7  Probability density distribution of horizontal wind speed and wind direction error of wind lidar under different conditions

    Fig. 8  Ratio of wind speed at each level under the fog and haze conditions

    Fig. 9  Ratio of wind direction at each level under the fog and haze conditions

  • [1]
    Chen W C, Song L L, Wang Z C, et al. The wind measuring performance of WINDCUBE V2 pulse laser wind profiler under different weather conditions. J Appl Meteor Sci, 2017, 28(3): 327-339. doi:  10.11898/1001-7313.20170307
    [2]
    Zhang R Y, Zhang X Z, Yang X S, et al. Wind characteristics study in surface layer of Typhoon Morakot(0908). J Appl Meteor Sci, 2012, 23(2): 184-194. http://qikan.camscma.cn/article/id/20120207
    [3]
    Chen S P, Duan Y H, Li Q Q. Fitting of wind shear index in the boundary layer of landfalling typhoons based on high tower observation. J Appl Meteor Sci, 2022, 33(2): 155-166. doi:  10.11898/1001-7313.20220203
    [4]
    Lin X M, Wei Y H, Zhang N, et al. Construction of air-sounding-profile system based on foundation-remote-sensing equipment. J Appl Meteor Sci, 2022, 33(5): 568-580. doi:  10.11898/1001-7313.20220505
    [5]
    Li M H, Fan S J, Wang B M, et al. Observation study on the temperature and wind profiles over the Pearl River Delta in autumn. J Appl Meteor Sci, 2008, 19(1): 53-60. http://qikan.camscma.cn/article/id/20080110
    [6]
    Fan Q. Lidar for Low-Level Wind Shear Identification and It's Application in Aviation Meteorology. Chengdu: Chengdu University of Information Technology, 2017.
    [7]
    Wu S H, Liu B Y, Liu J T, et al. Wind turbine wake visualization and characteristics analysis by Doppler lidar. Optics Express, Optical Society of America, 2016, 24(10): A762-A780.
    [8]
    Liu F X, Tang Z Y, Liu J X, et al. Evolvement characteristics of heavy pollution process in Xi'an based on wind lidar observation. J Catastrophology, 2021, 36(4): 88-95. doi:  10.3969/j.issn.1000-811X.2021.04.015
    [9]
    Dai B B, He M, Yang J X, et al. Causal analysis of a clear sky wind shear event at a plateau airport in southwest China using lidar data. Meteor Sci Technol, 2021, 49(4): 589-596.
    [10]
    Liu J X, Yun L, Shao S Y, et al. Observation of turbulence using Doppler wind lidar in Shenzhen. J Atmos Environ Opt, 2021, 16(5): 383-391.
    [11]
    Xia J R, Wang P C, Min M. Observation and validation of wind parameters measured by Doppler wind lidar Windcube. Clim Environ Res, 2011, 16(6): 733-741.
    [12]
    Hu Q, Rodrigo P J, Pedersen C. Remote wind sensing with a CW diode laser lidar beyond the coherence regime. Opt Lett, 2014, 39(16): 4875-4878. doi:  10.1364/OL.39.004875
    [13]
    Choukulkar A, Brewer W A, Sandberg S P, et al. Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign. Atmos Meas Tech, 2017, 10(1): 247-264. doi:  10.5194/amt-10-247-2017
    [14]
    Li L, Zhang Z G, Du C Y, et al. Inter-comparison of wind measurements between Doppler wind lidar and L-band radiosonde. J Atmos Environ Opt, 2022, 17(5): 494-505.
    [15]
    Fan Q, Zhu K Y, Zheng J F, et al. Detection performance analysis of all-fiber coherent wind lidar under different weather types. Chinese J Lasers, 2017, 44(2): 326-335.
    [16]
    Chen Q, Shi W H, Tang J, et al. Research on influence of rainfall intensity on accuracy of wind observation based on Doppler lidar. Meteor Sci Technol, 2022, 50(3): 324-333.
    [17]
    Zhang Z J, Zhang J, Wu G S, et al. Evaluation of wind lidar data inmegacities experiment on integrated meteorological observation. J Trop Meteor, 2022, 38(2): 253-264.
    [18]
    Shi W H, Tang J, Chen Y H, et al. Study on the accuracy of Doppler wind lidar in measuring the boundary layer wind field of Typhoon Lekima. J Trop Meteor, 2020, 36(5): 577-589.
    [19]
    Zhao W K, Zhao S J, Shan Y L, et al. Evaluation of wind detection performance based on wind lidar. China Meas Test, 2022, 48(1): 147-153.
    [20]
    Liu D W, Mu H Z, He Q S, et al. A low visibility recognition algorithm based on surveillance video. J Appl Meteor Sci, 2022, 33(4): 501-512. doi:  10.11898/1001-7313.20220410
    [21]
    Wang Y F, Qi Y B, Li Q, et al. Macro and micro characteristics of a fog process in Changbai Mountain in summer. J Appl Meteor Sci, 2022, 33(4): 442-453. doi:  10.11898/1001-7313.20220405
    [22]
    Pan W, Zuo Z Y, Xiao D, et al. Interdecadal variation of haze days over China with atmospheric causes in recent 50 years. J Appl Meteor Sci, 2017, 28(3): 257-269. doi:  10.11898/1001-7313.20170301
    [23]
    Lu X, Gao S H, Rao L J, et al. Sensitivity study of WRF parameterization schemes for the spring sea fog in the Yellow Sea. J Appl Meteor Sci, 2014, 25(3): 312-320. http://qikan.camscma.cn/article/id/20140307
    [24]
    Zhou X S, Guo Q Y, Xia Y C, et al. Inspection of FY-3D satellite temperature data based on horizontal drift round-trip sounding data. J Appl Meteor Sci, 2023, 34(1): 52-64. doi:  10.11898/1001-7313.20230105
    [25]
    Luo X G, Liang G F, Yang C. Result analysis of L-band radar wind mesurement system in different methods. Meteor Sci Technol, 2015, 43(6): 1025-1029.
    [26]
    Lei Y, Guo Q Y, Qian Y, et al. Evaluation and quality mark of radiosonde geopotential height of L-band radar. J Appl Meteor Sci, 2018, 29(6): 710-723. doi:  10.11898/1001-7313.20180607
    [27]
    Li L W, Chen Y J, Gong Y H. Applicability of wind field products retrieved from wind lidar in the Winter Games. Mod Electron Technol, 2022, 45(13): 93-98.
    [28]
    Wang D C, Qiu C, Dong X G, et al. Comparing strong wind data observed by boundary layer wind profiling radar and L-band radar in Jinan. Meteor Mon, 2019, 45(8): 1169-1180.
    [29]
    Wu D. A discuss on the difference between haze and fog and the warning of brownish haze weather. Guangdong Meteor, 2004, 26(4): 1-4.
    [30]
    Huang J, Wu D, Huang M H, et al. Visibility variations in the Pearl River Delta of China during the period of 1954-2004. J Appl Meteor Sci, 2008, 19(1): 61-70. http://qikan.camscma.cn/article/id/20080111
    [31]
    Lü W L, Shi X M, Zhang K. Visibility characteristics and influencing factors of a fog-haze process in Qingdao. J Meteor Environ, 2023, 39(3): 47-55.
    [32]
    Wang Y M, Gao G Q. Study of attenuation characteristics of laser propagation in fog. Infrared, 2013, 34(12): 14-19.
    [33]
    Han X, Zhou C. Classification and features of atmospheric lidars: A review. J Nanjing Univ(Nat Sci), 2023, 59(5): 900-913.
    [34]
    Wu D. More discussions on the differences between haze and fog in city. Meteor Mon, 2006, 32(4): 9-15.
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    • Received : 2023-11-15
    • Accepted : 2023-12-19
    • Published : 2024-01-31

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