• Acta Optica Sinica
  • Vol. 40, Issue 9, 0928003 (2020)
Bo Li1、2, Hongxia Wei1, Liang Zhao3, Yufeng Wang1, and Dengxin Hua1、*
Author Affiliations
  • 1School of Mechanical and Precision Instrument Engineering, Xi′an University of Technology, Xi′an, Shaanxi 710048, China
  • 2State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 3State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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    DOI: 10.3788/AOS202040.0928003 Cite this Article Set citation alerts
    Bo Li, Hongxia Wei, Liang Zhao, Yufeng Wang, Dengxin Hua. Data Splicing Method for LiDAR Detection Temperature Under Fog-Haze Condition[J]. Acta Optica Sinica, 2020, 40(9): 0928003 Copy Citation Text show less
    Flow chart of data splicing
    Fig. 1. Flow chart of data splicing
    Visibility and AQI before and after the typical fog-haze case in Xi'an
    Fig. 2. Visibility and AQI before and after the typical fog-haze case in Xi'an
    Qualitative analysis of the temperature profile during model data, lidar data and radiosonde data. (a) <2 km; (b) 2~20 km
    Fig. 3. Qualitative analysis of the temperature profile during model data, lidar data and radiosonde data. (a) <2 km; (b) 2~20 km
    Quantitative analysis of the splicing data and radiosonde data on relative error. (a) Between lidar data and radiosonde data; (b) between model data and radiosonde data
    Fig. 4. Quantitative analysis of the splicing data and radiosonde data on relative error. (a) Between lidar data and radiosonde data; (b) between model data and radiosonde data
    Relative error corresponding to 11 groups of step sizes
    Fig. 5. Relative error corresponding to 11 groups of step sizes
    Judgment criteria for based on 20 groups of splicing-regions in 4 groups of samples. (a) Correlation coefficient; (b) splicing-region deviation per km
    Fig. 6. Judgment criteria for based on 20 groups of splicing-regions in 4 groups of samples. (a) Correlation coefficient; (b) splicing-region deviation per km
    Parameters for selecting the best splicing-region. (a) Correlation coefficient; (b) splicing-region deviation per km; (c) fit-region deviation per km
    Fig. 7. Parameters for selecting the best splicing-region. (a) Correlation coefficient; (b) splicing-region deviation per km; (c) fit-region deviation per km
    Temperature splicing results between model and lidar data. (a) Qualitative contrast on profiles between splicing temperature and standard temperature during the whole layers; (b) quantitative contrast on relative error between splicing temperature and standard temperature during the whole layers; (c) qualitative contrast on profiles between splicing temperature and standard temperature in the best splicing region
    Fig. 8. Temperature splicing results between model and lidar data. (a) Qualitative contrast on profiles between splicing temperature and standard temperature during the whole layers; (b) quantitative contrast on relative error between splicing temperature and standard temperature during the whole layers; (c) qualitative contrast on profiles between splicing temperature and standard temperature in the best splicing region
    Temperature splicing results between radiosonde and lidar data, and their comparison with the splicing results between model and lidar data. (a) Profile of splicing temperature between radiosonde and lidar data during the whole layers; (b) qualitative contrast on profiles between radiosonde-lidar splicing temperature and standard temperature in the best splicing region; (c) quantitative contrast on relative errors between the model-lidar splicing temperature and standard temperature, and between
    Fig. 9. Temperature splicing results between radiosonde and lidar data, and their comparison with the splicing results between model and lidar data. (a) Profile of splicing temperature between radiosonde and lidar data during the whole layers; (b) qualitative contrast on profiles between radiosonde-lidar splicing temperature and standard temperature in the best splicing region; (c) quantitative contrast on relative errors between the model-lidar splicing temperature and standard temperature, and between
    Splicing-region deviation per km corresponding to 8 groups of splicing-regions to be selected
    Fig. 10. Splicing-region deviation per km corresponding to 8 groups of splicing-regions to be selected
    Contrast of splicing temperature at 20:00 UTC during the whole fog-haze phase. (a) Unsplicing temperature profile. (b) splicing temperature profile
    Fig. 11. Contrast of splicing temperature at 20:00 UTC during the whole fog-haze phase. (a) Unsplicing temperature profile. (b) splicing temperature profile
    ParameterD01D02
    Input dataNCEP
    Central grid109°E, 34.25°NNested region
    Longitude84--134°E106--112°E
    Latitude24.25--44.25°N31.25--37.25°N
    Horizontal resolution9 km3 km
    Vertical resolution59 levels
    Time resolution30 min
    Microphysics parameterizationGoddard GCE
    Cumulus convection parameterizationKain-FritschNone
    Integration time (UTC)2013-12-15T00:00:00 to 2013-12-31T12:00:00
    Table 1. Parameters for WRF model simulation
    Evaluation parameterModel-lidarRadiosonde-lidar
    The best splicing region /km0.89--1.410.78--1.22
    The best splicing value /km0.520.44
    Effective height /level≥41
    Correlation coefficient0.920.88
    Fit-region deviation per km /m-10.726.09
    Table 2. Comprehensive evaluation parameters between model-lidar splicing and radiosonde-lidar splicing
    Bo Li, Hongxia Wei, Liang Zhao, Yufeng Wang, Dengxin Hua. Data Splicing Method for LiDAR Detection Temperature Under Fog-Haze Condition[J]. Acta Optica Sinica, 2020, 40(9): 0928003
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