The scattering mechanism of squall lines with C-Band dual polarization radar. Part I: echo characteristics and particles phase recognition

doi:10.1007/s11707-020-0863-8

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 221-235 https://doi.org/10.1007/s11707-020-0863-8

RESEARCH ARTICLE

The scattering mechanism of squall lines with C-Band dual polarization radar. Part I: echo characteristics and particles phase recognition

Jiashan ZHU¹, Ming WEI¹(

), Sinan GAO¹, Hanfeng HU¹, Lei MA²

¹. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
². Leihua Electronic Technology Research Institute of Aviation Industry Corporation of China, Wuxi 214063, China

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Abstract

Squall line is a kind of common mesoscale disaster weather. At present, there are few studies on the elaborate detection of squall line by dual polarization radar. With the dual polarization upgrade of weather radar network, we need to study the relationship between squall line echoes of base data and polarization data to reveal new echo phenomena and formation mechanisms. The relationship between radar parameters and atmospheric physical processes also need to be examined. Based on the NUIST CDP radar, a squall line in the Yangtze and Huaihe River basin that occurred from July 30 to 31, 2014 is analyzed. The results show that polarization parameters have obvious advantages in the characteristics analysis of size, phase state, shape and orientation of the water condensate particles. The phase states of water condensate particles in convection cell can be distinguished through comparative discussion. Several phase states exist in the squall line, including small, medium and large raindrops, melting hails, dry hails and ice crystal particles and the Z_DR column can be used to identify the location of the main updraft. In addition, the polarization parameters are more sensitive to the melting layer. The gust front is presented as a narrow linear echo in Z affected by strong turbulence. It is an obvious velocity convergence line in V and approximately 0.70 in r_HV. The Z_DR can be used as a criterion to distinguish the horizontal and vertical scale of turbulence. The deforming turbulence, which is affected by environmental airflow, will cause an abnormally high Z_DR in the gust front and a negative Z_DR before and after the gust front. The variation of Z_DR depends on the turbulence arrangement, orientation and relative position between turbulence and radar. These dual polarization parameter characteristics offer insights into understanding the structure and evolution of the squall line.

Keywords dual polarization Doppler radar RHI squall line gust front turbulence

Corresponding Author(s): Ming WEI

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 20 April 2021 Issue Date: 26 August 2022

Cite this article:

Jiashan ZHU,Ming WEI,Sinan GAO, et al. The scattering mechanism of squall lines with C-Band dual polarization radar. Part I: echo characteristics and particles phase recognition[J]. Front. Earth Sci., 2022, 16(2): 221-235.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0863-8
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/221

Tab.1 Main characteristics of NUIST CDP radar

Fig.1 The location of NUIST CDP radar and the main meteorological observation stations, and the influence range of squall line in this study. The circle represents the observation range of NUIST CDP radar.

Fig.2 Weather charts at 08:00 on July 30, 2014, (a) 500 hPa, (b) 700 hPa and (c) 850 hPa. The black line represents isobaric surface, the vector arrow represents wind, and the color represents temperature.

Fig.3 Same as in Fig. 2 but at 20:00 on July 30, 2014.

Fig.4 T-logP plots at 08:00 on July 30, 2014; (a) Nanyang, (b) Anqing and (c) Nanjing.

Tab.2 Sounding data in Nanjing at 08:00 on July 30, 2014

Fig.5 Meteorological elements in (a) Hefei and (b) Nanjing.

Fig.6 (a) The average wind direction in 1 min, (b) the horizontal average wind speed in 1 min and (c) the precipitation per minute of automatic weather station in NUIST on July 31, 2014. The time of gust front and squall line passed through are circled.

Fig.7 Squall line echoes taken at 1.45° at (a) 22:22 Z, (b) 22:57 Z, (c) 23:28 Z and (e) 23:28 V on July 30, 2014, and (d) 01:38 Z and (f) 01:38 V on July 31, 2014. The distance circles represent 30 km. The red circles in (a) and (e) are the location of newly scattered convection bubbles and mesocyclones, respectively.

Fig.8 Squall line echoes taken at 1.45° from (a) Z, (b) Z_DR, (c) K_DP and (d) r_HV at 23:28 on July 30, 2014. The distance circles represent 30 km. The red section line in (a) represents the location of the vertical cross section shown in Fig. 9 and Fig. 10.

Fig.9 The vertical cross section at 268° (follow the red section line in Fig. 8(a)) based on the VCP data at 23:28 on July 30, 2014.

Fig.10 RHI echoes of (a) Z, (b) V, (c) Z_DR, (d) K_DP and (e) r_HV at 23:25:41; (f) L_DR at 23:26:18 on July 30, 2014. The thick arrows in (b) represent airflow.

Fig.11 Results of hydrometeor classification by HCA at 23:25:41 on July 30, 2014. The HCA classes are defined as (from bottom to top of color bar) GC= ground clutter, including that due to anomalous propagation, CA= clear air, including turbulent and biological scatterers, DS= dry aggregated snow, WS= wet snow, CR= crystals of various orientations, GR= graupel, BD= big drops, RA= light and moderate rain, HR= heavy rain, and RH= a mixture of rain and hail.

Fig.12 Squall line echoes taken at 1.45° for (a) Z, (b) V, (c) Z_DR and (d) r_HV at 00:37, 00:44 and 00:51, respectively, on July 31, 2014. The distance circle and dotted line in (a), (b) and (d) represent 30 km and the location of gust front, respectively.

Fig.13 Illustrating diagram of deforming turbulence echoes in the environmental westerly.

1	V N Bringi, V Chandrasekar (2001). Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge: Cambridge University Press
2	P Bukovcic, A V Ryzhkov, D Zrnić, G Zhang (2018). Polarimetric radar relations for quantification of snow based on disdrometer data. J Appl Meteorol Climatol, 57(1): 103–120 https://doi.org/10.1175/JAMC-D-17-0090.1
3	H B Bluestein, M M French, R L Tanamachi, S Frasier, K Hardwick, F Junyent, A L Pazmany (2007). Close-range observations of tornadoes in supercells made with a dual-polarization, X-band, mobile Doppler radar. Mon Weather Rev, 135(4): 1522–1543 https://doi.org/10.1175/MWR3349.1
4	D Cimini, M Nelson, J Guldner, R Ware (2015). Forecast indices from a ground-based microwave radiometer for operational meteorology. Atmos Meas Tech, 8(1): 315–333 https://doi.org/10.5194/amt-8-315-2015
5	M Du, L Liu, Z Hu, R Yu (2012). Quality control of differential propagation phase shift for dual linear polarization radar. Journal of Applied Meteorological Science, 23(6): 710–720
6	W A Gallus Jr, N A Snook, E V Johnson (2008). Spring and summer severe weather reports over the midwest as a function of convective mode: a preliminary study. Weather Forecast, 23(1): 101–113 https://doi.org/10.1175/2007WAF2006120.1
7	E Gaviola, A F Fuertes (2010). Hail formation, vertical currents, and icing of aircraft. J Atmos Sci, 4(4): 116–120
8	B Geerts (1998). Mesoscale convective systems in the southeast United States during 1994–95: a survey. Weather Forecast, 13(3): 860–869 https://doi.org/10.1175/1520-0434(1998)013<0860:MCSITS>2.0.CO;2
9	S E Giangrande, J M Krause, A V Ryzhkov (2008). Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D Radar. J Appl Meteorol Climatol, 47(5): 1354–1364 https://doi.org/10.1175/2007JAMC1634.1
10	Y He, H Xiao, D Lv (2010). Analysis of hydrometeor distribution characteristics in stratiform clouds using polarization Radar. Chin J Atmos Sci, 34(1): 23–34
11	R A Houze Jr, M I Biggerstaff, S A Rutledge, B F Smull (1989). Interpretation of doppler weather radar display of midlatitude mesoscale convective systems. Bull Am Meteorol Soc, 70(6): 608–619 https://doi.org/10.1175/1520-0477(1989)070<0608:IODWRD>2.0.CO;2
12	Z Hu, L Liu, R Chu, R Jin (2008). Comparison of different attenuation correction methods and their effects on estimated rainfall using X-band dual linear polarimetric radar. Acta Meteorol Sin, 66(2): 251–261
13	H Huang, G Zhang, K Zhao, S Giangrande (2017). A hybrid method to estimate specific differential phase and rainfall with linear programming and physics constraints. IEEE Trans Geosci Remote Sens, 55(1): 96–111 https://doi.org/10.1109/TGRS.2016.2596295
14	Q Huang, M Wei, H Hu, M I Abro (2018). Analysis of atmospheric wind, temperature and humidity structure and dual-polarization radar parameters of clear air echo. Meteorological Monthly, 44(4): 526–537
15	Z Huang, G Xu, X Wang, Y Tang (2014). Analysis on two hailstorm events in Xianning based on observations of ground-based microwave radiometer. Meteorological Monthly, 40(2): 216–222
16	A J Illingworth, J W F Goddard, S M Cherry (1987). Polarization radar studies of precipitation development in convective storms. Q J R Meteorol Soc, 113(476): 469–489 https://doi.org/10.1002/qj.49711347604
17	T Islam, M A Rico-Ramirez, D Han, P K Srivastava (2014). Sensitivity associated with bright band/melting layer location on radar reflectivity correction for attenuation at C-band using differential propagation phase measurements. Atmos Res, 135–136(1): 143–158 https://doi.org/10.1016/j.atmosres.2013.09.003
18	M R Kumjian, A P Khain, N Benmoshe, E Ilotoviz, A V Ryzhkov, V T J Phillips (2014). The anatomy and physics of ZDR columns: investigating a polarimetric radar signature with a spectral bin microphysical mode. J Appl Meteorol Climatol, 53(7): 1820–1843 https://doi.org/10.1175/JAMC-D-13-0354.1
19	C Langston, J Zhang, K Howard (2007). Four-dimensional dynamic radar mosaic. J Atmos Ocean Technol, 24(5): 776–790 https://doi.org/10.1175/JTECH2001.1
20	L Levi, L Lubart, J Lassig (1994). Study of a convective storm series and of precipitated hail in south Argentina. Atmos Res, 33(1–4): 75–91 https://doi.org/10.1016/0169-8095(94)90014-0
21	L Ma (2018). Data quality control for weather radar reflectivity ractor and its application in regional radar composite. Dissertation for Master’s Degree. Nanjing: Nanjing University of Information Science & Technology
22	P F Meischner, V N Bringi, D Heimann, H Holler (1991). A squall line in Southern Germany: kinematics and precipitation formation as deduced by advanced polarimetric and doppler radar measurements. Mon Weather Rev, 119(3): 678–701 https://doi.org/10.1175/1520-0493(1991)119<0678:ASLISG>2.0.CO;2
23	Z Meng, D Yan, Y Zhang (2013). General features of squall lines in East China. Mon Weather Rev, 141(5): 1629–1647 https://doi.org/10.1175/MWR-D-12-00208.1
24	F P Oliveira, M D Oyama (2009). Radiosounding-derived convective parameters for the alcantara launch center. J Aerosp Technol Manag, 1(2): 211–216 https://doi.org/10.5028/jatm.2009.0102211216
25	J Pan, L Jiang, M Wei, C Luo, L Gao, X Zheng, J Peng (2020). Analysis of a high precipitation supercell based on dual polarization radar observations. Acta Meteorol Sin, 78(1): 86–100
26	H S Park, A V Ryzhkov, D S Zrnic, K Kim (2009). The hydrometeor classification algorithm for the polarimetric WSR-88D: description and application to an MCS. Weather Forecast, 24(3): 730–748 https://doi.org/10.1175/2008WAF2222205.1
27	M D Parker, R H Johnson (2000). Organizational modes of midlatitude mesoscale convective systems. Mon Weather Rev, 128(10): 3413–3436 https://doi.org/10.1175/1520-0493(2001)129<3413:OMOMMC>2.0.CO;2
28	R M Rasmussen, A J Heymsfield (1987). Melting and shedding of graupel and hail. Part I: model physics. J Atmos Sci, 44(19): 2754–2763 https://doi.org/10.1175/1520-0469(1987)044<2754:MASOGA>2.0.CO;2
29	G S Romine, D W Burgess, R B Wilhelmson (2008). A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Mon Weather Rev, 136(8): 2849–2870 https://doi.org/10.1175/2008MWR2330.1
30	J M Straka, D S Zrnic, A V Ryzhkov (2000). Bulk hydrometeor classification and quantification using polarimetric radar data: synthesis of relations. J Appl Meteorol, 39(8): 1341–1372 https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2
31	V I Tatarski (1971). The effects of the turbulent atmosphere on wave propagation. National Technical Information, TT-68–50464
32	R A Wakimoto, V N Bringi (1988). Dual-polarization observations of microbursts associated with intense convection: the 20 July storm during the MIST Project. Mon Weather Rev, 116(8): 1521–1539 https://doi.org/10.1175/1520-0493(1988)116<1521:DPOOMA>2.0.CO;2
33	Y Wang, V Chandrasekar (2009). Algorithm for estimation of the specific differential phase. J Atmos Ocean Technol, 26(12): 2565–2578 https://doi.org/10.1175/2009JTECHA1358.1
34	J W Wilson, T M Weckwerth, J Vivekanandan, R M Wakimoto, R W Russell (1994). Boundary layer clear-air radar echoes: origin of echoes and accuracy of derived winds. J Atmos Ocean Technol, 11(5): 1184–1206 https://doi.org/10.1175/1520-0426(1994)011<1184:BLCARE>2.0.CO;2
35	G Zhang, J Vivekanandan, E Brandes (2001). A method for estimating rain rate and drop size distribution from polarimetric radar measurements. Geoscience & Remote Sensing IEEE Transactions on, 39(4): 830–841 https://doi.org/10.1109/36.917906
36	L Zhang, R Guo, N He, X Liao (2013). Characteristic analusis of a hail event in Beijing. Meteorol Sci Technol, 41(1): 114–120

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