Guan Li, Dai Jianhua, Tao Lan, et al. Application of QVP method to winter precipitation observation based on polarimetric radar. J Appl Meteor Sci, 2021,32(1):91-101. DOI:  10.11898/1001-7313.20210108.
Citation: Guan Li, Dai Jianhua, Tao Lan, et al. Application of QVP method to winter precipitation observation based on polarimetric radar. J Appl Meteor Sci, 2021,32(1):91-101. DOI:  10.11898/1001-7313.20210108.

Application of QVP Method to Winter Precipitation Observation Based on Polarimetric Radar

DOI: 10.11898/1001-7313.20210108
  • Received Date: 2020-09-18
  • Rev Recd Date: 2020-11-24
  • Publish Date: 2021-01-31
  • 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.
  • Fig. 1  The meteorological observation network in Shanghai

    Fig. 2  Sketch map of QVP (from Reference [15])

    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

    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

    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

    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

    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

    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

    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

    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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    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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    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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    • Received : 2020-09-18
    • Accepted : 2020-11-24
    • Published : 2021-01-31

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