Analysis of Precipitation Estimation Effect of S-Band Dual-Polarization Radar in Shantou
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Abstract:
As the main equipment for atmospheric detection, weather radar plays an important role in meteorological operations, weather early warning, and atmospheric science research. With the upgrading of the S-band dual-polarisation radar, it is very important to improve the precision of radar quantitative precipitation estimation. In this paper, three southwest monsoon and three typhoon precipitation processes over the Shantou region from 2018 to 2022 are selected as the research objects. Hourly rainfall data from the S-band dual-linear polarisation weather radar and 43 automatic weather gauges in Shantou are used to analyse the effectiveness of radar precipitation estimation. To improve the reliability and accuracy of data, the data quality control of radar and rain gauge data is carried out by various means. The data of 92,880 polarisation samples are obtained by matching the station coordinates of the rain gauge with the multi-polarisation parameters of the radar from the time dimension and the space dimension. By means of five error indexes: mean error (ME), relative error (RE), root mean square error (RMSE), standard deviation (STD) and correlation coefficient (ρ), the total samples, sub-rainfall intensity samples and sub-precipitation type samples of PPS, Ryzhkov, CSU-HIDRO and HCA-LIQ radar precipitation estimation algorithms in six precipitation processes are evaluated respectively. By comparing and analysing the error indexes and the stability of the algorithm between the estimated value of radar precipitation and the measured value of rain gauge, the following conclusions are drawn: (1) in the total sample, CSU-HIDRO algorithm and HCA-LIQ algorithm outperform PPS algorithm and Ryzhkov algorithm, with CSU-HIDRO algorithm being the best; (2) CSU-HIDRO algorithm and HCA-LIQ algorithm perform better than PPS algorithm and Ryzhkov algorithm in the samples of southwest monsoon precipitation and typhoon precipitation, with CSU-HIDRO algorithm performing best in the samples of southwest monsoon precipitation, and HCA-LIQ algorithm performing best in typhoon precipitation samples; (3) Ryzhkov algorithm performs best in light rain samples, CSU-HIDRO algorithm outperforms other algorithms in medium rain samples and heavy rain samples, with HCA-LIQ algorithm being next. In a word, both CSU-HIDRO algorithm and HCA-LIQ algorithm adopt the data of raindrop spectrum in South China to improve the fitting of formula parameters, and the effect of quantitative precipitation estimation is better than the other two algorithms in the United States. In practical application, the rainfall intensity and precipitation type can be considered synthetically, and the corresponding precipitation estimation algorithm can be adopted to improve the accuracy of radar quantitative precipitation estimation. At the same time, it also provides reference for the secondary development of radar products and short-term forecast and early warning.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.