A dynamic global cloud layer for virtual globes

We describe a technique to merge multiple environmental satellite data sets for an hourly updated, near real-time global depiction of cloud cover for virtual globe applications. A global thermal infrared composite obtained from merged geostationary- (GEO) and low-Earth-orbiting (LEO) satellite data is processed to depict clear and cloudy areas in a visually intuitive fashion. This GEO-plus-LEO imagery merging is complicated by the fact that each individual satellite observes a single ‘snapshot’ of the cloud patterns, each taken at different times, whereas the underlying clouds themselves are constantly moving and evolving. For the cloudy areas, the brightness and transparency are approximated based upon the cloud top temperature relative to the local radiometric surface temperatures (corrected for surface emissivity variations) at the time of the satellite observation. The technique clearly defines and represents mid- to high-level clouds over both land and ocean. Due to their proximity to the Earth's surface, low-level clouds such as stratocumulus and stratus clouds will be poorly represented with the current technique, since warmer temperatures in this case do not correspond to higher cloud transparency. Overcoming this problem requires the introduction of multispectral channel combinations.     

Mapping active fault associated with the 2003 Mw 6.6 Bam (SE Iran) earthquake with ASTER 3D images

Satellite images reveal the geomorphology and geometry of an active fault associated with an earthquake that caused over 40,000 deaths in southeast Iran. This earthquake, of moment magnitude (Mw) 6.6, occurred at 01:56:52 (UTC) on December 26, 2003 near the towns of Bam and Baravat. An active fault that ruptured during the earthquake can be seen on three-dimensional (3D) pre- and post-earthquake images generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible and near infrared (VNIR) data. The interpretation and analysis of satellite images suggest that a dextral fault extends for 65 km through the Bam-Baravat region. It strikes north-northwest and steps leftward at a segment boundary between two towns. Transpressional stress at this step-over (bend) probably triggered the 2003 earthquake rupture, as suggested also by source mechanism solution of the Bam earthquake and by landforms observed in the field. In the past 2000 years, Bam has had no earthquake as destructive as the one in 2003. The potential for this catastrophe could have been identified through a combination of satellite remote sensing technique and field reconnaissance.     

Earthquake cloud over Japan detected by satellite

Thermal anomaly detected by satellite before earthquake has been widely reported. Here we reported a new anomaly detected with geostationary satellite image. The geostationary satellite image series were combined together to make an animation, and it is found that the common cloud moved continuously, while the earthquake cloud stayed closely to the epicenter. Here an earthquake cloud example in Japan was reported.     

Automatic cloud-shadow removal from flood/standing water maps using MSG/SEVIRI imagery

Cloud shadows are a major problem in the detection of flood/standing water using satellite data. Because cloud shadows and flood/standing water have similar spectral characteristics, the traditional means of detection based on spectral properties may fail to distinguish them from each other accurately. Because clouds cast shadows over land, this phenomenon can be analysed using the geometric correlations between clouds and cloud shadows; thus, this method might detect cloud shadows. Based on this concept, geometric relationships were established between clouds and their shadows using satellite data and satellite-solar geometries. Furthermore, an iterative method combining geometric and spectral properties was developed to automatically remove cloud shadows from flood/standing water in satellite maps. This method was applied and tested using MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager) data and continues to show promising and consistent results.     

Discriminating sea ice from low-level water clouds in split-window,mid-wavelength IR imagery

A technique is demonstrated to enhance the contrast between sea ice and low-level water clouds. The approach uses the brightness temperature difference (BTD) feature from data collected in the split-window, mid-wavelength infrared (IR) region (i.e. two bands at 3.7 μm and 4.0 μm). These spectral data are available with Visible Infrared Imager Radiometer Suite (VIIRS) moderate-resolution bands M12 and M13, respectively. Under daytime conditions, the data collected in these bands contain energy that originates from both the sun and the Earth–atmosphere system. Due to the small wavelength difference between these, the terrestrial energy component in the bands is typically quite similar as are the surface reflectances for sea ice and ocean surfaces. Thus, the enhanced contrast between sea ice and water clouds, evident in a M12–M13 BTD image, results from differences in the solar energy, which decreases rapidly across this atmospheric window. Observed BTD values for water clouds can exceed 30K, while those for snow-ice fields are typically much smaller (e.g. 0–5K). Thus, water clouds appear bright in the image while sea ice, oceans, and most land surfaces are very dark. The enhanced contrast in the split-window, mid-wave IR BTD image makes it valuable for both image analysis and use in cloud algorithms. In addition, these images support the creation of manually generated cloud masks that have been shown useful for quantitatively evaluating the performance of automated cloud analysis algorithms and cloud forecast models. In this article, the value of 3.7 μm minus 4.0 μm BTD imagery for distinguishing between sea ice and low-level water clouds is shown using VIIRS data collected over the Beaufort Sea on 31 May 2012. Manually generated cloud masks, derived in part from these data, are then used to quantitatively evaluate the effectiveness of various cloud tests, including those used in the VIIRS cloud mask algorithm and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm. The results strongly suggest that split-window, mid-wavelength IR imagery provides valuable information to help differentiate between clouds and sea ice. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring, especially over the polar regions.     

Land cover estimation with ALOS satellite image using a neural-network

On May 12, 2008, a large earthquake occurred in Sichuan, China. We analyzed the damage caused by this disaster using satellite images from ALOS, a Japanese satellite. The land cover classification is operated by images captured on AVNIR-2. Frequently, the AVNIR-2 images cannot be monitored because of the cloud cover and solar irradiation. The area near the center of the earthquake area is covered with clouds. The goal of this article is to classify the land cover using PALSAR images. PALSAR can observe over a 350-km-wide area independently of the weather. The PALSAR is a single-band sensor, and the inputs consist of many pixels by using the nearest pixel values, and the supervisor signal is the classes estimated by AVNIR-2.     

Cloud detection from thermal infrared images using a segmentation technique

In this paper, a fast and accurate method is proposed for two cloud detection tests on thermal infrared (IR) data obtained from satellite images over sea; an IR gross cloud test and a spatial coherence test. The proposed method is based on a regional segmentation technique. After the segmentation of an IR image, small regions were regarded as cloudy due to their high spatial variability in temperature. This technique preserved the spatial resolution of the detected cloud image which would be degraded by the conventional spatial coherence test. It also reduced the computation dramatically, compared to the conventional spatial coherence test. An accurate temperature threshold between clear sea and clouds was determined directly from the segmented image. This post-determined threshold was found to be more accurate than pre-determined temperature thresholds. Since this algorithm does not require any human interaction, it can be combined with other tests in an automatic cloud detection algorithm.     

Derivation of 3D cloud animation from geostationary satellite images

Large-scale cloud animation is crucial to TV weather presentation, weather observer training and video products. In this paper, a physically based system is presented for the derivation of time-varying 3D clouds from geostationary satellite images. Cloud properties are derived from a set of meteorological models while the clouds are rendered by graphics models, the proposed method thus presents a new modeling methodology, which integrates the reality of the data with the realistic visual feeling. In particular, image pixels are first classified into cloud-free, water cloud, ice cloud, thin cirrus cloud in terms of their spectral signature. Then, cloud top surface, cloud bottom surface and cloud extinction are generated by applying different combinations of images. Finally, clouds are rendered under various light directions or view directions. The results have indicated that the proposed method can yield a realistic and approximately valid clouds with similar appearance to those in the input satellite images.     

An Online Fault Detection Model and Strategies Based on SVM-Grid in Clouds

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM-Grid, whose construction is based on a support vector machine (SVM). SVM-Grid is used to optimize parameters in SVM. Proper attributes of a cloud system's running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.      

Night-time cloud detection for FY-3A/VIRR using multispectral thresholds

Night-time cloud detection using satellite data is a challenging area of research. This article presents a night-time cloud detection algorithm based on multispectral thresholds for the Visible and Infrared Radiometer (VIRR). VIRR is one of the keystone instruments on board the Chinese Feng Yun 3 (FY-3) polar-orbiting meteorological satellite. In this algorithm, three thermal infrared channels and other ancillary data are used to test for the presence of clouds according to different underlying surface types, and the four levels of possible cloud confidence are used to report whether a pixel is cloudy or clear. This algorithm strengthens the ability of identification of low cloud using the brightness temperature difference between the 3.7 and 12 μm channels. The comparisons of a new cloud mask with the official VIRR cloud mask product and with the official Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product are shown to illustrate and validate the effect of this new algorithm. In addition, this algorithm is applied to FY-3B/VIRR data to test the validity and accuracy of cloud detection.     

地震预报的新方法、新途径

地震预报, 特别是短临预报一直是个世界性的科学难题。介绍了几种地震预报的新方法、新途径, 包括: 卫星热红外异常法、地震云法、磁暴二倍法、引潮力共振的异常叠加法等。对各种方法, 特别是涉及遥感应用的地震预报方法的机理及其优缺点进行了分析和初步总结。希望通过与地震预报常规方法的优势互补, 相互印证, 从而促进地震预报能够尽早取得突破性进展。     

吉林Ms4.3微地震天体引潮力和温度变化过程初探

计算2009年3月20日发生在东经124.9°、北纬43.4°的吉林省四平的Ms4.3地震的天体引潮力周期变化过程,并据该周期,分析多源综合温度数据资料,提取地震过程的异常温度变化图像,结果表明:天体引潮力对地应力处于临界状态的活动断层具有诱发作用,震前异常增温明显,异常增温经历了:起始→加强→高峰→衰减→平静的演变过程。对此次微震的研究结果表明温度异常在微地震中也有清晰反映,微震过程中引力作用明显,是对地震-热异常的有力证据,同时也是对利用热异常预测地震方法科学性的补充证实。     

基于被动微波的地表温度反演研究综述

热红外遥感反演地表温度已取得丰硕的成果,某些反演算法精度可达到1 K以内。然而在非晴空条件下,热红外遥感的应用受到很大限制,甚至无能为力。而被动微波遥感受大气干扰小,可穿透云层获取地表辐射信息,具有全天候、多极化及高时间分辨率等特点,在地表温度反演中具有独特的优越性。被动微波反演地表温度已经成为被动微波遥感技术应用研究的主要问题之一。系统阐述了微波热辐射机理、地表温度反演模型、反演算法及应用现状,分析了目前被动微波地表温度反演研究中存在的主要问题与技术难点,为后续相关研究提供参考。     

Improving volcanic ash cloud detection by a robust satellite technique

Automated and reliable satellite-based techniques are strongly required for volcanic ash cloud detection and tracking. In fact, volcanic ash clouds pose a serious hazard for air traffic and the synoptic (and possibly frequent) coverage offered by satellites can provide exciting opportunities for monitoring activities as well as for risk mitigation purposes.A new, AVHRR-based technique for improved automatic detection of volcanic clouds by means of multi-temporal analysis of historical, long-term satellite records has been recently proposed. The technique basically rests on the Robust AVHRR Techniques (RAT) approach, which is an innovative strategy of satellite data analysis, devoted to a former characterisation of the measured signal, in terms of expected value and natural variability and a further recognition of signal anomalies by an automatic, unsupervised change detection step. In this work, an extension of this method to nighttime observations is presented, by using thermal infrared information coming from AVHRR bands centred approximately at 3.5, 11.0 and 12.0 μm. Results achieved for two recent eruptive events of Mount Etna (occurred in May 2000 and in July 2001) seem to be encouraging, showing clear improvements in terms of ash detection sensitivity as well as in terms of false alarms reduction. The technique performance is also evaluated by comparison with the traditional “split-window” brightness temperature difference method; this exercise revealed a general improvement obtained by the proposed approach, even though some common problems still remain unsolved. The main merits of such an approach are its intrinsic self-adaptability to different environmental/natural/observational conditions and its natural exportability also to different satellite sensors. The results here presented show the benefits of such a technique especially when different observational conditions (time of pass, seasonal period, atmospheric moisture, solar illumination, volcanic cloud composition, satellite angles of view, etc.) are considered.The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (like, for example, SEVIRI aboard Meteosat Second Generation platform) are also discussed.     

Cloud screening in IRS-P4 OCM satellite data: potential of spatial coherence method in the absence of thermal channel information

Cloud screening of satellite data for the remote sensing of atmospheric aerosols, ocean sediments, chlorophyll, and phytoplankton in the marine environment is a major problem in the absence of information from thermal channel. This is particularly the case with the data from some of the highly potential satellite sensors such as the Ocean Colour Monitor (OCM—on board the Indian Remote Sensing Satellite, IRS-P4) and the SeaWiFS. Two main tests conventionally used for cloud screening of data from such satellite sensors are the threshold method applied to visible and near-IR bands and the visible to near-IR channel ratio method. These methods do not have the potential to eliminate the pixels with small cloud fractions, leading to overestimation of the aerosol optical depth (AOD) derived from satellite data, and might also identify the pixels with high values of AOD as cloudy. The purpose of this paper is to study the potential of Spatial Coherence Test (SCT) applied to the data from the near-IR bands for cloud screening of satellite data over the oceanic environment. We use here the data from IRS-P4 OCM. Though more computationally intensive, the SCT does not suffer from the serious limitations of the threshold and channel ratio methods and is found to be superior in identifying the clear sky pixels that are not affected by clouds. Although the SCT applied to near-IR channel data may be overestimating the number of cloud affected pixels, it neither leads to overestimation of AOD nor identifies the pixels with high AOD values as cloudy.     

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。     

Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments

Failures are normal rather than exceptional in cloud computing environments, high fault tolerance issue is one of the major obstacles for opening up a new era of high serviceability cloud computing as fault tolerance plays a key role in ensuring cloud serviceability. Fault tolerant service is an essential part of Service Level Objectives (SLOs) in clouds. To achieve high level of cloud serviceability and to meet high level of cloud SLOs, a foolproof fault tolerance strategy is needed. In this paper, the definitions of fault, error, and failure in a cloud are given, and the principles for high fault tolerance objectives are systematically analyzed by referring to the fault tolerance theories suitable for large-scale distributed computing environments. Based on the principles and semantics of cloud fault tolerance, a dynamic adaptive fault tolerance strategy DAFT is put forward. It includes: (i) analyzing the mathematical relationship between different failure rates and two different fault tolerance strategies, which are checkpointing fault tolerance strategy and data replication fault tolerance strategy; (ii) building a dynamic adaptive checkpointing fault tolerance model and a dynamic adaptive replication fault tolerance model by combining the two fault tolerance models together to maximize the serviceability and meet the SLOs; and (iii) evaluating the dynamic adaptive fault tolerance strategy under various conditions in large-scale cloud data centers and consider different system centric parameters, such as fault tolerance degree, fault tolerance overhead, response time, etc. Theoretical as well as experimental results conclusively demonstrate that the dynamic adaptive fault tolerance strategy DAFT has high potential as it provides efficient fault tolerance enhancements, significant cloud serviceability improvement, and great SLOs satisfaction. It efficiently and effectively achieves a trade-off for fault tolerance objectives in cloud computing environments.     

Geometric cloud heights from Meteosat

Atmospheric Motion Vectors (AMVs) constitute an integral part of the global observing system. The heights of these vectors are determined by the target temperature, which, in the case of semi-transparent or broken clouds, is corrected for the upwelling radiance from scenes below the target. Despite the success of these methods, the attributed heights are still the largest single source of error of the AMVs. In order to improve the existing methodologies to determine the height of the AMVs alternative methods have to be employed. The potential of geometric cloud height estimates, where two satellites view the same cloud, has already been demonstrated with geosynchronous satellite data only as well as together with data from polar orbiting satellites. In these studies the cloud targets have been selected manually and the analysis has generally been based on imagery data from the visible channels. In this paper we take the methods further by applying them to geostationary infrared imagery data, using a fully automated procedure. The reason for exploiting infrared data is the capability of continuous observations also during night-time. The results presented demonstrate the stereo method as a verification and analysis tool for satellite cloud top temperature height retrievals and that it can be applied to 5-km resolution infrared imagery data from the geostationary satellites Meteosat 5 and Meteosat 7. A further analysis combining Meteosat data with imagery data from a polar orbiting satellite shows that the automated geometric technique can be extended beyond the range of overlapping geosynchronous observations, using an asynchronous stereo technique.     

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.     

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.