共查询到19条相似文献，搜索用时 171 毫秒
1.
使用中国气象局大气探测综合试验基地35 GHz毫米波云雷达和L波段风廓线雷达2016年5月1日-7月31日在降水条件下的观测数据,根据不同观测模式下两部雷达得到的数据,计算在一定高度区间内不同下落速度的降水粒子反射率因子变化量,初步分析不同下落速度的降水粒子对毫米波衰减的影响。结果表明:在持续时间较长的层状云降水且降水粒子在雷达观测范围内均匀分布条件下,毫米波衰减与降水粒子下落速度呈近似线性关系,且毫米波经过的路径长度越长,衰减越大;毫米波在经过1110~2430 m,1110~3510 m的高度区间时,下落速度处于3.5~7.5 m·s-1之间的降水粒子对毫米波的衰减作用导致毫米波云雷达所测的等效反射率因子分别减小约1~7 dB和2~11 dB。 相似文献
2.
对基于雷达反射率因子观测数据的层状云降水粒子谱参数反演算法进行研究。（1）给出层状云降水粒子谱参数的反演理论和反演算法流程;（2）选取吉林省伊通县的一次降水层状云过程进行反演试验和验证分析,利用雷达反射率因子观测数据反演得到雨滴平均直径和数浓度参数,并用放置在伊通县气象局观测场中的Parsivel激光雨滴谱仪的实测数据与近地层反演结果进行对比。结果发现,通过反演算法计算得到的滴谱与实测滴谱的变化趋势基本相同,而且反演的雨滴平均直径和数浓度在量级和数值上的大小与实测数据具有良好的一致性,说明该反演方法用于从现有天气雷达回波强度数据中挖掘出降水性层状云的微物理参数是可行的。 相似文献
3.
基于星载云雷达资料的东亚大陆云垂直结构特征分析 总被引：2，自引：0，他引：2
利用近5年(2006年6月—2011年4月)的Cloudsat卫星资料分析了东亚大陆云垂直结构特征。结果表明:(1)降水(文中可降水是根据观测到的可降水粒子信息计算到达地面的降水,并不是指地面观测到的实际降水)云和非降水云的雷达反射率(回波)垂直分布存在一定差异,除降水云反射率通常接地外,降水云主要集中在8 km以下,反射率通常为-20—15dBz,非降水云主要集中在4—12 km,反射率为-28—0 dBz;降水云雷达反射率频数大值中心在2 4 km,对应的雷达反射率为0—10 dBz,而非降水云出现在8—10 km,且对应的雷达反射率为-26—-24 dBz;(2)从雷达反射率廓线来看,降水云中雷达反射率随高度的变化先增强后减弱,而非降水云几乎不变;(3)液态降水云、固态降水云和毛毛雨降水云反射率的垂直分布明显不同;(4)液态降水云自11至7 km雷达反射率迅速增强,表明此高度是粒子快速增长的优势空间;(5)固态降水云中-15℃温度频数分布与雷达反射率频数大值中心有很好的对应关系,表明在-15℃附近的条件下冰相粒子凝华-碰冻是粒子增长的优势过程;(6)云的垂直结构随着季节变更而变化,降水云春季、夏季和秋季的雷达反射率垂直分布变化不明显,而冬季主要在低层;固态降水云的垂直分布频数大值中心从春季至冬季呈"双-单"中心交替变化,且与云中-15℃频数分布变化一致;非降水云雷达反射率垂直分布没有明显的季节变化;(7)深对流云和雨层云是形成降水粒子的主要云型。 相似文献
4.
5.
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 总被引：7，自引：2，他引：5
本文首先利用数值模拟的方法,分析了利用毫米波云雷达功率谱密度反演雨滴谱时,降水粒子米散射效应、空气湍流、空气上升速度等对雨滴谱和液态水含量等参数反演的影响;建立了功率谱密度处理及其直接反演雨滴谱、液态水含量、降水强度和空气上升速度的方法;并利用2012年7月在云南腾冲观测的二次弱降水数据,采用毫米波雷达和Ku波段微降水雷达观测的回波强度、径向速度垂直廓线以及780 m高度上的功率谱密度对比的方法,以及毫米波云雷达观测的780 m高度上功率谱密度、回波强度与地面雨滴谱计算得到的这些量的对比方法,分析了毫米波雷达数据的可靠性;并将780 m高度上毫米波雷达反演的雨滴谱与地面雨滴谱数据进行了对比,分析了毫米波雷达反演的雨滴谱的准确性;分析了毫米波雷达回波强度偏弱的原因,讨论了该高度以下降水对毫米波雷达衰减的影响。结果表明:空气湍流对弱降水微物理参数反演影响不大,而空气上升速度和米散射效应均对反演结果有一定影响;毫米波雷达观测到的径向速度和功率谱密度与微降水雷达比较一致,回波强度的垂直廓线的形状与微降水雷达也比较一致,但毫米波雷达观测的回波强度偏弱;与雨滴谱计算值相比,毫米波雷达观测的低层的回波强度也偏弱,天线上的积水是造成毫米波雷达回波强度变弱的主要原因。毫米波雷达观测的低层的功率谱密度与地面雨滴谱观测的数据形状比较一致,但有一定的位移。毫米波雷达反演的雨滴谱与地面观测的谱型和粒子大小也比较一致。这些结果初步验证了毫米波雷达观测的功率谱密度及其反演方法的可靠性。 相似文献
6.
毫米波雷达测云个例研究 总被引：9，自引：2，他引：7
云参数是影响降水和大气辐射过程的重要因子,但对云参数的遥感探测存在许多困难。利用35GHz的毫米波雷达进行云探测,并进行云参数反演研究,反演了云水含量、冰水含量和云滴有效直径的垂直廓线,得到了6类云况的垂直分布。结果表明：1）不同类型的云具有不同的云参数分布;2）在低于-15dBz的非降水云情况下,反演的云水含量及云滴有效直径较可靠;3）雷达探测的线性退偏振比因子,可以用于判别云中的过冷却水和冰晶,有助于更好了解云的宏微观特征。 相似文献
7.
根据2013—2014年5—10月西安地区观测得到的雨滴谱数据,结合C波段新一代多普勒天气雷达的观测资料,对西安地区43次积层混合云降水的平均雨滴谱分布、微物理特征量及雷达反射率因子Z和雨强R的关系进行统计分析。结果表明:积层混合云降水的平均雨滴谱呈单峰型,Gamma分布对降水大粒子的拟合明显优于M-P分布;积层混合云中雨滴数浓度最大值及对雨强贡献最大值均出现在雨滴直径小于1 mm的范围内;利用最小二乘法建立了西安地区积层混合云的Z-R关系Z=168R1.43;当雨滴谱数据计算的回波强度小于(大于)30 dBz,雷达对回波强度有明显高估(低估)现象,针对此现象提出了积层混合云雷达回波的5档修正方案;利用Z=168R1.43估算西安积层混合云降水个例的降雨量更接近实测降雨量,估算降雨量的相对误差从51.3%减小到25.4%。 相似文献
8.
两次降水过程的微降雨雷达探测精度分析 总被引：3，自引：3，他引：0
垂直指向微降雨雷达(MRR)能够测量从近地面至高空的雷达反射率因子和雨滴谱分布特征,对认识降水微物理结构,改进雷达定量降水估计精度有重要作用。为评估MRR探测的雨滴谱分布、降水和雷达回波精度,利用南京地区夏季观测的两次降水过程,将MRR与业务S波段天气雷达、二维视频雨滴谱仪、常规雨量筒观测进行层状云降水和对流性降水下的定量对比分析。结果表明,MRR垂直探测的雷达反射率因子与S波段雷达观测在中低层(＜4 km)平均差异＜1 dB, 但高层(＞4 km)出现显著低估,且该现象随降水强度增强更明显,这主要是雷达回波衰减导致。MRR在回波强度＜35 dBz时对降水率的探测精度较高,但在＞35 dBz时低估降水。其中,层状云降水的降水率比对流性降水更接近雨量筒观测。常规雨量筒对0.1 mm以下的降水无探测能力,而MRR探测敏感度较高,对于微弱降水率的估计效果也很好。由于MRR最大探测范围的限制,相对于2DVD而言,MRR探测的最大粒子直径低估、最小粒子浓度高估,但在中间段的探测效果和2DVD雨滴谱观测一致性较高。总体而言,MRR是一个有效的降水探测仪器,其探测结果在层状云降水过程中优于对流性降水过程。 相似文献
9.
2014—2017年四川地区开展了大范围云系观测科学试验。观测对象以盆地层状云系和积层混合云系为主,积状云(对流云)为辅。本文围绕试验目标、区域、观测要素、观测布局设计、观测方案设计、设备技术参数和典型个例等7个方面进行介绍,根据不同类型云系和降水变化特征,设计有针对性的观测方案,获得了不同类型云系和降水的多尺度连续性观测数据,为四川地区开展云和降水关系研究提供详实的综合型外场观测数据。层状降水云典型个例云高可达8km,云强核心部位的云雷达反射率可达28dBz,径向速度可达-6m·s~(-1),在0℃附近,反射率和退偏振因子LDR上有一条明显的亮带,表现为极大值,液态水主要集中于4.5km以下,随着雨强增大,液水含量增加,降水滴谱分布较窄,随着雨强减小,雨滴谱和速度谱变窄,但小粒径数浓度增加,说明对层状云雨强起主导作用的是雨滴直径,而不是数浓度;无降水层状云典型个例云层厚度为3.2km,云顶约为4km,云雷达反射率不超过0dBz,径向速度不超过5m·s~(-1),层状云内整层水汽含量和液水含量较为稳定,云中主要为液态粒子且粒径偏小、小粒径数浓度较高。 相似文献
10.
利用毫米波测云雷达反演层状云中过冷水 总被引：1，自引：0，他引：1
毫米波测云雷达已成为研究云内微物理参数的有效工具,利用其从混合相云中识别出过冷水,对人工影响天气及预防飞机积冰具有重要意义,对我国毫米波雷达的数据处理也具有借鉴作用。本文利用英国的35 GHz、94 GHz测云雷达,结合激光雷达和探空资料,采用阈值法,反演分析了层状云中的过冷水。结果表明:(1)毫米波雷达联合激光雷达可以识别层状云中的过冷水,其结果与微波辐射计测量的液态水路径或毫米波雷达的双峰谱相符合;(2)利用多普勒速度的双峰谱可以反演混合相云中的过冷水含量、冰晶含水量。混合相云的雷达反射率因子主要取决于冰晶,根据雷达反射率因子反演会低估云内液态水含量;(3)本次层状云降水的亮带以上含有较多过冷水,此处35 GHz的雷达回波强度随冰晶的增大而减弱,且冰晶的含水量主导了总液态水含量。 相似文献
11.
Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with simultaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between reflectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived. 相似文献
12.
The First Observed Cloud Echoes and Microphysical Parameter Retrievals by China''s 94-GHz Cloud Radar 总被引：1，自引：0，他引：1 
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下载免费PDF全文 WU Juxiu WEI Ming HANG Xin ZHOU Jie ZHANG Peichang LI Nan 《Acta Meteorologica Sinica》2014,28(3):430-443
By using the cloud echoes first successfully observed by China's indigenous 94-GHz SKY cloud radar, the macrostructure and microphysical properties of drizzling stratocumulus clouds in Anhui Province on 8 June2013 are analyzed, and the detection capability of this cloud radar is discussed. The results are as follows.(1) The cloud radar is able to observe the time-varying macroscopic and microphysical parameters of clouds,and it can reveal the microscopic structure and small-scale changes of clouds.(2) The velocity spectral width of cloud droplets is small, but the spectral width of the cloud containing both cloud droplets and drizzle is large. When the spectral width is more than 0.4 m s-1, the radar reflectivity factor is larger(over –10 dBZ).(3) The radar's sensitivity is comparatively higher because the minimum radar reflectivity factor is about–35 dBZ in this experiment, which exceeds the threshold for detecting the linear depolarized ratio(LDR) of stratocumulus(commonly –11 to –14 dBZ; decreases with increasing turbulence).(4) After distinguishing of cloud droplets from drizzle, cloud liquid water content and particle effective radius are retrieved. The liquid water content of drizzle is lower than that of cloud droplets at the same radar reflectivity factor. 相似文献
13.
为了揭示热带测雨卫星上的测雨雷达热带测雨卫星的星载雷达与X波段多普勒雷达在探测云的反射率因子的大小和结构方面的差异 ,用散射模式和数值模拟的方法讨论了这两种雷达的波长、雷达波入射方向、波瓣宽度等参量对反射率因子的大小和结构的影响 ,并利用所得结果讨论了两种雷达实际观测的差异。结果表明 :雷达波长和入射方向的不同引起的两种雷达测量的反射率因子的差异在 2 .0dBz以内 ,TRMMPR可在强回波中心探测到更大的反射率因子 ,并在很大程度上平滑了回波的结构 ,在强回波和弱回波区分别低估和高估 3～ 5dBz,造成了观测的云的面积增大、平均回波强度减小、面积积分降水量增大。这些理论结果还不能完全揭示两种雷达实际观测结果的差异 ,看来星载雷达和地基雷达探测结果的对比问题很复杂 ,其中雷达波的衰减问题是必须考虑的。 相似文献
14.
《Atmospheric Research》2008,87(3-4):297-314
This paper addresses the sensitivity of the relationships between radar reflectivity (Z) and liquid water content (M) for liquid water clouds to microphysical drizzle parameters by means of simulated radar observation at a frequency of 3 GHz of modeled cumulus clouds. A power law relationship for non drizzling clouds with water content as high as 3 gm− 3: Zc = 0.026 Mc1.61 is numerically derived and agreed with previous empirical relationships relative to cumulus and stratocumulus. This relationship is then used to explore the influence of drizzle on the correlation between radar reflectively and water content. Due to their large diameters with respect to cloud droplets, drizzle sized drops dominate radar reflectivity but do not carry the cloud water content so that reflectivity and liquid water content are expected to be not correlated in clouds containing drizzle. It is shown that for congestus or extreme congestus cumuli, microphysical conditions for which the Zc–Mc relationship can be used with a tolerance of 5 and 10% are provided whereas for humilis or mediocris cumuli, the presence of drizzle breaks down the Zc–Mc relationship whatever the situations. 相似文献
15.
毫米波测云雷达在降雪观测中的应用初步分析 总被引：2，自引：0，他引：2
本文利用毫米波云雷达联合称重式雨量计、气球探空和S波段天气雷达在北京对2015年11月三次降雪进行了观测,以2015年11月22~23日降雪过程为例,主要从降雪系统的宏观结构特征、微物理变化以及毫米波雷达在降雪探测中电磁波衰减情况、雪粒子含水量和地面降雪量估测几方面进行初步分析。结果表明:（1）毫米波云雷达具有高时空分辨率,能对降雪系统进行精细化探测,在降雪系统发展最旺盛的阶段能够通过反射率（Z）、退极化比（LDR）和径向速度（V）初步判断出云中是否含有过冷液滴;（2）降雪回波强度最大值能反映整层云系中含水量最大的区域,当最大值Z大于20 dBZ时,最大值的大小、最大值持续时间、最大值出现的高度与地面降水量成正相关,速度最大值表示云中粒子上升最大速度（速度为正时）或者粒子下落的最小速度（速度为负时）,主要分布在－0.5~2 m s?1,速度最小值表示粒子下落的最大速度,主要在－3~－1 m s?1;（3）随着高度增加反射率的垂直廓线会出现多个峰值,这是由于不同高度层风速分布不均造成的,降雪回波这种特点比降雨回波更明显;（4）对比Ka与S波段雷达反射率可知,两雷达反射率平均差值小于2.5 dBZ,Ka波段反射率略大S波段雷达反射率;（5）降雪量反演与地面降雪量仪数据对比,逐小时降雪量反演精度为20.38%,累计降雪量反演误差为6.58%,24小时累计降雪量绝对误差为1.9 mm,说明云雷达估算累计降雪量具有较高的可行性,能够很准确的反映地面实际降雪情况,当降雪系统发展旺盛时,雪粒子含水量分布在0.05~0.15 g m?3,在降雪初期或者降雪系统消散期,雪粒子含水量一般小于0.04 g m?3,能够很好地反映出整层降雪回波的雪粒子含水量。这些云雷达在降雪观测中的应用和初步分析结果可以更好的地了解降雪系统宏微观结构,为云模式的发展和人工影响天气中增雪潜力评估提供一些参考。 相似文献
16.
Influence of drizzle on Z–M relationships in warm clouds 总被引：1，自引：0，他引：1
This paper addresses the sensitivity of the relationships between radar reflectivity (Z) and liquid water content (M) for liquid water clouds to microphysical drizzle parameters by means of simulated radar observation at a frequency of 3 GHz of modeled cumulus clouds. A power law relationship for non drizzling clouds with water content as high as 3 gm− 3: Zc = 0.026 Mc1.61 is numerically derived and agreed with previous empirical relationships relative to cumulus and stratocumulus. This relationship is then used to explore the influence of drizzle on the correlation between radar reflectively and water content. Due to their large diameters with respect to cloud droplets, drizzle sized drops dominate radar reflectivity but do not carry the cloud water content so that reflectivity and liquid water content are expected to be not correlated in clouds containing drizzle. It is shown that for congestus or extreme congestus cumuli, microphysical conditions for which the Zc–Mc relationship can be used with a tolerance of 5 and 10% are provided whereas for humilis or mediocris cumuli, the presence of drizzle breaks down the Zc–Mc relationship whatever the situations. 相似文献
17.
Application of Radar Reflectivity Factor in Initializing Cloud-Resolving Mesoscale Model. Part Ⅰ: Retrieval of Microphysical Elements and Vertical Velocity 下载免费PDF全文
下载免费PDF全文 Assuming that cloud reaches static state in the warm microphysical processes, water vapor mixing ratio(qv), cloud water mixing ratio (qc), and vertical velocity (w) can be calculated from rain water mixing ratio (qr)- Through relation of Z-qr, qr can be retrieved by radar reflectivity factor (Z). Retrieval results indicate that the distributions of mixing ratios of vapor, cloud, rain, and vertical velocity are consistent with radar images, and the three-dimensional spatial structure of the convective cloud is presented. Treating q,v saturated at the echo area, the retrieved qr is about 0.1 g kg-1, qc is always less than 0.3 g kg-1, w is usually below 0.5 m s-1, and rain droplet terminal velocity (vr) is around 5.0 m s-1 in the place where radar reflectivity factor is about 25 dBz; in the place where echo is 45 dBz, the retrieved qr and qc are always about 3.0 g kg-1, w is greater than 5.0 m s-1, and vr is around 7.0 m s-1. In the vertical, the maximum updraft velocity is greater than 3.0 m s-1 at the height of around 5.0 kin, the maximum cloud water content is about 3.0 g kg-1 above 5 km and the maximum rain water content is about 3.0 g kg-1 below 6 kin. Due to the assumption that the cloud is in static state, there will be some errors in the retrieved variables within the clouds which axe rapidly growing or dying-out, and in such cases, more sophisticated radar data control technique will help to improve the retrieval results. 相似文献
18.
In this study, the vertical profiles of radar refractive factor (Z) observed with an X-band Doppler radar in Jurong on July 13, 2012 in different periods of a stratiform cloud precipitation process were simulated using the SimRAD software, and the contributions of each impact resulting in the bright band were analyzed quantitatively. In the simulation, the parameters inputted into SimRAD were updated until the output Z profile was nearly consistent with the observation. The input parameters were then deemed to reflect real conditions of the cloud and precipitation. The results showed that a wider (narrower) and brighter (darker) bright band corresponded to a larger (smaller) amount, wider (narrower) vertical distribution, and larger (smaller) mean diameter of melting particles in the melting layer. Besides this, radar reflectivity factors under the wider (narrower) melting layer were lager (smaller). This may be contributed to the adequate growth of larger rain drops in the upper melting layer. Sensitivity experiments of the generation of the radar bright band showed that a drastic increasing of the complex refractive index due to melting led to the largest impact, making the radar reflectivity factor increase by about 15 dBZ. Fragmentation of large particles was the second most important influence, making the value decrease by 10 dBZ. The collision–coalescence between melting particles, volumetric shrinking due to melting, and the falling speed of raindrops made the radar reflectivity factor change by about 3–7 dBZ. Shape transformation from spheres to oblate ellipsoids resulted in only a slight increase in the radar reflectivity factors (about 0.2 dBZ), which might be due to the fact that there are few large particles in stratiform cloud. 相似文献