Abstract
In this study, the correlation between simulated and measured radar velocity spectrum width (σv) is investigated. The results show that the dendrites growth zones (DGZs) and needles growth zones (NGZs) mostly contain dendrites (DN) and needles (NE), respectively.
Clear σv zones (1.1 < σv (m s−1) < 1.3 and 0.3 < σv (m s−1) < 0.7 for the DGZ and NGZ, respectively) could be identified in the case studies (27 and 28 February 2016) near altitudes corresponding to temperatures of −15°C and −5°C, according to the Japan Meteorological Agency and mesoscale model reanalysis data. Oblate particles with diverse particle shapes were observed in the DGZ with σV > 1.2 m s−1, a differential reflectivity (ZDR) higher than 0 dB, and a cross-correlation coefficient (ρhv) less than 0.96. In contrast, prolate particles with relatively uniform shapes were observed in the NGZ with σv < 0.6 m s−1, a ZDR less than 0 dB, and ρhv higher than 0.97.
The simulation results show that the DN exhibited a larger σv compared to the NE, and this observed σv was strongly dependent on the wind fluctuations (v’) due to turbulence or wind shear. In contrast, the NE exhibited a significantly small σv ∼ 0.55 m s−1, which converges irrespective of v’. In addition, a strong correlation between the measured σv values at five radar elevation angles (θ = 6.2°, 9.1°, 13.1°, 19°, and 80°) and those simulated in this study confirmed the significance of the analysis results.
摘要
本文研究了模拟和实测雷达速度谱宽之间的相关性。结果表明,枝状固态水凝物增长区(DGZs)和针状固态水凝物增长区(NGZs)主要分别含有枝状冰晶(DN)和针状冰晶(NE)。
根据日本气象厅和中尺度模式再分析数据,在与-15°C和-5°C温度相对应的高度附近案例(2016年2月27日和28日)的研究中,雷达速度谱宽区(DGZ的雷达速度谱宽范围是1.1 m s–1 至 1.3 m s–1,NGZ的雷达速度谱宽范围是0.3 m s–1 至 0.7 m s–1)可以被清晰地识别出来。在DGZ中观察到具有不同颗粒形状的扁椭球粒子,其雷达速度谱宽大于1.2 m s–1,差分反射率高于0 dB,互相关系数小于0.96。相比之下,在NGZ中,观察到具有相对均匀形状的长椭球粒子,其雷达速度谱宽小于0.6 m s–1,差分反射率小于0 dB,互相关系数高于0.97。
模拟结果表明,与NE相比,DN对应着更大的雷达速度谱宽,并且观测到的雷达速度谱宽强烈依赖于湍流或风切变引起的风波动。相比之下,NE表现出明显较小的雷达速度谱宽~ 0.55 m s–1,其收敛与风波动无关。此外,在五个雷达仰角(θ=6.2°、9.1°、13.1°、19°和80°)下测得的雷达速度谱宽值与本研究中模拟得到的雷达速度谱宽值之间表现出一种强相关性,印证了分析结果的重要性。
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References
Andrić, J., M. R. Kumjian, D. S. Zrnić, J. M. Straka, and V. M. Melnikov, 2013: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study. J. Appl. Meteorol. Climatol., 52, 682–700, https://doi.org/10.1175/JAMC-D-12-028.1.
Atlas, D., R. C. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11, 1–35, https://doi.org/10.1029/RG011i001p00001.
Auer, A. H. Jr., and D. L. Veal, 1970: The dimension of ice crystals in natural clouds. J. Atmos. Sci., 27, 919–926, https://doi.org/10.1175/1520-0469(1970)027<0919:TDOICI>2.0.CO;2.
Bechini, R., L. Baldini, and V. Chandrasekar, 2013: Polarimetric radar observations in the ice region of precipitating clouds at C-band and X-band radar frequencies. J. Appl. Meteorol. Climatol., 52, 1147–1169, https://doi.org/10.1175/JAMC-D-12-055.1.
Böhm, H. P., 1989: A general equation for the terminal fall speed of solid hydrometeors. J. Atmos. Sci., 46, 2419–2427, https://doi.org/10.1175/1520-0469(1989)046<2419:AGEFTT>2.0.CO;2.
Boodoo, S., D. Hudak, N. Donaldson, and M. Leduc, 2010: Application of dual-polarization radar melting-layer detection algorithm. J. Appl. Meteorol. Climatol., 49, 1779–1793, https://doi.org/10.1175/2010JAMC2421.1.
Brewster, K. A., and D. S. Zrnić, 1986: Comparison of eddy dissipation rates from spatial spectra of Doppler velocities and Doppler spectrum widths. J. Atmos. Oceanic Technol., 3, 440–452, https://doi.org/10.1175/1520-0426(1986)003<0440:COEDRF>2.0.CO;2.
Chandrasekar, V., R. Keränen, S. Lim, and D. Moisseev, 2013: Recent advances in classification of observations from dual polarization weather radars. Atmospheric Research, 119, 97–111, https://doi.org/10.1016/j.atmosres.2011.08.014.
Dolan, B., and S. A. Rutledge, 2009: A theory-based hydrometeor identification algorithm for X-band polarimetric radars. J. Atmos. Oceanic Technol., 26, 2071–2088, https://doi.org/10.1175/2009JTECHA1208.1.
Doviak, R. J., and D. S. Zrnić, 2006: Doppler Radar and Weather Observations. Courier Corporation.
Gent, R. W., N. P. Dart, and J. T. Cansdale, 2000: Aircraft icing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 358(1776), 2873–2911, https://doi.org/10.1098/rsta.2000.0689.
Giangrande, S. E., and A. V. Ryzhkov, 2008: Estimation of rainfall based on the results of polarimetric echo classification. J. Appl. Meteorol. Climatol., 47, 2445–2462, https://doi.org/10.1175/2008JAMC1753.1.
Giangrande, S. E., J. M. Krause, and A. V. Ryzhkov, 2008: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. J. Appl. Meteorol. Climatol., 47, 1354–1364, https://doi.org/10.1175/2007JAMC1634.1.
Gourley, J. J., P. Tabary, and J. P. D. Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from non-precipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol., 24, 1439–1451, https://doi.org/10.1175/JTECH2035.1.
Hannesdóttir, Á., M. Kelly, and N. Dimitrov, 2019: Extreme wind fluctuations: Joint statistics, extreme turbulence, and impact on wind turbine loads. Wind Energy Science, 4(2), 325–342, https://doi.org/10.5194/wes-4-325-2019.
Hashino, T., M. Chiruta, D. Polzin, A. Kubicek, and P. K. Wang, 2014: Numerical simulation of the flow fields around falling ice crystals with inclined orientation and the hydrodynamic torque. Atmospheric Research, 150, 79–96, https://doi.org/10.1016/j.atmosres.2014.07.003.
Hölzer, A., and M. Sommerfeld, 2008: New simple correlation formula for the drag coefficient of non-spherical particles. Powder Technology, 184, 361–365, https://doi.org/10.1016/j.powtec.2007.08.021.
Ishizaka, M., 1993: An accurate measurement of densities of snowflakes using 3-D microphotographs. Annals of Glaciology, 18, 92–96, https://doi.org/10.3189/S0260305500011319.
Istok, M. J., and R. J. Doviak, 1986: Analysis of the relation between Doppler spectral width and thunderstorm turbulence. J. Atmos. Sci., 43, 2199–2214, https://doi.org/10.1175/1520-0469(1986)043<2199:AOTRBD>2.0.CO;2.
Ji, W. S., and P. K. Wang, 1991: Numerical simulation of three-dimensional unsteady viscous flow past finite cylinders in an unbounded fluid at low intermediate Reynolds numbers. Theoretical and Computational Fluid Dynamics, 3, 43–59, https://doi.org/10.1007/BF00271515.
Kennedy, P. C., and S. A. Rutledge, 2011: S-band dual-polarization radar observations of winter storms. J. Appl. Meteorol. Climatol., 50, 844–858, https://doi.org/10.1175/2010JAMC2558.1.
Kikuchi, K., T. Kameda, K. Higuchi, and A. Yamashita, 2013: A global classification of snow crystals, ice crystals, and solid precipitation based on observations from middle latitudes to polar regions. Atmospheric Research, 132–133, 460–472, https://doi.org/10.1016/j.atmosres.2013.06.006.
Knupp, K. R., and W. R. Cotton, 1982: An intense, quasi-steady thunderstorm over mountainous terrain. Part II: Doppler radar observations of the storm morphological structure. J. Atmos. Sci., 39, 343–358, https://doi.org/10.1175/1520-0469(1982)039<0343:AIQSTO>2.0.CO;2.
Kunii, D., and O. Levenspiel, 1969: Entrapment and elutriation from fluidized beds. Journal of Chemical Engineering of Japan, 2, 84–88, https://doi.org/10.1252/jcej.2.84.
Kuroiwa, D., Y. Mizuno, and M. Takeuchi, 1967: Micromeritical properties of snow. Physics of Snow and Ice: Proceedings, 1, 751–772, http://hdl.handle.net/2115/20340.
Lee, J.-E., S.-H. Jung, H.-M. Park, S. Kwon, P.-L. Lin, and G. W. Lee, 2015: Classification of precipitation types using fall velocity-diameter relationships from 2D-video distrometer measurements. Adv. Atmos. Sci., 32, 1277–1290, https://doi.org/10.1007/s00376-015-4234-4.
Lee, J. T., 1977: Application of Doppler weather radar to turbulence measurements which affect aircraft. NSSL-1, FAA/RD-77/145.
List, R., and R. S. Schemenauer, 1971: Free-fall behavior of planar snow crystals, conical graupel and small hail. J. Atmos. Sci., 28, 110–115, https://doi.org/10.1175/1520-0469(1971)028<0110:FFBOPS>2.0.CO;2.
Mason, B. J., 1971: The Physics of Clouds. Clarendon Press.
Matrosov, S. Y., R. F. Reinking, R. A. Kropfli, and B. W. Bartram, 1996: Estimation of ice hydrometeor types and shapes from radar polarization measurements. J. Atmos. Oceanic Technol., 13, 85–96, https://doi.org/10.1175/1520-0426(1996)013<0085:EOIHTA>2.0.CO;2.
Matrosov, S. Y., R. F. Reinking, and I. V. Djalalova, 2005: Inferring fall attitudes of pristine dendritic crystals from polarimetric radar data. J. Atmos. Sci., 62, 241–250, https://doi.org/10.1175/JAS-3356.1.
Nakaya, U., and T. TeradaJr., 1935: Simultaneous observations of the mass, falling velocity and form of individual snow crystals. Journal of the Faculty of Science, Hokkaido Imperial University, 1, 191–200.
Nettesheim, J. J., and P. K. Wang, 2018: A numerical study on the aerodynamics of freely falling planar ice crystals. J. Atmos. Sci., 75, 2849–2865, https://doi.org/10.1175/JAS-D-18-0041.1.
Nygaard, B. E. K., J. E. Kristjánsson, and L. Makkonen, 2011: Prediction of in-cloud icing conditions at ground level using the WRF model. J. Appl. Meteorol. Climatol., 50, 2445–2459, https://doi.org/10.1175/JAMC-D-11-054.1.
O’Connor, A., and D. Kearney, 2018: Evaluating the effect of turbulence on aircraft during landing and take-off phases. International Journal of Aviation, Aeronautics, 5(4), 10, https://doi.org/10.15394/ijaaa.2018.1284.
Park, H. S., A. V. Ryzhkov, D. S. Zrnić, and K.-E. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24, 730–748, https://doi.org/10.1175/2008WAF2222205.1.
Politovich, M. K., 2003: Aircraft icing. Encyclopedia of Atmospheric Sciences, J. R. Holton, Ed., Academic Press, 68–75, https://doi.org/10.1016/B0-12-227090-8/00055-5.
Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. 2nd ed. Kluwer Academic Publishers, 954 pp.
Ralph, F. M., and Coauthors, 2005: Improving short-term (0–48 h) cool-season quantitative precipitation forecasting: Recommendations from a USWRP workshop. Bull. Amer. Meteor. Soc., 86, 1619–1632, https://doi.org/10.1175/BAMS-86-11-1619.
Ribaud, J.-F., O. Bousquet, and S. Coquillat, 2016: Relationships between total lightning activity, microphysics and kinematics during the 24 September 2012 HyMeX bow — echo system. Quart. J. Roy. Meteor. Soc., 142, 298–309, https://doi.org/10.1002/qj.2756.
Ribaud, J.-F., L. A. T. Machado, and T. Biscaro, 2019: X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems. Atmospheric Measurement Techniques, 12, 811–837, https://doi.org/10.5194/amt-12-811-2019.
Ryzhkov, A., P. F. Zhang, H. Reeves, M. Kumjian, T. Tschallener, S. Trömel, and C. Simmer, 2016: Quasi-vertical profiles—A new way to look at polarimetric radar data. J. Atmos. Oceanic Technol., 33, 551–562, https://doi.org/10.1175/JTECH-D-15-0020.1.
Ryzhkov, A. V., D. S. Zrnić, and B. A. Gordon, 1998: Polarimetric method for ice water content determination. J. Appl. Meteorol., 37, 125–134, https://doi.org/10.1175/1520-0450(1998)037<0125:PMFIWC>2.0.CO;2.
Ryzhkov, A. V., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005a: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22(8), 1138–1155, https://doi.org/10.1175/JTECH1772.1.
Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnić, 2005b: The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809–824, https://doi.org/10.1175/BAMS-86-6-809.
Sekhon, R. S., and R. C. Srivastava, 1970: Snow size spectra and radar reflectivity. J. Atmos. Sci., 27, 299–307, https://doi.org/10.1175/1520-0469(1970)027<0299:SSSARR>2.0.CO;2.
Thompson, E. J., S. A. Rutledge, B. Dolan, V. Chandrasekar, and B. L. Cheong, 2014: A dual-polarization radar hydrometeor classification algorithm for winter precipitation. J. Atmos. Oceanic Technol., 31, 1457–1481, https://doi.org/10.1175/JTECH-D-13-00119.1.
Vivekanandan, J., V. N. Bringi, M. Hagen, and P. Meischner, 1994: Polarimetric radar studies of atmospheric ice particles. IEEE Trans. Geosci. Remote Sens., 32, 1–10, https://doi.org/10.1109/36.285183.
Wang, P. K., 2002: Ice Microdynamics. Academic Press.
Wang, P. K., and S. M. Denzer, 1983: Mathematical description of the shape of plane hexagonal snow crystals. J. Atmos. Sci., 40, 1024–1028, https://doi.org/10.1175/1520-0469(1983)040<1024:MDOTSO>2.0.CO;2.
Wang, P. K., and W. S. Ji, 1997: Numerical simulation of three-dimensional unsteady flow past ice crystals. J. Atmos. Sci., 54, 2261–2274, https://doi.org/10.1175/1520-0469(1997)054<2261:NSOTDU>2.0.CO;2.
Wang, P. K., and W. S. Ji, 2000: Collision efficiencies of ice crystals at low-intermediate Reynolds numbers colliding with supercooled cloud droplets: A numerical study. J. Atmos. Sci., 57, 1001–1009, https://doi.org/10.1175/1520-0469(2000)057<1001:CEOICA>2.0.CO;2.
Williams, E. R., and Coauthors, 2011: Dual polarization radar winter storm studies supporting development of NEXRAD-based aviation hazard products. Preprints, AMS 35th Conf. on Radar Meteorology, Pittsburgh, PA, AMS, 26–30.
Williams, E. R., and Coauthors, 2013: Validation of NEXRAD radar differential reflectivity in snowstorms with airborne microphysical measurements: Evidence for hexagonal flat plate crystals. Preprints, 36th Conf. on Radar Meteorology, AMS.
Willmarth, W. W., N. E. Hawk, and R. L. Harvey, 1964: Steady and unsteady motions and wakes of freely falling disks. The Physics of Fluids, 7, 197–208, https://doi.org/10.1063/1.1711133.
Wolde, M., and G. Vali, 2001: Polarimetric signatures from ice crystals observed at 95 GHz in winter clouds. Part I: Dependence on crystal form. J. Atmos. Sci., 58, 828–841, https://doi.org/10.1175/1520-0469(2001)058<0828:PSFICO>2.0.CO;2.
Xia, D. D., L. M. Dai, L. Lin, H. F. Wang, and H. T. Hu, 2021: A field measurement based wind characteristics analysis of a Typhoon in near-ground boundary layer. Atmosphere, 12(7), 873, https://doi.org/10.3390/atmos12070873.
Zhang, P., P. W. Chan, R. Doviak, and M. Fang, 2009: Estimate of Eddy Dissipation Rate Using Spectrum Width Observed by the Hong Kong TDWR Radar. Preprints, 34th Conf. on Radar Meteorology, Williamsburg, VA.
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This research study was supported by the Space Center Development Project (II) of the Ministry of Science and ICT (MSIT).
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Article Highlights
• Aerodynamic property analysis and identification of solid hydrometeors is performed using the radar spectrum width
• Dendrites and needles growth zones are identified using σv.
• Results can be used for aviation safety assessment and hydrometeor classification.
Author contributions
Sung-Ho SUH designed the study. Sung-Ho SUH and Eun-Ho CHOI collected the samples and performed the investigations. All these authors gathered the results and prepared the manuscript with contributions from all the coauthors. All the coauthors examined the results and checked the manuscript. All the authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The data obtained by the YIT in this study are available by request from the Korea Meteorological Administration (KMA).
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Suh, SH., Choi, EH., Kim, HI. et al. Possibility of Solid Hydrometeor Growth Zone Identification Using Radar Spectrum Width. Adv. Atmos. Sci. 40, 317–332 (2023). https://doi.org/10.1007/s00376-022-1472-0
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DOI: https://doi.org/10.1007/s00376-022-1472-0