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Application of QVP Method to Winter Precipitation Observation Based on Polarimetric Radar
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摘要: 双偏振雷达是研究降水微物理过程的重要探测设备，为研究我国东部沿海地区冬季降水的微物理特性，选取2019—2020年不同天气背景下（包含相态转换）影响上海的冬季降水过程，基于上海南汇WSR-88D双偏振雷达资料，采用准垂直廓线（QVP）方法反演3次降水过程的反射率因子ZH、差分反射率ZDR和相关系数ρhv的高度-时间廓线。基于QVP，结合探空、再分析资料、地面自动气象站和雨滴谱数据，分析过程时段内融化层高度变化、云中粒子微物理特征，同时借助云雷达观测比对QVP方法的实际效果。结果表明：QVP方法反演的廓线可以体现贝吉龙过程的发生以及融化层高度的变化，并能够有效区分过程时段内的凇附和聚合过程。与此同时，对于非连续性或非均匀性质的降水系统，有针对性地选择方位利用QVP方法进行处理可准确获取重点关注区域降水云系中的微物理过程变化。综上所述，QVP方法反演的高度-时间廓线能够用于降水相态的快速诊断及降水粒子从空中到地面演变的微物理过程分析，为冬季降水相态短时临近预报提供依据。Abstract: Winter precipitation events, especially those involving transitions of precipitation type, continue to pose a formidable forecasting and nowcasting challenge to operational meteorologists. The polarimetric radars provide unique insight into microphysical processes in clouds and precipitation. Using polarimetric radars in conjunction with thermodynamic information is a promising way for better winter precipitation detection. To explore the microphysical characteristic and the internal structure of winter precipitation over eastern coast of China, the data collected by the WSR-88D polarimetric radar at Nanhui, Shanghai are exploited by the quasi-vertical profile (QVP) method. The QVP method involves azimuthal averaging of radar reflectivity factor at horizontal polarization(ZH), differential reflectivity(ZDR) and the copular correlation coefficient (ρhv) at high antenna elevation, presenting QVPs in a height-time format. QVP generation is an efficient way to examine the temporal evolution of microphysical processes governing precipitation production and to display physical links between polarimetric signatures aloft in the ice-phase or mixed-phase parts of the clouds. In 3 different synoptic system governing snow cases affecting Shanghai, the QVPs retrieved from dual-polarization radars at elevations of 19.5ånd 9.9åre demonstrated to successfully monitor the evolution of melting layer and Bergeron process. They also provide opportunities to discriminate between the processes of snow aggregation and riming with the joint analysis of reanalysis data and observations of radio sounding, auto weather station, disdrometer and wind profiler radar. The vertical observation by cloud radar data is used to compare with QVP retrieved profile. Additionally, for discontinuous or multi-scale synoptic precipitation, a selected azimuthal averaging QVP technique is introduced to separate QVPs into before and after the synoptic system for detailed comparisons and monitoring microphysical processes leading to precipitation formation. The method is demonstrated to monitor important microphysical signatures as well as following precipitation development. In conclusion, the procedure for generating quasi-vertical profiles of polarimetric radar variables is very simple and straightforward, and the QVP plots in the height-time format can be produced in real time for operational polarimetric weather radars as a standard product, which is very easy to implement and very promising to use along with traditional weather radar products of PPIs and reconstructed RHIs. The QVP methodology is particularly effective because of its local coverage and high precision as well as its potential for nowcasting.
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图 3 2019年2月8日QVP方法反演的19.5°仰角南汇雷达参量高度-时间分布及7日20:00宝山探空
Fig. 3 Height-time representation retrieved by QVP based on Nanhui radar data at 19.5° elevation on 8 Feb 2019 and Baoshan radio sounding record at 2000 BT 8 Feb 2019
图 4 2019年2月8日QVP方法反演的9.9°仰角南汇雷达参量高度-时间分布和南汇自动气象站实况
Fig. 4 Height-time representation retrieved by QVP based on Nanhui radar data at 9.9° elevation and Nanhui automatic weather station record on 8 Feb 2019
图 5 2019年2月8—9日QVP方法反演的19.5°仰角南汇雷达参量高度-时间分布和8日20:00宝山探空
Fig. 5 Height-time representation retrieved by QVP based on Nanhui radar data at 19.5° elevation during 8-9 Feb 2019 and Baoshan radio sounding record at 2000 BT 8 Feb 2019
图 6 2019年2月8—9日QVP方法反演的9.9°仰角南汇雷达参量高度-时间分布和南汇自动气象站实况
Fig. 6 Height-time representation retrieved by QVP based on Nanhui radar at 9.9° elevation and Nanhui automatic weather station during 8-9 Feb 2019
图 7 2020年2月15日20:00宝山站探空和16日02:00距离南汇雷达站最近格点的NCEP再分析资料
Fig. 7 Baoshan radio sounding record at 2000 BT 15 Feb 2020 and nearest grid in NCEP reanalysis data at 0200 BT 16 Feb 2020
图 8 2020年2月15日16:00—16日04:00世博站毫米波云雷达和QVP方法反演的南汇雷达9.9°仰角ZH廓线
Fig. 8 Height-time vertical profile of reflectivity from Shibo cloud radar and QVP retrieval from Nanhui radar at 9.9°elevation from 1600 BT 15 Feb to 0400 BT 16 Feb in 2020
图 9 2020年2月15日17:00—16日01:30 QVP方法反演的系统经过前后9.9°仰角南汇雷达参量高度-时间分布
Fig. 9 Height-time representations retrieved by QVP based on Nanhui radar data at 9.9° elevation before and after system movement from 1700 BT 15 Feb to 0130 BT 16 Feb in 2020
表 1 2019年2月8日关键层降水粒子偏振量和相态时间变化表
Table 1 Polarimetric parameters and hydrometeor classification at key levels on 8 Feb 2019
仰角 高度 03:00 05:30 ZH/dBZ ZDR/dB ρhv 相态 ZH/dBZ ZDR/dB ρhv 相态 19.5° 2.5 km 26.5 0.5 0.96 球形小冰晶(凇附) 7.6 0.06 0.92 冰水混合(聚合状冰晶和小水滴) 5.5 km 5.8 0.97 0.94 冰晶 1.4 1.25 0.94 冰晶(枝状) 9.9° 2.5 km 31.7 0.48 0.93 球形小冰晶(凇附) 12.1 -0.13 0.93 冰水混合(聚合状冰晶和小水滴) 5.1 km 10.9 0.56 0.98 冰晶 6.2 0.95 0.97 冰晶(枝状) 
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表 2 2019年2月9日关键层降水粒子偏振量和相态时间变化表
Table 2 Polarimetric parameters and hydrometeor classification at key levels on 9 Feb 2019
仰角 高度 22:30 00:00 ZH/dBZ ZDR/dB ρhv 相态 ZH/dBZ ZDR/dB ρhv 相态 19.5° 2.5 km 33.6 0.85 0.93 冰水混合 20.0 -0.03 0.975 小水滴 5.5 km 16.8 0.39 0.94 冰晶(柱状) 2.2 16.7 0.91 冰晶(枝状) 9.9° 2.5 km 33.7 0.63 0.93 冰水混合 21.6 0.04 0.92 冰水混合(含小霰粒) 5.1 km 13.0 0.74 0.98 冰晶(柱状) 5.1 1.18 0.97 冰晶(枝状)
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表 3 2020年2月15日01：30 9.9°仰角关键层降水粒子偏振量和相态时间变化表
Table 3 Polarimetric parameters and hydrometeor classification at key levels at 9.9° elevation at 0130 BT 15 Feb 2020c
高度 系统后部 系统前部 ZH/dBZ ZDR/dB ρhv 相态 ZH/dBZ ZDR/dB ρhv 相态 3.0 km 28.0 0.57 0.95 冰水混合 25.4 1.89 0.91 冰水混合(含大水滴) 0.79 km 21.1 -0.14 0.92 小雪花 10.4 -0.21 0.93 冰粒
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