OTT Parsivel2激光雨滴谱仪在黔江CD雷达降水估测中的应用

Application of OTT Parsevel2 Laser Raindrop Spectrometer in Precipitation Estimation of Qianjiang CD Radar

  • 摘要: 依据OTT Parsivel2激光雨滴谱仪、天气雷达、地面雨量计等资料对2019年6月28日黔江区的一次短时强降水天气过程主要降雨时段进行分析,根据反射率因子(Z)与降雨强度(I)的常用表达Z=Aube,利用最小二乘法拟合雨滴谱Z-I关系Z=388I1.3,利用最优化法计算出雷达Z-I关系Z=50I1.1,再用WSR-88D中默认的雷达Z-I关系Z=300I1.4,估测黔江本次过程主要降雨时段的雨量,并与实际雨量进行对比分析。结果表明:雷达回波修正后通过雨滴谱Z-I关系估测的降水效果最好,与实际情况较为接近;证明通过雨滴谱资料修正雷达回波,建立黔江本地化的各种降雨类型Z-I关系的方法是可行的,对今后定量估测降雨强度和区域降雨量,做好精细化预报,有针对性地开展气象服务提供了参考方法。

     

    Abstract: Based on the data of OTT Parsevel2 laser raindrop spectrometer, weather radar and surface rain gauge, this paper analyzes the main rainfall periods of a short-term heavy rainfall weather process in Qianjiang district on June 28, 2019.According to the common expression Z=AIb of reflectivity factor (Z) and precipitation intensity (I), the least square method is used to fit the relationship Z-I of raindrop spectrum Z=388I1.3, and the optimization method is used to calculate the radar Z-I The relationship Z=50I1.1, and then the default radar Z-I relationship Z=300I1.4 in WSR-88D are used to estimate the rainfall in the main precipitation period of Qianjiang, and the rainfall is compared with the actual rainfall. The results show that after the radar echo correction, the precipitation estimated by Z-I relation of raindrop spectrum is the best, which is close to the actual situation.It is proved that it is feasible to modify the radar echo by raindrop spectrum data to establish the local Z-I relation of all kinds of rainfall types in Qianjiang. The method can be used to estimate the precipitation intensity and regional precipitation in the future, make fine forecast, and carry out meteorology pertinently the service provides a good reference method, which is close to the actual situation; it proves that it is feasible to modify the radar echo through the raindrop spectrum data to establish the Z-I relationship of various rainfall types localized in Qianjiang, which provides a reference method for quantitative estimation of precipitation intensity and regional precipitation in the future, fine prediction and targeted meteorological services.

     

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