业务DSG5型降水现象仪与Parsivel降水滴谱仪测量参数对比分析

Comparison of precipitation measurement by DSG5 and Parsivel present weather instrument

业务降水现象仪作为一种降水滴谱测量设备,可以用于天气现象判断,其包含的降水滴谱信息更是反映了诸多降水微物理特征,而不同设备的观测特性和精度存在一定的差异,了解不同设备的差异,对其资料的后续应用有重要的意义。本文利用2018年湖北省咸宁市黑山观测站8次液态降水过程中地面降水观测、DSG5型业务降水现象仪与同址的Parsivel型降水滴谱仪测量的降水粒子谱信息及其反演的物理参量,共1 503对样本开展对比分析,研究结果表明:(1)相比于地面自动站降水数据,Parsivel与地面观测一致性较高,DSG5则有一定的偏小。(2)相对于DSG5,Parsivel观测的粒子数浓度Nw相对较多、质量加权平均直径Dm则相对较小。(3)对于分钟雨强RR≥20 mm·h^(-1)的降水,Parsivel粒子直径小于(大于) 3 mm数浓度高于(低于)DSG5的观测。(4)对于直径大于4 mm的粒子,Parsivel的观测的下落速度略低于理论值,DSG5则明显大于理论值。另外,2次雨雪转换过程中降水现象观测对比研究表明DSG5对降水现象有着良好的观测,相比于Parsivel,其在降水相态转换过程中的天气现象与人工观测结果一致性更好。

As a new type of raindrop distrometer, the operational DSG5 present weather sensor has the ability to identify not only the weather phenomena but also the raindrop size distribution, which is important to the rain microphysical and dynamical analysis. It’s very important to understand the raindrop distribution observation performance with different instruments type, which is useful for the further research. In this paper, the microphysical parameters inverted by the DSG5 and Parsivel are compared with a total of 1503 samples for 8 liquid rian events during 2018 in Heishan station, Xianning, Hubei Province. The results show that:(1) Compared with the AWS(Automatic Weather Station)precipitation, the Parsivel data has a higher consistency with the AWS, and DSG5 is somewhat smaller than them.(2) Compared with the DSG5, Nw(the normalized intercept) observed by Parsivel is larger and the Dm(mass-weighted mean diameter) is smaller.(3) For RR≥20 mm·h^(-1), the number concentration which the diameter smaller(larger) than 3 mm observed by Parsivel is higher(lower) than that of the DSG5.(4) For raindrops diameter larger than 4 mm, the falling velocity observed by Parsivel is slightly lower than the theoretical value, and the falling velocity observed by DSG5 is significantly larger. The DSG5 has good observation performance for the weather phenomena. Compared with Parsivel, the weather phenomena observed by DSG5 during two snowfall events are more consistent with the manually observation.