The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Macrophysical properties of specific cloud types from radiosonde and surface active remote sensing measurements over the ARM Southern Great Plains site

Accurate observation of clouds is challenging because of the high variability and complexity of cloud types and occurrences.By using the long-term cloud data collected during the ARM program at the Southern Great Plains central facility during 2001-2010,the consistencies and differences in the macrophysical properties of clouds between radiosonde and ground-based active remote sensing are quantitatively evaluated according to six cloud types：low;mid-low（ML）;high-midlow;mid;high-mid（HM）;and high.A similar variability trend is exhibited by the radiosonde and surface observations for the cloud fractions of the six cloud types.However,the magnitudes of the differences between the two methods are different among the six cloud types,with the largest difference seen in the high clouds.The distribution of the cloud-base height of the ML,mid,and HM clouds agrees in both methods,whereas large differences are seen in the cloud-top height for the ML and high clouds.The cloud thickness variations generally agree between the two datasets for the six cloud types.

Characterization of cloud microphysical properties in different cloud types over East Asia based on CloudSat/CALIPSO satellite products

By using four-year CloudSat/CALIPSO satellite data,the authors investigated cloud microphysical properties in three representative regions over East Asia,where models commonly suffer from great biases in simulations of cloud radiative effects.This study aims to provide an observational basis of cloud microphysical properties for the modeling community,against which the model simulations can be validated.The analyzed cloud microphysical properties include mass,number concentration,and effective radius for both liquid and ice phases.For liquid clouds,both cloud mass and number concentration gradually decrease with height,leading to the effective radius being nearly uniformly spread in the range of 8-14μm.For ice clouds,the cloud mass and effective radius decrease with height,whereas the number concentration is nearly uniform in the vertical.The cloud microphysical properties show remarkable differences among different cloud types.Cloud mass and number concentration are larger in cumuliform clouds,whereas smaller in cirrus clouds.By comparing cloud properties among the Tibetan Plateau,East China,and the western North Pacific,results show the values are overall smaller for liquid clouds but larger for ice clouds over the Tibetan Plateau.

Cloud Classification and Distribution of Cloud Types in Beijing Using Ka-Band Radar Data

A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite

Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Cloud type identification for a landfalling typhoon based on millimeter-wave radar range-height-indicator data

As a basic property of cloud,accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons.Millimeter-wave cloud radar is an important means of identifying cloud type.Here,we develop a fuzzy logic algorithm that depends on radar range-height-indicator(RHI)data and takes into account the fundamental physical features of different cloud types.The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar.The input parameters of the algorithm include average reflectivity factor intensity,ellipse long axis orientation,cloud base height,cloud thickness,presence/absence of precipitation,ratio of horizontal extent to vertical extent,maximum echo intensity,and standard variance of intensities.The identified cloud types are stratus(St),stratocumulus(Sc),cumulus(Cu),cumulonimbus(Cb),nimbostratus(Ns),altostratus(As),altocumulus(Ac)and high cloud.The cloud types identified using the algorithm are in good agreement with those identified by a human observer.As a case study,the algorithm was applied to typhoon Khanun(1720),which made landfall in south-eastern China in October 2017.Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.

Temporal and spatial variation of cloud cover in arid regions of Central Asia in the last 40 years

Water resources are one of the key factors restricting the development of arid areas,and cloud water resources is an important part of water resources.The arid region of central Asia is the core region of the current national green silk road construction,and is the largest arid region in the world.Based on cloud cover data of ECMWF,the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019.Our findings show that:(1)From the point of view of spatial distribution,total cloudiness in arid regions of Central Asia was low in the south and high in the north.The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north,while low cloud frequency distribution was low in the south and high in the north.(2)In terms of time,the variation of cloud cover and cloud type frequency had obvious seasonal characteristics.From winter to spring,cloud cover increased,and the change of cloud type frequency increased.From spring to summer,cloud cover continued to increase and the change of cloud type frequency increased further.Cloud cover began to decrease from summer to autumn,and the change of cloud type frequency also decreased.(3)Generally,average total cloud cover decreased in most of central Asia,and high and medium cloud cover increased while low cloud cover decreased.This study provides a reference for the rational development of cloud resources in the region.

新型电力系统面向云边端架构的安全访问控制技术研究

新型电力系统的建设正朝着云边端一体化方向发展,云边端架构带来灵活性和可扩展性的同时,也带来了数据隐私安全、非法操作、缺乏标准化集成方案等问题。基于此,文章提出一种结合云边端架构特点的基于属性的安全访问控制方案(cloud-edge-device attribute-based access control,CED-ABAC),方案通过边缘融合终端进行重加密,既保护数据安全,又减轻终端设备的通信开销,在策略授权方面,使用可扩展的访问控制标记语言(extensible access control markup language,XACML)设计授权策略和策略匹配算法,实现对多终端访问控制策略的同时下发,更高效地解决标准化集成问题。同时通过实验仿真,证明CED-ABAC方案的效率和性能相比于已有方案具有明显优势。

西北太平洋主要云类型光学厚度的时空分布特征

本文利用CloudSat数据处理中心发布的二级云分类产品和云光学厚度产品,对西北太平洋上空主要云类型光学厚度的空间分布和季节变化特征进行了系统研究。使用云分类产品数据获得了西北太平洋上8类云的云量时空分布,以此为依据选择出云量较高的5类主要云。将二级云光学厚度产品中的数据与5类主要云类型进行匹配,获得了西北太平洋上主要云类型的平均光学厚度的空间分布及其季节变化特征。结果表明:西北太平洋上主要云类型的平均光学厚度分别为,高云约3~9,高层云约13~25,高积云约5~25,层积云约5~40,积云约5~30。各类云的光学厚度高值区主要分布在高纬度海域以及大洋西北尤其中国东部沿海海域,光学厚度低值主要分布在低纬度海域及信风带附近。此外,各类云的光学厚度在低纬度海域上季节变化幅度小,高纬度海域变化幅度大。以上结果可用于分析该区域不同地点及季节跨介质海洋激光通信系统的可通率,也可用于评估该区域可见光对海观测系统的探测能力等。

基于CloudSat资料的中国及周边地区各类云的宏观特征分析

利用2006年7月—2009年4月的CloudSat2B-CLDCLASS云分类资料,针对中国及周边地区(0°—60°N,70°—140°E)各类云量和垂直结构参数的地理分布及季节变化进行了统计分析,并根据气候特征的地域差异从该区域选出8个子区域,逐区统计了各类云的垂直结构特征。结果表明,各类云量的分布存在较明显的区域差异和季节变化;青藏高原和帕米尔高原地区卷云、高层云和高积云等中高云的高度和厚度相对较小,陆上深对流云的云底高度大于海上,而热带、副热带地区云顶高度大于中纬度地区;除积云、层积云和雨层云外,中国南方地区其他各类云的云层厚度均大于北方地区;除了层积云外,其他各类云的云顶高度在各区域都存在比较明显的季节变化,低云云底高度的季节变化和区域差异都很小,而中高云的云底高度除了在印度洋季风区、南海和西太平洋地区季节差异较小外,其他地区季节差异较明显,各个地区在任何季节内,深对流云厚度最大,层积云最小;各类云出现频率随高度的分布具有较明显的区域差异;卷云与高积云的相关性比较强,经常相伴出现,夏季更加明显,而雨层云和深对流云之间相互排斥,两者几乎不可能同时出现。此外,统计中国及周边地区各类云的水平均一性发现,中高云的云底高度水平均一性较低云差,各类云中深对流云的云顶高度水平均一性最差,除了卷云和高积云的云顶高度水平均一性同其云底高度相当外,其他各类云的云顶高度水平均一性均比云底高度均一性差。

结合注意力机制的深度学习葵花8号影像云相态识别方法研究

云相态的准确分类对地气辐射收支系统及云光学性质等方面的研究至关重要。传统的云相态分类方法利用不同种类云所具有的光谱特征差异,这些分类算法大多仅在白天场景下进行,并且存在数据集相对匮乏的问题。以2016年1—8月地球同步卫星葵花8号的多波段辐射信息作为研究对象,以主动观测极轨卫星CloudSat以及CALIPSO合并的云相态分类产品作为参考标签,建立嵌入自注意力机制的云相态分类深度神经网络(DNN),实现白天/夜间场景下的“冰云”“水云”“混合相云”的云相态分类判定和输出。实验结果表明,夜间模型平均精确率约79%,白天模型平均精确率约85%;引入自注意力机制提升云相态检测模型的性能,添加两层注意力层的白天模型和夜间模型,相比未添加注意力层的模型,F 1分数均提高约0.04。

Verification and Correction of Cloud Base and Top Height Retrievals from Ka–band Cloud Radar in Boseong,Korea

In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.

播撒碘化银人工消雹对冰雹云微物理过程和起放电过程影响的数值模拟研究

为了全面了解人工消雹对云中冰相粒子的浓度和时空变化以及冰雹云微物理过程和起放电的影响,本文利用包含了AgI催化方案的三维强风暴动力电耦合数值模式,以AgI播撒量为唯一变化量进行了多组模拟实验。结果表明,催化过后,整体上,-40~0℃范围内的冰晶、雪花和霰粒子数浓度和比含水量增多。播撒AgI使霰的平均尺度减小,向雹的转化比例大为降低,因此冰雹的比含水量和浓度都减小。随着AgI播撒剂量的增加,冰晶、雪花和霰粒子的数浓度和比含量整体变化趋势不变,增大与减小区域相对应,但变化更明显;消雹效果会更好,但降雨量呈先增加后减小。播撒AgI会使冰雹云前期正的非感应起电率区出现时间提前,中心增强,上界高度抬升,后期负的非感应起电率区中心增强。所以,播撒后前期主要使底部次正电荷区增强,后期主要使上方主正电荷区和中部负电荷区增强。随着催化剂量增多,这些变化越明显。播撒AgI对地闪有一定的抑制作用,而使云闪和总闪少量增多,首次放电和云闪的峰值时间提前,放电持续性更强,闪电频发时段的闪电频率略有降低。

基于L波段探空观测的FY-4A云顶温度产品评估及其在冬季降水相态判识中的应用

利用2019—2021年冬季上海宝山站L波段探空资料对FY-4A云顶温度(CTT)产品进行评估,分析发现,FY-4A的云顶温度产品能够较好地反映单层云的云顶温度,对双层云或多层云的云顶温度则普遍高估(平均高估幅度超过14℃);当探空观测到的云顶高度不超过6 km或云顶温度不低于-20℃时,FY-4A云顶温度产品误差较小,平均偏差约为3℃。根据2021—2022年冬季长三角地区地面站观测的降水现象统计云顶温度与降水相态的关系,并对典型个例进行分析,结果表明,云顶温度低是出现降雪的必要条件之一,绝大部分降雪出现在云顶温度低于-12℃的情况下。FY-4A的云顶温度产品时空分辨率高、精度尚可,可以辅助预报员判识雨雪落区,在雨雪转换过程的预报服务中有较大的应用潜力。

级联边缘卷积与注意力机制的点云分类分割研究

近几年点云的分类分割研究多采用多层次架构提取点云特征的方法,提取到较为稳定的高层语义特征,但是全局特征和邻域特征提取不足并且缺乏对上下文信息的特征融合。因此,提出一种新的LAM-EdgeCNN网络,采用边缘卷积与注意力机制级联的方式对点云进行多层级特征提取,获取高层次特征信息。为了加强对特定通道特征和关键空间点的捕捉,提出一种轻量型LAM注意力机制,使用CAM特征通道注意力获取各通道的关联,定位关键通道特征的捕获,使网络更加关注特定通道特征以减少信息弥散和特征冗余;引入SAM空间注意力机制获取点空间的位置信息的注意力权重,增加获得浅层信息的细粒度。采用注意力机制与边缘卷积EdgeConv相结合的方式,增强上下文感知能力,充分提取和融合点云的局部特征和上下文特征,获得面向下游任务的点云特征。将模型应用于公开数据集,实验表明,模型在点云分类、部件分割、语义分割任务中取得良好效果且具有较好鲁棒性。

一种场磨式三维大气电场传感器

针对市面上电场传感器只能探测一维电场垂直分量且电场测量结果存在一定的误差,为了更加精确地测量大气电场强度,提高雷暴云预警的准确率,提出一种场磨式三维大气电场传感器,传感器主要结构由屏蔽罩、感应电极、光电开关、叶片和电机组成,电路设计主要包括I-V转换电路、差分放大电路、峰值检测电路和滤波电路等,进行传感器实地电场测量实验并整理实验数据;实验结果表明,该传感器能够对环境电场进行水平和垂直分量测量,有效反映了环境电场特征,验证了场磨式三维大气电场传感器结构设计和电路设计的有效性。

C型料场刮板机取料速度控制方法与系统开发

在C型料场刮板机自动化作业过程中,取料过快经常导致刮板机跳闸故障,取料过慢会导致供料不足、效率低等问题。对此,本文提出一种基于点云模型的C型料场刮板机取料速度控制方法,并在某C型料场现场试验验证,该控制方法步骤:(1)获取料堆点云数据和形状,将其转换为高度灰阶图,并采用一种新型点云过滤算法降低其噪声、提高平滑度;(2)基于刮板机链板恒定条件建立物料流量模型,结合能量转换构建刮板机电流-速度模型,将模型结果实时写入刮板机PLC中,进而实时控制刮板机取料速度。结果表明:使用该模型,料堆点云模型的内存资源降低98.64%,点与点之间形成有效的拓扑关系,计算的复杂度降低,料堆点云模型的平滑度显著提高;刮板机作业时料流的流量波动在8.29%内,刮板机的设备故障率减少5.24%,生产效率提高9.68%。本文提出的取料速度控制方法可用于替代人工调节刮板机的取料速度,减少刮板机的作业故障,提高生产效率,有效提高设备的无人化、智能化水平,并可为各种刮板机取料速度控制与作业稳定性控制提供参考。

Observation of clouds and solar radiation over the Pacific Ocean as relation to global climate

During a research cruise over the Pacific Ocean in 1989, solar irradiance was measured with a broad-band pyranometer along the cruise track. Cloud cover was photographed with an all-sky time-lapse came ra. Cloud types were observed and recorded. The data show that both the types and the amounts of clouds affect radiation fluxes on the sea surface. For low-level and middle-level clouds, the correlations (r) between measured irradiance (in Percent of calculated maximum irradiance) and cloud amount (in fraction of sky) were significant: r=-0. 79 and - 0. 66, respectively. For high-level clouds, the correlation was not significant: r=-0. 21. The results indicate that cloud shortwave forcing is a major modifier of the earth's surface insolation and change of cloud amount may affect global climate.