The scattering mechanism of squall lines with C-Band dual polarization radar. Part II: the mechanism of an abnormal <i>Z</i><sub>DR</sub> echo in clear air based on the parameterization of turbulence deformation

doi:10.1007/s11707-021-0870-4

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 236-247 https://doi.org/10.1007/s11707-021-0870-4

RESEARCH ARTICLE

The scattering mechanism of squall lines with C-Band dual polarization radar. Part II: the mechanism of an abnormal Z_DR echo in clear air based on the parameterization of turbulence deformation

Jiashan ZHU, Ming WEI(

), Sinan GAO, Chunsong LU

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China

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Abstract

In part I, the clear air echo in front of the squall line is caused by turbulence diffraction, which makes the Z_DR echo characteristics different from particle scattering. To study the turbulence deformation phenomenon that is affected by environmental wind, the turbulence-related method is used to analyze the characteristics of three-dimensional turbulence energy spectrum density, and the parametric model of turbulence integral length scale and environmental wind speed is established. The results show that the horizontal scale of turbulence is generally larger than the vertical scale. The turbulence is nearly isotropic in the horizontal direction, presenting a flat ellipsoid with the vertical orientation of the rotation axis when there is no horizontal wind or the horizontal velocity is small. When horizontal wind exists, the turbulence scale increases along the dominant wind direction. The turbulence scale is positively correlated with the wind speed. The power function is used to fit the relationships of turbulence integral length scale and horizontal wind speed, which obtains the best fitting effect, and the goodness of fit (GF) is above 0.99 in each direction. The deforming turbulence can cause 8–9 dB Z_DR anomalies in the echo of dual polarization radar, which the ratio of scales in the dominant wind and the vertical direction of deforming turbulence (L_u/L_w) is around 4.3. The variation in Z_DR depends on the turbulence shape, orientation and the relative position between turbulence and radar. The shape of turbulence derived from radar detection results is consistent with that of the parametric model, which can provide a parametric scheme for turbulence research. The results reveal the mechanism of abnormal Z_DR echo caused by deforming turbulence.

Keywords dual polarization radar clear air echo Bragg diffraction deforming turbulence parameterization

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 II: the mechanism of an abnormal Z_DR echo in clear air based on the parameterization of turbulence deformation[J]. Front. Earth Sci., 2022, 16(2): 236-247.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0870-4
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/236

Fig.1 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.2 Average wind direction and speed in 30 min in Zhanjiang, Guangdong Province on March 13, 2017. The circles represent south winds with high speeds, crosses represent quasi-east winds with higher speeds, squares represent south winds with low speeds, and triangles represent south-east winds with high speeds.

Fig.3 (a) Normalized turbulence energy spectral density and (b) its probability density in case I, where u represents the east wind component, v represents the north wind component, and w represents the updraft component. S_u, S_v and S_w represent the turbulence integral length scale in the u, v and w directions, respectively. The dashed line in (a) is 10^-2/3.

Fig.4 Same as Fig. 3, but in case II.

Fig.5 Same as Fig. 3, but in case III.

Fig.6 Same as Fig. 3, but in case IV.

Fig.7 Probability density of the normalized turbulence energy spectrum density after horizontal coordinate rotation in (a) case III and (b) case IV, where u_r represents the component of the dominant flow direction within 30 min, v_r represents the component of the lateral average flow direction, and w_r represents the updraft component.

Fig.8 A parametric model of turbulence integral length scale (

∑

) and wind speed.

Fig.9 The relationship between radial velocity and turbulence scale in the gust front by the parametric model of turbulence integral length scale and wind speed.

1	J Bian, J Qiao, D Lv (2002). Reanalysis of the turbulent spectra in the atmospheric surface layer. Chin J Atmos Sci, 26(4): 474–480
2	W L Bragg (1913). The structure of some crystals as indicated by their diffraction of X-rays. Proc R Soc Lond, 89(610): 248–277
3	J A Businger, J C Wyngaard, Y Izumi, E F Bradley (1971). Flux-profile relationships in the atmospheric surface layer. J Atmos Sci, 28(2): 181–189 https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2
4	R J Doviak, D S Zrnic, R M Schotland (1994). Doppler Radar and weather observations. Appl Opt, 33(21): 4531
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	J M Frank, W J Massman, B E Ewers (2013). Underestimates of sensible heat flux due to vertical velocity measurement errors in non-orthogonal sonic anemometers. Agric Meteorol, 171–172: 72–81 https://doi.org/10.1016/j.agrformet.2012.11.005
7	J Guo, L Bian, Y Dai (2007). Measured CO2 concentration and flux at 16 m height during corn growing period on the North China Plain. Chin J Atmos Sci, 31(4): 695–707
8	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
9	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
10	J C Kaimal, J J Finnigan (1994). Atmospheric Boundary Layer Flows: Their Structure and Measurement. New York: Oxford University Press
11	M Kanda, A Inagaki, M O Letzel, S Raasch, T Watanabe (2004). LES study of the energy imbalance problem with eddy covariance fluxes. Boundary-Layer Meteorol, 110(3): 381–404 https://doi.org/10.1023/B:BOUN.0000007225.45548.7a
12	C A Knight, L J Miller (1993). First radar echoes from cumulus clouds. Bull Am Meteorol Soc, 74(2): 179–188 https://doi.org/10.1175/1520-0477(1993)074<0179:FREFCC>2.0.CO;2
13	A N Kolmogorov (1962). A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high reynolds number. J Fluid Mech, 13(1): 82–85 https://doi.org/10.1017/S0022112062000518
14	A N Kolmogorov (1941a). Energy dissipation in locally isotropic turbulence. Proceedings of the Royal Society a Mathematical Physical & Engineering Sciences, 434(1890): 16–18
15	A N Kolmogorov (1941b). The local structure of turbulence in incompressible viscous fluid for very large reynolds numbers. Sov Phys Usp, 30(4): 301–305
16	S Liao, D Jiang (2003). Method to extract turbulent characteristic parameters based on wavelet analysis. Journal of Combustion Science and Technology, 9(1): 21–28
17	Z Ma (1986). Information and Principle of Weather Radar Echo. Beijing: Science Press (in Chinese)
18	V M Melnikov, R J Doviak, D S Zrnic, D J Stensrud (2013). Structures of bragg scatter observed with the polarimetric WSR-88D. J Atmos Ocean Technol, 30(7): 1253–1258 https://doi.org/10.1175/JTECH-D-12-00210.1
19	A S Monin, A M Obukhov (1954). Basic laws of turbulent mixing in the surface layer of the atmosphere. Trudy Geofizicheskogo Instituta. Akademiya Nauk SSSR, 24(151): 163–187
20	L M Richardson, J G Cunningham, W D Zittel, R R Lee, R L Ice, V M Melnikov, N P Hoban, J Gebauer (2017). Bragg scatter detection by the WSR-88D. Part I: algorithm development. J Atmos Ocean Technol, 34(3): 465–478 https://doi.org/10.1175/JTECH-D-16-0030.1
21	P Sheng, J Mao, J Li, A Zhang, J Sang, N Pan (2003). Atmospheric Physics. Beijing: Peking University Press (in Chinese)
22	Y Tang (2014). The scattering mechanism and analysis of clear-air echo. Dissertation for Master’s Degree. Nanjing: Nanjing University of Information Science & Technology (in Chinese)
23	V I Tatarski (1971). The effects of the turbulent atmosphere on wave propagation. National Technical Information, TT-68–50464
24	V I Tatarski, R A Silverman, N Chako (1961). Wave propagation in a turbulent medium. Phys Today, 14(12): 46–51 https://doi.org/10.1063/1.3057286
25	G I Taylor (1938). The Spectrum of turbulence. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 919: 476–490
26	D Vickers, L Mahrt (1997). Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol, 14(3): 512–526 https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2
27	J Wang, W Wang, Y Ao, F Sun, G Shu (2007). Turbulence flux measurements under complicated conditions. Advances in Earth Science, 22(8): 791–797 (in Chinese)
28	J Wang, W Wang, S Liu, M Ma, X Li (2009). The problems of surface energy balance closure—an overview and case study. Advances in Earth Science, 24(7): 705–713 (in Chinese)
29	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
30	J M Wilczak, S P Oncley, S A Stage (2001). Sonic anemometer tilt correction algorithms. Boundary-Layer Meteorol, 99(1): 127–150 https://doi.org/10.1023/A:1018966204465
31	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
32	B Wu, H Zhang, Z Wang, H Zhu, Y Xie (2011). Study on turbulent structures and energy transfer during an advective fog period. Acta Scientiarum Naturalium Universitatis Pekinensis, 47(2): 295–301
33	J C Wyngaard (1990). Scalar fluxes in the planetary boundary layer—theory, modeling and measurement. Boundary-Layer Meteorol, 50(1–4): 49–75 https://doi.org/10.1007/BF00120518
34	Z Xu, S Liu, L Gong, J Wang, X Li (2008). A study on the data processing and quality assessment of the eddy covariance system. Advances in Earth Science, 23(4): 357–370
35	X Yao (2016). Data quality control and echo characteristics analysis of NUIST-C Band dual linear polarimetric doppler radar. Dissertation for Master’s Degree. Nanjing: Nanjing University of Information Science & Technology (in Chinese)

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