Vertically pointing Ka-band Millimeter Radar has a good detection sensitivity and high temporal-spatial resolution. The Doppler spectral density data can be utilized to retrieve the detailed cloud structure and micro-physical parameters. In this study, a stratiform precipitation case in 2016 CAMs South China Precipitation Test is used to retrieve the vertical air velocity in clouds and drop size distribution, and compare the DSD results with Distrometer and MRR. First, Liquid droplets trace methods is used to get the vertical airspeed which can obtain Doppler spectral in static air. Then we retrieve DSD from the translated Doppler spectrum by a V-D relation. Last, Normalized Gamma Distribution is used to fit the retrieved DSD. Several conclusions are get from the case study:(1)Weak downwards flow dominates the vertical air motions in cloud between the height from 1 km to the 0℃ level height. The clear-air echo caused by plankton contamination and noisy echo influence the Doppler spectral, as well as the phenomenon of saturation in Z existing near surface layer and affect the results of vertical air motions too. (2) Differences in Z is found in the detection of CR、MRR and Distrometer. MRR's Z data has a smaller difference with Distrometer than CR. In stable stratiform precipitation, Doppler spectral of CR and MRR are almost consistent. (3) In the comparison test of CR and MRR, DSD retrieval results are highly sensitive to the introduction of vertical Airspeed. Because of airspeed changing with height, DSD from CR has an overall increasing number concentration and decreasing value of average radium. DSDs retrieved by the two instruments' spectrum have a similar distribution along all heights, demonstrating the validity of retrieving DSD from Doppler spectral. (4) In the comparison of Gamma fitting parameters of CR and distrometer, the DSD of CR introducing vertical air motions results in has a smaller Dm and a same Nw magnitude comparing to Distrometer.
MA Ningkun
,
LIU Liping
,
ZHENG Jiafeng
. Application of Doppler Spectral Density Data in Vertical Air Motions and Drop Size Distribution Retrieval in Cloud and Precipitation by Ka-band Millimeter Radar[J]. Plateau Meteorology, 2019
, 38(2)
: 325
-339
.
DOI: 10.7522/j.issn.1000-0534.2018.00127
[1]Gossard E E. 1994. Measurement of cloud droplet size spectra by Doppler Radar[J]. Journal of Atmospheric & Oceanic Technology, 11(3):712-726.
[2]Hildebrand P H, Sekhon R S. 1974. Objective determination of the noise level in Doppler spectra[J]. Journal of Applied Meteorology, 13(7):808-811.
[3]Kollias P, Albrecht B A. 2000. The turbulence structure in a continental stratocumulus cloud from millimeter-wavelength Radar observations[J]. Journal of the Atmospheric Sciences, 57(57):2417-2434.
[4]Kollias P, Albrecht B A, Marks F. 2002. Why Mie?[J]. Bulletin of the American Meterological Society, 83(10):1471-1483.
[5]Kollias P, Albrecht B A, Lhermitte R, et al. 2001. Radar observations of updrafts, downdrafts, and turbulence in fair-weather cumuli[J]. Journal of the Atmospheric Sciences, 58(13):1750-1766.
[6]Lhermitte R M. 1966. Probing air motion by doppler analysis of Radar clear air returns[J]. Journal of the Atmospheric Sciences, 23(5):575-591.
[7]Petitdidier M, Sy A, Garrouste A, et al. 1997. Statistical characteristics of the noise power spectral density in UHF and VHF wind profilers[J]. Radio Science, 32(3):1229-1247.
[8]Rogers R R. 1964. An extension of the Z-R relation for Doppler radars[C]. The 11th Weather Radar Conference AMS, Boston, MA., Goulder, Co., Sept., 14-18.
[9]Shupe M D, Kollias P, Poellot M, et al. 2008. On deriving vertical air motions from cloud Radar Doppler spectra[J]. Journal of Atmospheric & Oceanic Technology, 25(4):547.
[10]Testud J, Oury S, Black R A, et al. 2001. The concept of 'normalized' distribution to describe raindrop spectra:A tool for cloud physics and cloud remote sensing[J]. Journal of Applied Meteorology, 40(40):1118-1140.
[11]Ulbrich C W. 1983. Natural Variations in the analytical form of the raindrop size distribution[J]. J Climate and Applies Meteorology, 22(10):1764-1775.
[12]Yahao W U, Liu L. 2017. Statistical characteristics of raindrop size distribution in the Tibetan Plateau and Southern China[J]. Advances in Atmospheric Sciences, 34(6):727-736.
[13]Zheng J F, Liu L P, Zhu K Y, et al. 2017. A method for retrieving vertical air velocities in convective clouds over the Tibetan Plateau from TIPEX-Ⅲ Cloud Radar DopplerSpectra[J]. Remote Sensing, 9(9):964.
[14]樊雅文. 2012.云雷达资料订正及应用研究[D].南京: 南京信息工程大学.
[15]刘黎平, 谢蕾, 崔哲虎. 2014.毫米波云雷达功率谱密度数据的检验和在弱降水滴谱反演中的应用研究[J].大气科学, 38(2):223-236.
[16]刘黎平, 周淼. 2016.垂直指向的Ka波段云雷达观测的0℃层亮带自动识别及亮带的特征分析[J].高原气象, 35(3):734-744. DOI:10.7522/j. issn. 1000-0534.2014.00160.
[17]彭亮, 陈洪滨, 李柏. 2012.3 mm多普勒云雷达测量反演云内空气垂直速度的研究[J].大气科学, 36(1):1-10.
[18]阮悦, 阮征, 魏鸣, 等. 2018.基于C-FMCW雷达的高原夏季对流云垂直结构分析研究[J].高原气象, 37(1):93-105. DOI:10.7522/j. issn. 1000-0534.2017.00025.
[19]孙豪, 刘黎平. 2017.不同波段垂直探测雷达对降水云雨滴谱反演效果对比分析[J].成都信息工程大学学报, 32(1):35-40.
[20]王洪, 雷恒池, 杨洁帆. 2017.微降水雷达测量精度分析[J].气候与环境研究, 22(4):392-404.
[21]温龙, 刘溯, 赵坤, 等. 2015.两次降水过程的微降雨雷达探测精度分析[J].气象, 41(5):577-587.
[22]谢蕾, 刘黎平, 姚雯. 2014.毫米波雷达与雨滴谱仪观测弱降水的对比分析[J].成都信息工程学院学报, 29(1):39-46.
[23]吴亚昊, 刘黎平, 周筠珺, 等. 2016.雨滴谱的变化对降水估测的影响研究[J].高原气象, 35(1):220-230. DOI:10.7522/j. issn. 1000-0534.2014.00093.
[24]姚志刚, 杨超, 赵增亮, 等. 2018.毫米波雷达反演层状云液态水路径研究[J].高原气象, 37(1):223-233. DOI:10.7522/j. issn. 1000-0534.2016.00127.
[25]郑佳锋, 刘黎平, 刘艳霞, 等. 2016a.云雷达回波强度谱密度定标及云内大气垂直运动速度反演试验[J].高原气象, 35(6):1650-1661. DOI:10.7522/j. issn. 1000-0534.2015.00064.
[26]郑佳锋, 刘黎平, 曾正茂, 等. 2016b. Ka波段毫米波云雷达数据质量控制方法[J].红外与毫米波学报, 35(6):748-757.
[27]郑娇恒, 陈宝君. 2007.雨滴谱分布函数的选择:M-P和Gamma分布的对比研究[J].气象科学, 27(1):17-25.
