• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 7, 2110 (2021)
Bo LI1、1; 2;, Ya-zhou PU1、1;, Nan WANG3、3;, Yu-feng WANG1、1;, Hui-ge DI1、1;, Yue-hui SONG1、1;, and Deng-xin HUA1、1; *;
Author Affiliations
  • 11. School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
  • 33. The Meteorological Observatory of Shaanxi Province, Xi'an 710014, China
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    DOI: 10.3964/j.issn.1000-0593(2021)07-2110-06 Cite this Article
    Bo LI, Ya-zhou PU, Nan WANG, Yu-feng WANG, Hui-ge DI, Yue-hui SONG, Deng-xin HUA. A Method for Assimilating the Raman Lidar Detecting Temperature in WRF on Simulating the Short-Time Heavy Rainfall[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2110 Copy Citation Text show less
    (a) The microwave radar echo (shaded, unit: dBz) at 1200 UTC 20 July 2015, the black cross represents the location of the microwave radar, the dashed pane is the convective cloud influenced Xi'an, and the black dot is the location of lidar; (b) The atmospheric temperature profile (densely gray pane) detected by lidar, and the FNL temperature profile (black circle) in Xi'an at 1200 UTC 20 July 2015, unit: K
    Fig. 1. (a) The microwave radar echo (shaded, unit: dBz) at 1200 UTC 20 July 2015, the black cross represents the location of the microwave radar, the dashed pane is the convective cloud influenced Xi'an, and the black dot is the location of lidar; (b) The atmospheric temperature profile (densely gray pane) detected by lidar, and the FNL temperature profile (black circle) in Xi'an at 1200 UTC 20 July 2015, unit: K
    The detecting points, referencing points, and interpolating points in the MQ method
    Fig. 2. The detecting points, referencing points, and interpolating points in the MQ method
    Flow chart for the coupling of MQ and WRF
    Fig. 3. Flow chart for the coupling of MQ and WRF
    The relationship between the RMSE and c(a): 1 530 m; (b): 3 710 m
    Fig. 4. The relationship between the RMSE and c
    (a): 1 530 m; (b): 3 710 m
    The temperature distribution on the 49 groups of interpolated grids at 1 530 m (a) and 3 710 m (b)The black dots represent irregular observations, unit: ℃
    Fig. 5. The temperature distribution on the 49 groups of interpolated grids at 1 530 m (a) and 3 710 m (b)
    The black dots represent irregular observations, unit: ℃
    Qualitative comparison of rainfall during 1200 UTC—1800 UTC 20 July 2015, unit: mm(a): Observation precipitation; (b): CTRL Simulation precipitation before assimilating; (c): Simulation precipitation after assimilating the ground and sounding data; (d): Simulation precipitation after assimilating the lidar data
    Fig. 6. Qualitative comparison of rainfall during 1200 UTC—1800 UTC 20 July 2015, unit: mm
    (a): Observation precipitation; (b): CTRL Simulation precipitation before assimilating; (c): Simulation precipitation after assimilating the ground and sounding data; (d): Simulation precipitation after assimilating the lidar data
    6 h雨强
    /mm
    评分
    指标
    一次同
    化前
    一次
    同化后
    二次
    同化前
    一次
    同化
    效果
    二次
    同化
    二次
    同化
    效果
    TS0.590.660.070.710.05
    0.1~5空报率0.130.170.040.09-0.08
    漏报率0.280.19-0.090.190
    Table 1. Quantitative analysis of precipitation before and after data assimilating
    Bo LI, Ya-zhou PU, Nan WANG, Yu-feng WANG, Hui-ge DI, Yue-hui SONG, Deng-xin HUA. A Method for Assimilating the Raman Lidar Detecting Temperature in WRF on Simulating the Short-Time Heavy Rainfall[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2110
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