Author Affiliations
1School of Mechanical and Precision Instrument Engineering, Xi′an University of Technology, Xi′an, Shaanxi 710048, China2State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China3State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Chinashow less
Fig. 1. Flow chart of data splicing
Fig. 2. Visibility and AQI before and after the typical fog-haze case in Xi'an
Fig. 3. Qualitative analysis of the temperature profile during model data, lidar data and radiosonde data. (a) <2 km; (b) 2~20 km
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
Fig. 5. Relative error corresponding to 11 groups of step sizes
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
Fig. 7. Parameters for selecting the best splicing-region. (a) Correlation coefficient; (b) splicing-region deviation per km; (c) fit-region deviation per km
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
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
Fig. 10. Splicing-region deviation per km corresponding to 8 groups of splicing-regions to be selected
Fig. 11. Contrast of splicing temperature at 20:00 UTC during the whole fog-haze phase. (a) Unsplicing temperature profile. (b) splicing temperature profile
Parameter | D01 | D02 |
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Input data | NCEP | Central grid | 109°E, 34.25°N | Nested region | Longitude | 84--134°E | 106--112°E | Latitude | 24.25--44.25°N | 31.25--37.25°N | Horizontal resolution | 9 km | 3 km | Vertical resolution | 59 levels | Time resolution | 30 min | Microphysics parameterization | Goddard GCE | Cumulus convection parameterization | Kain-Fritsch | None | Integration time (UTC) | 2013-12-15T00:00:00 to 2013-12-31T12:00:00 |
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Table 1. Parameters for WRF model simulation
Evaluation parameter | Model-lidar | Radiosonde-lidar |
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The best splicing region /km | 0.89--1.41 | 0.78--1.22 | The best splicing value /km | 0.52 | 0.44 | Effective height /level | ≥4 | 1 | Correlation coefficient | 0.92 | 0.88 | Fit-region deviation per km /m-1 | 0.72 | 6.09 |
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Table 2. Comprehensive evaluation parameters between model-lidar splicing and radiosonde-lidar splicing