基于RS92探空资料的云垂直结构判定及其分布研究

基于已有的云垂直结构判断方法,结合高垂直分辨探空资料,对相对湿度阈值法进行相应改进,并利用德比尔特地区2007年全年的RS92探空资料,对该地区的云垂直结构进行判定,同时对该地区云出现频率及云垂直结构参数的分布特征进行统计分析。结果表明:利用改进相对湿度阈值法来分析高分辨探空资料的云垂直结构是可行的;该地区的云层,以单层云和双层云为主,且单层云出现频率呈“冬高夏低”分布,双层云则与其相反;其所有云平均云底高、云顶高、云厚度、顶层云顶高的出现频率,随高度的增加而减小。     

利用EOS/MODIS数据反演水云云底高度的初步研究

云底高度作为重要的云宏观物理特征参数,在云层与地表之间的能量交换中起着重要作用。传统的云底高度测量方法大多基于常规观测资料,利用星载被动遥感仪器的观测数据反演云底高度在国内尚未开展。论述了基于EOS/MODIS可见光、红外数据反演云底高度的原理、方法和可行性,并结合西北某空域的飞机探测数据进行了MODIS水云云底高度反演的对比试验。初步结果表明：利用MODIS数据反演水云的云底高度是可行的;在与3次飞机穿云记录的云高真实数据对比中,反演结果平均误差为249.4 m。     

Observational study of cloud base height and its frequency over a tropical station,Thiruvananthapuram, using a ceilometer

This study aims to investigate the characteristic features of cloud base height (CBH) over Thiruvananthapuram during different seasons. CBH data were used for the present work derived from the Vaisala Laser Ceilometer, CL31 (VLC) installed at the campus of the Centre for Earth Science Studies, Akkulam (8.29° N, 76.59° E, 15 m above sea level). The VLC was in operation from the second week of July 2006 onwards. From the study, we found that CBH shows distinct diurnal and seasonal variations during all the seasons (except on rainy days). The diurnal variation for low-level clouds was different from that for the mid-level clouds. A cloud-free layer is evident in the region between 2.5 and 4 km. This cloud-free zone is more prominent during the southwest monsoon period compared to other seasons. Moreover, the monthly variations of cloud frequency and CBH were also described in addition to the different periodicities in cloud frequency. The periodicities found in the cloud frequency were 8 days and 30 days and these are significant at the 5% level. Thermodynamic parameters from the radiosonde were also related to the cloud frequency for various seasons and they were in good agreement.     

Assessing VIIRS cloud base height products with data collected at the Department of Energy Atmospheric Radiation Measurement sites

A system-level analysis has been completed to assess the accuracy of cloud base height (CBH) products of lower-altitude water clouds created from Visible Infrared Imaging Radiometer Suite (VIIRS) data collected by the National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS). While the analysis focused on the CBH products, it necessarily included an assessment of other key, upstream VIIRS cloud products, including VIIRS cloud top height (CTH), VIIRS cloud optical thickness (COT), and VIIRS cloud effective particle size (EPS). The COT and EPS products are used to derive a geometric cloud thickness, which is then subtracted from the CTH product to generate the CBH product. Thus, a system-level analysis is critical to establishing the detailed algorithm error budget needed to identify the major sources of errors in the CBH product and help focus future research efforts to improve this important cloud product. As a consequence, ground-based data collected at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites became most useful for conducting this system-level analysis. Therefore, match-up data sets were created between VIIRS cloud products and DOE ARM site data sets from June 2013 to October 2015, and while the primary focus was on the highest-quality data at the Lamont, OK ARM site, data were analysed from a total of four ARM sites. The initial results showed the errors in the VIIRS CBH products, compared to the ARM site data sets, to be large and highly variable; however, errors in VIIRS COT and the derived VIIRS cloud geometric thickness were much smaller. Consequently, the VIIRS CTH products were replaced with the ARM CTH products, which substantially reduced the variability and errors in the VIIRS CBH products. It is concluded that the performance of the VIIRS CBH products is most strongly correlated with errors in the VIIRS CTH products, while errors in COT and geometric cloud thicknesses are acceptable. Thus, future research is needed to reduce the errors in the VIIRS CTH products in order to ensure the VIIRS CBH products are suitable for civilian and military aerodrome operations.     

局部聚类分析的FCN-CNN云图分割方法

空气中的尘埃、污染物及气溶胶粒子的存在严重影响了大气预测的有效性,毫米波雷达云图的有效分割成为了解决这一问题的关键.本文提出了一种基于超像素分析的全卷积神经网路FCN和深度卷积神经网络CNN（FCN-CNN）的云图分割方法.首先通过超像素分析对云图每个像素点的近邻域实现相应的聚类,同时将云图输入到不同步长的全卷积神经网络FCN32s和FCN8s中实现云图的预分割;FCN32s预测结果中的"非云"区域一定是云图中的部分"非云"区域,FCN8s预测结果中的"云"区域一定是云图中的部分"云"区域;剩下不确定的区域通过深度卷积神经网络CNN进行进一步分析.为提高效率,FCN-CNN选取了不确定区域中超像素的几个关键像素来代表超像素区域的特征,通过CNN网络来判断关键像素是"云"或者是"非云".实验结果表明,FCN-CNN的精度与MR-CNN、SP-CNN相当,但是速度相比于MR-CNN提高了880倍,相比于SP-CNN提高了1.657倍.     

The computation of cloud base height from paired whole-sky imaging cameras

We have developed a novel approach to the extraction of cloud base height (CBH) from pairs of whole-sky imagers (WSIs). The core problem is to spatially register cloud fields from widely separated WSIs; this complete, triangulation provides the CBH measurements. The wide camera separation and the self-similarity of clouds defeats standard matching algorithms when applied to static views of the sky. In response, we use optical flow methods that exploit the fact that modern WSIs provide image sequences. We will describe the algorithm, a confidence metric for its performance, a method to correct the severe projective effects of the WSI camera, and results on real data.     

Modeling Cumulus Cloud Scenes from High‐resolution Satellite Images

We present a reconstruction framework, which fits physically‐based constraints to model large‐scale cloud scenes from satellite images. Applications include weather phenomena visualization, flight simulation, and weather spotter training. In our method, the cloud shape is assumed to be composed of a cloud top surface and a nearly flat cloud base surface. Based on this, an effective method of multi‐spectral data processing is developed to obtain relevant information for calculating the cloud base height and the cloud top height, including ground temperature, cloud top temperature and cloud shadow. A lapse rate model is proposed to formulate cloud shape as an implicit function of temperature lapse rate and cloud base temperature. After obtaining initial cloud shapes, we enrich the shapes by a fractal method and represent reconstructed clouds by a particle system. Experiment results demonstrate the capability of our method in generating physically sound large‐scale cloud scenes from high‐resolution satellite images.     

基于随机森林和遥感的台风降水云光谱与降水关系研究

台风降水云系的云顶光谱-微物理信息与降水强度的变化密切相关,是卫星遥感定量反演降水的重要参数。以2001—2012年西北太平洋的7个典型超强台风和5个非超级台风为研究对象,基于热带测雨卫星搭载的测雨雷达与可见光红外扫描仪的融合资料,建立了降水云顶光谱-微物理参数反演台风降水的随机森林（RF）模型。结果表明：随机森林模型的交叉验证显示,反演的降水强度与观测的降水强度的相关系数为0.773,均方根误差为0.299 mm/h,表明该模型具有较高的降水反演精度。在随机森林构建过程输入的所有云顶光谱—微物理参数中,3.7μm云顶亮温对模型的方差贡献最大,重要性最高;而由于台风中心云系整体均发展旺盛且云顶较高,10.8μm云顶红外通道亮温以及云光学厚度、云水含量对模型的方差贡献较小,重要性低。     

云发生器的软件实现

用MATLAB实现了云发生器,包括正向云发生器和逆向云发生器。正向云发生器的核心技术是用中心极限定理生成正态随机数。对随机数产生的原理进行了阐述,通过用云表示定性概念“青年”对实验结果进行了分析。     

Cloud based management and control system for smart communities: A practical case study

In this study, the implementation of cloud based smart community management and control system was undertaken. SmartComunity.in is a flexible platform to manage and control the affairs of a condominium or society with thorough participation, visibility and transparency. Our research is the first attempt to study one such real life system of cloud based control and management in a smart housing community in India. There is a dearth of exploratory studies that explain the diffusion and adoption of cloud computing in different contexts and from a multiple stakeholder perspective. So, the main contribution of our research is to understand the framework of cloud computing based smart community services in India and the emerging cloud computing ecosystems. This research has wide ranging implications on the future of Internet of Things, and can be extended to elderly health and support, energy efficient systems and smart cities.     

NOAA卫星云检测和云修复业务应用系统的研制和建立

介绍了NOAA气象卫星云检测、云替补的光谱原理、技术模型以及基于VB5.0版本的业务系统的基本功能。系统经过试验运行和实况验证,取得了良好的运行效率与精度较高的云检测和替补效果,从而为NOAA卫星的遥感应用提供了可靠的数据源保证。     

长春地区云检测及云相态反演研究

采用经ENVI几何校正、定标及裁剪等预处理的2008年10月1日04:25和18:00(UTM)的MODIS L1B数据,利用MODIS云检测Ackerman方法及云相态反演红外二光谱技术,对长春地区进行了云检测和云相态反演。从检测及反演结果中看出,云检测及白天云相态反演效果都很好,而在夜晚条件下,由于低温,存在较大面积的不确定相态区域。     

云环境中基于cache负载实时定噪的同驻分析方法

云计算具有使用便捷、可按需定制服务、优化资源利用等特点,成为提供外包服务的主要计算模式。云环境中的虚拟机侧通道攻击是云计算的主要潜在威胁之一,同驻是云环境中侧通道攻击的前提。针对如何在多租户云环境下进行同驻检测,提出基于链式结构的Prime-Probe测量cache负载方法MCLPPLS和针对云环境噪声复杂多变问题的实时噪声分析机制RTNAM。结合MCLPPLS与RTNAM提出一种新型的同驻检测分析方法。实验表明,该方法能减少突发噪声对同驻检测的干扰,有较高的同驻检测正确率及较低的同驻检测时耗,表现出良好的性能。     

Inter-comparison of INSAT-3D atmospheric motion vectors height with cloud-base height from a Ceilometer

ABSTRACT

The atmospheric motion vectors (AMV) are derived by tracking cloud and moisture features in the subsequent images of geostationary as well as polar satellites. The heights of the AMVs are nothing but the height of cloud tracers used during the retrieval process for tracking. This height is derived using different complex techniques. In this study, a detailed comparison has been performed with the use of ground-based cloud-base height (CBH) measurements from ceilometer CL31, installed at Ahmedabad (23.03°N, 72.54°E), India and height assigned to AMVs which are retrieved from INSAT-3D satellite images. Six months CBH measurement over Ahmedabad from ceilometer CL31 has been used to inter-compare the co-located AMV heights. Although both ground-based and satellite-based techniques have their own limitations, however, it is found from this study that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds and height-assignments technique of AMVs retrieved from INSAT-3D satellite provides all high-, mid- and low-levels cloud information over this region. As an example, it is found that AMVs height of INSAT-3D is about 867.92, 750.00 and 465.09 hPa on 26 May 2014, 7 July 2014 and 29 October 2014, respectively, which matches very closely with ceilometer-measured CBH of about 873.15, 769.16 and 507.44 hPa, respectively. However, in case multi-level clouds present on rainy days, CBH measurements from ceilometer are differing from INSAT-3D AMV cloud tracer heights.     

基于云模型的网络攻击检测方法及其性能分析

为了有效判断网络数据包是否存在被攻击的可能性,提出了一种新的基于云模型的检测算法DMCM(Detection Method based on Cloud Model)。该算法首先结合数据包属性的离散度和偏差定义了状态指标,并根据云模型给出了标准差分布的计算流程,以此判断数据包的异常状况。最后,通过OPNET和MATLAB进行仿真实验,深入研究了影响该算法的关键因素,同时 与其它算法之间进行了性能对比,结果表明DMCM具有较好的适应性。     

峰度驱动的云进化策略

为了从传统进化策略的角度分析并改进云进化策略,研究云分布的峰度统计量及其应用。云分布在固定标准差时,也可通过调整峰度来改变噪声形状,可能产生更有效的变异。推导云分布峰度计算公式,以支持熵-超熵空间和标准差-峰度空间的相互转换。比较峰度和峰比对云分布噪声的影响,证明峰度更适宜自适应演化。给出峰度驱动的云进化策略,它的参数演化结合基于1/5规则的标准差演化和自适应峰度演化。对8个测试函数的实验结果显示,高峰度利于全局寻优,低峰度利于局部寻优,而峰度的自适应调整可综合二者优势。     

两种氧气A吸收带云顶高度反演算法的理论比较与实测结果验证

近年来,氧气A吸收带作为云高反演通道重新引起学者们的关注,其中用于欧空局ENVISat/SCIAMACHY仪器的SACURA和FRESCO+两种云高算法是目前比较成熟的氧气A带云高反演算法,并已有相关产品发布。为充分了解SACURA和FRESCO+两种算法的适用性及反演效果,对两种算法特点及其模拟反演的结果进行了深入比较,在敏感性分析的基础上评估这两种算法的特点及产品精度。结果表明,FRESCO+算法对地表反照率、云量等参数的依赖性较低,得到的云高结果偏低达20～750 m,而SACURA算法对太阳天顶角、云光学厚度、地表反照率和云量等参数较敏感,得到的云高值波动性较大。不包括光学厚度小于5的云且满云时,平均云顶误差小于300 m。在亮地表上的薄云结果最差,误差最大可达10 km以上。为验证上述结论,我们对2008年度覆盖我国及周边地区两种算法的相应产品进行了统计分析,并与寿县ARM计划的地基雷达和探空资料进行了对比,表明模拟反演得到的结论可信。      

利用MODIS云产品对安徽不同地区云特性的研究

针对不同区域云特性的差异,利用2006—2015年MODIS云产品MYD06_L2,对安徽淮北和江淮地区云相态、云粒子有效半径、云光学厚度和云顶高度发生概率进行统计分析,并对两地区云特性进行对比研究。研究表明,两地区夏季水云发生概率>冰云>晴空>混合相云,秋冬季则水云>晴空>冰云>混合相云。夏季两地区水云粒子有效半径相当,在14μm左右。淮北地区四季冰云粒子有效半径略高,夏季年际变化小,两地区逐年在24μm左右。春冬季江淮地区云光学厚度相对淮北地区较高。夏季冰云和混合相云云顶高度相对较高。研究结果为安徽省不同地区合理科学的规划和开展人工增雨作业提供了一定的理论依据。     

基于云空间和模糊嫡的边缘检测算法

基于模糊集理论及云理论,提出了对象云的图像模糊边缘检测方法(OCFD).算法充分考虑图像的模糊性和随机性,建立起图像空间与云空间的映射模型,生成模糊对象云和边界云,完成图像空间到云空间的映射.在云空间中实现逻辑云运算的边界云提取,提出并实现了基于边界云的过渡区定义及其提取算法.最后利用最大模糊熵在过渡区内实现检测边缘.实验证明,OCFD算法在检测性能方面优于模糊Sobel,Pal.King等算法,为图像的模糊理解和分析提供了一种新的思路,同时也丰富和拓展了云理论.     

耦合SVM和Cloud-Score算法的Sentinel-2影像云检测模型研究

云覆盖阻碍了光学遥感卫星对地观测的有效范围,快速、准确的云检测是遥感应用产品生成过程中的重要一步。针对Google Earth Engine云平台中缺乏适用且高质量的云检测模型,以热带多云的斯里兰卡为研究区,构建了耦合SVM和Cloud-Score算法的Sentinel-2影像云检测模型,通过实验从目视判读与定量分析两个角度对比了其与QA60法、Cloud-Score算法以及Fmask的云检测精度,并在海南岛和亚马逊森林两个地区进行了云检测测试。研究结果表明：Fmask模型的云检测性能最低,总体精度仅为63.45%,存在严重的水体误分为云的现象,但其漏提率极低;QA60法对卷云识别不足,漏提率较高,同时存在一定的误分现象,并且低空间分辨率影响了云体边界提取结果的细节性;Cloud-Score算法的云检测性能明显好于QA60法,总体精度达到了89.83%,误提率仅为2.17%,但仍存在部分卷云漏提的现象;相比于其他3种云检测方法,本文提出的云检测模型总体精度最高,达到了98.21%,并且拥有极低的漏提率和误提率,能比较精准地识别出云体的边界,可满足Sentinel-2遥感产品的云检测预处...