基于2014～～2017年Ka毫米波雷达数据分析北京地区云宏观分布特征

本文利用2014年1月至2017年12月Ka毫米波雷达数据对北京地区云宏观特征进行统计分析。云出现率方面，4年平均值约36.3%；冬季最低，夏季最大；月出现率值9月最大，12月最小；出现率日变化有季节差异，春夏两季呈现中午（11:00，北京时间，下同）开始逐步升高至下午17:00后逐步下降的特点，增高幅度大于15%；冬、秋两季日变化特征不显著。高度方面，4年平均云底高约4.9 km，平均云顶高约7.2 km；云顶高和云底高的月变化特征明显，从年初1月开始逐步上升，在6月达到峰值，而后下降到12月达到低值；3～10月，高云（云底高>5 km)占约一半左右比例；厚度小于1 km的云在各月中所占比例最高；厚度1～4 km的云，厚度越大所占比例越低；特别地，厚度大于4 km的云所占比例在4～9月中仅次于厚度小于1 km云的比例。4年期间，北京地区单层云居多约占66.7%，两层云占比约25.2%，两层以上云占8.1%；冬季约80%的云为单层云，而6～9月云层分布变化最多，其中9月单层云比例最低约为40%。本文基于4年高时空分辨率雷达数据对北京地区云分布特征，特别是云垂直分布特征在数值上准确刻画，该项工作在已有云气候研究中尚未见开展，所获得的知识将对了解地区气候特征、区域模式云参数化选择提供参考。     

Automatic Identification of Clear-Air Echoes Based on Millimeter-wave Cloud Radar Measurements

Millimeter-wave cloud radar(MMCR) provides the capability of detecting the features of micro particles inside clouds and describing the internal microphysical structure of the clouds. Therefore, MMCR has been widely applied in cloud observations. However, due to the influence of non-meteorological factors such as insects, the cloud observations are often contaminated by non-meteorological echoes in the clear air, known as clear-air echoes. It is of great significance to automatically identify the clear-air echoes in order to extract effective meteorological information from the complex weather background. The characteristics of clear-air echoes are studied here by combining data from four devices: an MMCR, a laser-ceilometer, an L-band radiosonde, and an all-sky camera. In addition, a new algorithm, which includes feature extraction, feature selection, and classification, is proposed to achieve the automatic identification of clear-air echoes. The results show that the recognition algorithm is fairly satisfied in both simple and complex weather conditions.The recognition accuracy can reach up to 95.86% for the simple cases when cloud echoes and clear-air echoes are separate,and 88.38% for the complicated cases when low cloud echoes and clear-air echoes are mixed.     

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.     

毫米波雷达云回波的自动分类技术研究

毫米波雷达在云探测方面比厘米波天气雷达和激光雷达具有显著优势,可获得更多的云粒子信息,是研究云特性的主要遥感探测设备。为了开展对毫米波雷达探测的云回波进行自动分类的研究,利用161次云回波的个例数据,统计得到了卷云、高层云、高积云、层云、层积云和积云6类云型的特征量和其他参量的数值范围,利用分级的多参数阈值判别方法,达到了自动分类的目标,通过与人工分类的初步验证,两种分类结果的一致性达到84%,其中,层云和积云的识别一致较低的原因在于样本数据有限,仅有6次层云和8次积云的个例样本数据。通过更多样本的处理,提取的特征参量更可靠,自动分类的准确率会得到提高,以便将基于毫米波雷达的云分类技术应用于将来的云观测自动化业务。      

基于CloudSat云分类资料的华北地区云宏观特征分析

利用2007年1月至2008年12月的CloudSat 2B-CLDCLASS-LIDAR云分类资料对华北地区(36°~42°N,110°~120°E)各类云在单层及多层云中的出现频率、平均高度及平均厚度进行统计分析。结果表明:华北地区单层云和多层云出现频率存在明显的季节变化,夏季最大,春秋次之,冬季最小。单层云的出现频率远高于多层云,单层云出现频率在春、夏、秋、冬4个季节分别为44.3%、46.1%、37.8%和32.8%,而多层云中2层云所占比例最大。单层云和多层云各云层平均高度、平均厚度分析显示,3层云上层云顶云底高度最高,3层云下层云顶云底高度最低,单层云平均厚度明显大于多层云,云层数越多,各云层的平均厚度越小。对不同类型云出现频率分析显示,卷云主要出现在单层云及多层云中、上层,高层云和高积云在单层云和多层云各云层中均占有一定的比例,层云主要出现在多层云下层,层积云、积云、深对流云主要出现在单层云及多层云下层,雨层云主要出现在夏季单层云中。卷云、高层云、高积云的平均高度及厚度在不同云系统中存在显著的差异。     

基于雷达回波强度面积谱识别降水云类型

基于谱分析原理提出了雷达回波强度面积谱的概念及算法,利用宁夏银川多普勒天气雷达回波资料,分析了不同性质降水云的雷达回波强度面积谱,并根据不同性质降水云雷达回波强度面积谱特征,提出了基于雷达回波强度面积谱识别降水云类型的方法,利用强回波面积（回波强度不小于40 dBZ的回波面积）占总回波面积百分比和基本降水回波面积（回波强度不小于20 dBZ的回波面积）占总回波面积百分比作为降水云类型判别的主要因子,提炼出基于雷达回波强度面积谱特征参数的层状云、积层混合云、对流云等不同类型降水云的判别指标,建立了基于雷达回波的降水云类型自动判识模型。利用该模型对2016-2017年6次强降水过程进行了降水云类型判别试验,模型准确判别出6次强降水过程中2次为对流云降水、4次为混合云降水,判别结果较好地反映了降水云类型,验证了判识方法的可行性。     

Ka波段固态发射机体制云雷达和激光云高仪探测青藏高原夏季云底能力和效果对比分析

激光云高仪和云雷达是探测云底的两种设备,但其探测能力和探测结果有一定的差异,对比分析两种设备的测云效果有助于正确认识它们的探测优势,推进我国云雷达在云探测中的应用。本文提出了基于云雷达数据的云底和云顶高度分析方法,利用2014年夏季第三次青藏高原大气科学试验云雷达、激光雷达和激光云高仪数据,统计了三种设备探测青藏高原低云、中云和高云的云底高度偏差、探测率,分析了激光云高仪探测云底偏高的原因,根据探测结果提出了固态发射机体制雷达探测青藏高原低云的优化观测模式,模拟分析了探测效果。结果表明:（1）云雷达对高云的探测能力要明显优于激光云高仪,但其对低云的探测能力有待改进,激光云高仪探测云底下部的边界层内的云雷达回波信号可能是非云降水回波;低层云的遮挡作用明显降低了激光云高仪对多层云的观测能力;与激光云高仪相比,云雷达仍然会漏掉一些高云和中云。（2）激光云高仪探测的中云和高云的云底很多在云雷达回波内部,云雷达和激光云高仪观测的云底的时空对应关系比较差。（3）增大激光发射功率和优化固态发射机体制云雷达观测模式可提高云的观测能力,微波和激光雷达数据融合可全面了解不同类型云的宏观特征。这一工作为云雷达和激光雷达数据的应用,评估激光云高仪和云雷达探测青藏高原云的能力,讨论设计优化的云观测方案,为推进我国云观测技术的发展提供了重要参考依据。     

新疆博湖地区大气向下红外辐射特性及其对云识别应用的研究

为加强新疆戈壁地区云的自动观测,利用辐射传输模式SBDART和新疆博湖地区历史气候资料,分析了当地大气向下红外辐射对大气影响因子的敏感性和利用全天空红外辐射进行云识别分类的可行性,并提出了一种基于典型云族红外阈值的云分类方法。结果表明：（1）水汽、云、天顶角、能见度等对中红外波段向下红外辐射值都有较大影响;（2）当能见度较差时,气溶胶对向下红外辐射的贡献不可忽略,并由于局地气溶胶辐射特性难以确定,因此是利用天空向下红外辐射进行云识别时的一个不确定因素;（3）根据8～14μm波段不同云族的辐射特性差异分析,阈值法是进行云识别和云族区分的有效方法。但该方法应用效果的好坏依赖于实测探空数据和历史气候数据的有效性及准确性。     

Cloud Base Height and Effective Cloud Emissivity Retrieval with Ground-Based Infrared Interferometer

Based on ground-based Atmospheric Emitted Radiance Interferometer (AERI) observations in Shouxian, Anhui province, China, the authors retrieve the cloud base height (CBH) and effective cloud emissivity by using the minimum root-mean-square difference method. This method was originally developed for satellite remote sensing. The high-temporal-resolution retrieval results can depict the trivial variations of the zenith clouds continu-ously. The retrieval results are evaluated by comparing them with observations by the cloud radar. The comparison shows that the retrieval bias is smaller for the middle and low cloud, especially for the opaque cloud. When two layers of clouds exist, the retrieval results reflect the weighting radiative contribution of the multi-layer cloud. The retrieval accuracy is affected by uncertainties of the AERI radiances and sounding profiles, in which the role of uncertainty in the temperature profile is dominant.     

Observational Characteristics of Cloud Vertical Profiles over the Continent of East Asia from the CloudSat Data

The CloudSat satellite data from June 2006 to April 2011 are used to investigate the characteristics of cloud vertical profiles over East Asia(20°-50°N,80°-120°E),with particular emphasis on the profiles of precipitative clouds in comparison with those of nonprecipitative clouds,as well as the seasonal variations of these profiles.There are some obvious differences between the precipitative and nonprecipitative cloud profiles.Generally,precipitative clouds mainly locate below 8 km with radar reflectivity in the range of-20 to 15 dBZ and maximum values appearing within 2-4-km height,and the clouds usually reach the ground;while nonprecipitative clouds locate in the layers of 4-12 km with radar reflectivity between-28 and 0 dBZ and maximum values within 8-10-km height.There are also some differences among the liquid precipitative,solid precipitative,and possible drizzle precipitative cloud profiles.In precipitative clouds,radar reflectivity increases rapidly from 11 to 7 km in vertical,implying that condensation and collision-coalescence processes play a crucial role in the formation of large-size drops.The frequency distribution of temperature at-15℃ is consistent with the highest frequency of radar reflectivity in solid precipitative clouds,which suggests that the temperatures near-15℃ are conductive to deposition and accretion processes.The vertical profiles of liquid precipitative clouds show almost the same distributions in spring,summer,and autumn but with differences in winter at mainly lower levels.In contrast,the vertical profiles of solid precipitative clouds change from spring to winter with an alternate double and single high-frequency core,which is consistent with variations of the frequency distribution of temperature at-15℃.The vertical profiles of nonprecipitative clouds show a little change with season.The observations also show that the precipitation events over East Asia are mostly related to deep convective clouds and nimbostratus clouds.These results are expected to be useful for evaluation of weather and climate models and for improvement of microphysical parameterizations in numerical models.     

青藏高原那曲夏季云中水成物分布特征的毫米波雷达观测

结合2014年7—8月第三次青藏高原大气科学试验获得的毫米波雷达资料与探空温度资料,利用模糊逻辑法反演了西藏那曲地区夏季云中水成物的相态并对其分布特征开展了研究。首先,分析了层积云、雨层云以及深对流云的典型个例,发现三类云反射率因子、多普勒速度、速度谱宽以及退偏振因子垂直分布均有较大差别,相应的云中水凝物的回波特征与相态分布差别也较大。其次,研究了液相、混合相和冰相云层的云雷达探测特征,发现液相云层在0℃层以下的暖云层和0℃层以上的过冷水云层均具有反射率因子高值中心,混合云层的反射率因子高值中心随高度上升变化不大,冰云层的反射率因子高值主要集中在6 km以上,且随高度上升而趋于集中;三种相态云层出现频率高值分别集中在地面以上1、2～3、3～4 km高度层;液相云层在上午出现频率最高,混合相云层高频率发生在下午,冰相云层在晚上的出现频率最高。三种相态云层出现在上午的高度与下午和晚上相比较低,出现在晚上的高度范围最大;液相云层厚度一般小于0.3 km,冰相云层云顶位于9 km左右高度层时平均厚度最大,中云内的混合相和冰相厚度变化较小。     

Cloud cover classification through simultaneous ground-based measurements of solar and infrared radiation

Simultaneous measurements of downwelling short-wave solar irradiance and incoming total radiation flux were performed at the Reeves Nevè glacier station (1200 m MSL) in Antarctica on 41 days from late November 1994 to early January 1995, employing the upward sensors of an albedometer and a pyrradiometer. The downwelling short-wave radiation measurements were analysed following the Duchon and O'Malley [J. Appl. Meteorol. 38 (1999) 132] procedure for classifying clouds, using the 50-min running mean values of standard deviation and the ratio of scaled observed to scaled clear-sky irradiance. Comparing these measurements with the Duchon and O'Malley rectangular boundaries and the local human observations of clouds collected on 17 days of the campaign, we found that the Duchon and O'Malley classification method obtained a success rate of 93% for cirrus and only 25% for cumulus. New decision criteria were established for some polar cloud classes providing success rates of 94% for cirrus, 67% for cirrostratus and altostratus, and 33% for cumulus and altocumulus.The ratios of the downwelling short-wave irradiance measured for cloudy-sky conditions to that calculated for clear-sky conditions were analysed in terms of the Kasten and Czeplak [Sol. Energy 24 (1980) 177] formula together with simultaneous human observations of cloudiness, to determine the empirical relationship curves providing reliable estimates of cloudiness for each of the three above-mentioned cloud classes. Using these cloudiness estimates, the downwelling long-wave radiation measurements (obtained as differences between the downward fluxes of total and short-wave radiation) were examined to evaluate the downwelling long-wave radiation flux normalised to totally overcast sky conditions. Calculations of the long-wave radiation flux were performed with the MODTRAN 3.7 code [Kneizys, F.X., Abreu, L.W., Anderson, G.P., Chetwynd, J.H., Shettle, E.P., Berk, A., Bernstein, L.S., Robertson, D.C., Acharya, P., Rothman, L.S., Selby, J.E.A., Gallery, W.O., Clough, S.A., 1996. In: Abreu, L.W., Anderson, G.P. (Eds.), The MODTRAN 2/3 Report and LOWTRAN 7 MODEL. Contract F19628-91-C.0132, Phillips Laboratory, Geophysics Directorate, PL/GPOS, Hanscom AFB, MA, 261 pp.] for both clear-sky and cloudy-sky conditions, considering various cloud types characterised by different cloud base altitudes and vertical thicknesses. From these evaluations, best-fit curves of the downwelling long-wave radiation flux were defined as a function of the cloud base height for the three polar cloud classes. Using these relationship curves, average estimates of the cloud base height were obtained from the three corresponding sub-sets of long-wave radiation measurements. The relative frequency histograms of the cloud base height defined by examining these three sub-sets were found to present median values of 4.7, 1.7 and 3.6 km for cirrus, cirrostratus/altostratus and cumulus/altocumulus, respectively, while median values of 6.5, 1.8 and 2.9 km were correspondingly determined by analysing only the measurements taken together with simultaneous cloud observations.     

物理-统计反演和云-晴空辐射特征差异检测结合的全天空高云判别能力改进算法与初步试验

在全天空红外-可见光云像观测反演方法基础上,针对已往阈值法判断高云的局限性,提出改进的算法。对可见光图像可利用以太阳-天顶为连线的主平面两边相元特性的对称性来判断云的存在,对红外成像仪可利用同一仰角扫描时所得不同方位角亮温分布的非均匀性来判断云的存在。以北京2011年1 3月观测资料为基础,选择原阈值法未能判别出来的案例进行再处理,全天空可见光成像观测仪高云判别能力最高可达73.9%,全天空扫描式红外成像仪可达70.1%。该工作为天空的高云识别提供新的复合算法。     

利用CloudSat/CALIPSO卫星资料分析北半球污染排放区云型频率分布特征北大核心

本文利用A-Train卫星队列中的Cloudsat卫星所提供的二级云分类产品资料(结合了CALIPSO卫星气溶胶激光雷达)2B-CLDCLASS-LIDAR,选取2007年3月至2017年2月的样本数据进行统计分析,研究北半球主要的气溶胶排放源区(中国东部,美国东部和欧洲西部)不同云型出现频率的分布特征。结果表明,在以单层云出现的8种云类中卷云,层积云和积云的发生频率总和高达50%~70%,其次为高层云、高积云和雨层云,而深对流云和层云这两种云仅占10%以下。各类云的发生频率的空间分布可看出卷云和层积云的发生频率可高达90%以上,高层云的发生频率在70%~80%左右,高积云和积云的发生频率则接近70%以上,深对流云和层云的发生频率则在20%以下。其中,卷云、深对流云和积云主要出现在低纬度的海洋上;高层云和高积云主要出现在中低纬工业发达的陆地上;层积云、层云和雨层云主要出现在中高纬地区,其中层积云和层云出现在海洋上居多,雨层云出现频次的海陆差别不大。不同云型在不同的季节出现频次差异明显,在夏季出现较多的云型以卷云、深对流云,积云和层云为主;在冬季则是高层云、层积云和雨层云这样稳定型的云型占据主导,同时还发现卷云和层积云发生频率的月变化相反,而高层云和雨层云发生频率的月变化相似。     

利用SCIT算法分析江淮地区对流云特征

本文设计了一种运用SCIT算法提取雷达拼图中对流云特征量的方法:用SCIT算法识别并追踪云体,算法中只用一个反射率阈值识别对流单体、云体边界;使用模糊逻辑法区分层状云和对流云,提取对流云雷达特征量。使用该方法提取了2013—2015年江淮地区夏季对流云的雷达特征量,并对雷达特征量与降水量分布的关系进行统计分析。结果表明:江淮地区夏季对流云中生命史为13～30 min的对流云比较多,约占总数的60%;生命史大于30 min的对流云较少,并具有平均回波强度、VIL、冷层厚度较大的特征,其最大回波强度和VIL与降水量的相关性较好。     

基于探空云识别方法的云垂直结构分布特征

云的垂直结构特征作为云重要的宏观特征之一,直接决定了云的类型,进而通过发射和吸收辐射的方式影响着地气系统的能量收支平衡,因此对云垂直结构特征的研究一直都是云物理研究的一个重要方向。作为观测云垂直结构特征的一种方式,探空气球通过获取沿路径方向高分辨率的廓线信息,采用一定反演方法从而能够较为准确的识别云的垂直结构。本文即利用我国业务布网探空站的观测资料,采用相对湿度阈值法识别云垂直结构,并同激光云高仪、“风云四号”静止卫星和毫米波云雷达对识别的云结构特征量进行了一致性检验。在此基础上,统计分析了2015～2017年单层、两层和三层云的垂直结构分布特征、日变化和季节变化特征以及全国区域分布特征,结果表明:（1）整体分布上,单层云在垂直方向上出现的高度范围介于多层云的高度范围内,并且随着云层数的增加,云在垂直方向上更为伸展,即高层云越高,低层云越低;（2）在日变化中,中午单层和多层云中最低层云的云底高度均高于早晨,而夜间单层和多层云中最高层云的云顶高度则高于早晨和中午,同时中间层云厚的变化要小于最上层和最下层云厚的变化;（3）在季节变化中,夏季云量较其他季节更多,云体发展也更为深厚,表明温暖的大气条件更有利于云的形成和发展;（4）我国云垂直结构分布特征具有明显的纬向变化趋势,从以青藏高原为中心的西南地区的云底较高云体较薄的云,逐步过渡到以东南沿海地区为中心的云底较低云体较为深厚的云,表明不同地形和气候带的差异与不同云类型的分布直接相关。     

基于模糊纹理光谱的全天空红外图像云分类

为了对全天空红外测云系统获得的红外图像进行云类自动识别, 提出了基于模糊纹理光谱结合云物理属性的全天空云类识别方法。首先根据不同滤波窗口的模糊纹理光谱图像特征, 确定了滤波窗口大小, 然后通过分析不同天空类型下的FUTS谱 (fuzzy uncertainty texture spectrum) 以及同一种天空类型下的FUTS谱, 考察了FUTS进行云类识别的适用性, 最后利用最小距离分类法和云基本物理属性对全天空红外图像进行了分类测试。在200个测试样本中, 层状云、积云、高积云、卷云和晴空的识别率分别为100%, 100%, 90%, 100%, 100%, 平均识别率达到98%。基于模糊纹理光谱的云分类算法对单一云空具有很好的分类效果, 可进一步应用于全天空红外图像的云分类识别。     

青藏高原墨脱地区云降水综合观测及初步统计特征分析

为加深对雅鲁藏布大峡谷水汽通道入口处云和降水三维结构及微物理特征的认识,在第二次青藏高原综合科学考察研究专题和国家重点研发计划项目的支持下,中国气象科学研究院于2019年在西藏墨脱地区建立了野外观测试验基地,开展了水汽、云和降水的综合观测,先后布设了先进的Ka波段云雷达、微波辐射计、X波段双偏振相控阵雷达、降水现象仪、K波段微雨雷达等设备,获取了高时、空分辨率的云和降水的宏、微观数据。文中简单介绍了此次观测的情况,并利用云雷达2019年的观测数据和降水现象仪2019年6月至2020年6月的观测数据对云的宏观特征及雨滴谱特征进行了统计分析。从云的宏观特征来看,该地区云的发生率较高,云廓线占2019年云雷达观测廓线的67%,降水云廓线占总云廓线的45%。旱季和雨季云底高度的频率分布在垂直方向均有两个高值区,分别为0—1 km和2—3 km,且超过40%的云底高度低于1 km,这可能是墨脱降水云较多造成的。接近60%的云顶高度在4—7 km。总的来说,墨脱地区以中云和低云为主,云通常在下午到晚上形成,早上到中午慢慢消散;从雨滴谱分布特征来看,该地区平均的雨滴谱谱宽和大雨滴的浓度随雨强的增大而增大,降水以中、小粒子为主,中、小粒子浓度超过粒子数浓度的99%。对流云降水的特点是粒子直径较小,而数浓度较高。粒子质量加权平均直径（D_m）的范围在1.0—1.6 mm（平均1.38 mm）,标准化截距参数（lgN_w）的范围在3.6—4.5（平均4.01）,表现出海洋性对流降水的特征。此外,该地区降水的lgN_w呈现双峰特征,分别对应于对流云和层状云降水。      

基于Ka波段云雷达的六盘山顶云特征分析

利用宁夏六盘山气象站2017年9月至2018年8月的Ka波段云雷达观测资料,统计分析了六盘山顶不同云的出现频率及宏观特征。结果表明：六盘山顶云出现频率最高值在7月,为61%,最低值在12月,为26%;按云层数划分,六盘山顶出现的云主要以1层云、2层云及3层云为主,相对总云的月平均出现频率分别为68%—86%、14%—27%及0.4%—4.8%;按云底高度及云层厚度划分,六盘山顶低云、中云、高云及直展云相对总云的月平均出现频率分别为29%—53%、14%—58%、6%—22%及2%—20%。云底高度在冬春季节高于夏秋季节,云顶高度在夏秋季节高于冬春季节,云层厚度为1.6—3.6 km,年变化特征与云顶高度类似。整体来看,春、夏、秋季云厚在白天大于夜间,冬季云厚在夜间大于白天,其中夏、秋季云厚日变化特征较为明显。     

基于红外实时阈值的全天空云量观测

红外测温传感器在旋转平台控制下定时对全天空进行扫描,拼接全天空红外辐射亮温图像。利用天空中的云点与非云点在红外波段中表现出的不同特性,考虑不同仰角方向天空中云点与非云点的温度差异,结合地面环境参数,实时拟合天顶到水平区间内晴空时刻的温度阈值函数,利用阈值分割方式得出全天空云分布及云量信息。该方法可以有效减少地面环境参数及太阳光照对云图的影响,能够全天实时运行。将利用该方法获取的数据分别与人工观测数据及典型天气条件下可见光测云结果进行对比,结果表明该系统在云量观测方面具有一定的先进性和准确性。