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A Method for Assimilating the Raman Lidar Detecting Temperature in WRF on Simulating the Short-Time Heavy Rainfall |
LI Bo1, 2, PU Ya-zhou1, WANG Nan3, WANG Yu-feng1, DI Hui-ge1, SONG Yue-hui1, HUA Deng-xin1* |
1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
2. Institute of Tropical and Marine Meteorology/Guangdong Provinical Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510080, China
3. The Meteorological Observatory of Shaanxi Province, Xi’an 710014, China |
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Abstract In order to take full advantage of Raman lidar, a high-resolution detecting experiment was carried out at the periphery of the convective cloud, and a temperature with a vertical resolution of 4 m was inversed by using the synthetically multilevel quality analysis and control technique. Three groups of experiments were conducted, and a novel method for assimilating the lidar detecting temperature was especially proposed based on the coupling between the Multiquadric method and WRFDA (the Weather Research and Forecasting model Data Assimilation system). Firstly, a controlled experiment (CTRL) was carried out, and the suitable model parameters were obtained after debugging model. Secondly, based on the WRFDA module, the conventional observational data, including ground station and radiosonde station were integrated into the initial field. The results showed that TS increased by 0.07, and the dummy precipitation in southwest Shaanxi was successfully avoided. Thirdly, the Multiquadric method was used to assimilate Lidar data. TS increased by 0.12, and the Miss hit forecast rate was reduced by 0.09. When the lidar data was integrated into the WRF model, the positive simulation effects could be obtained according to quantitative and qualitative analysis. It was also discovered that the variation of simulation precipitation was because of the change of elements, including wind, water vapor, and temperature. Lidar could be well used to detect short-time heavy rainfall, according to this study.
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Received: 2019-04-02
Accepted: 2019-10-14
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Corresponding Authors:
HUA Deng-xin
E-mail: dengxinhua@xaut.edu.cn
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