- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Search for Best Astronomical Observatory Sites in the MENA Region using Satellite Measurements
摘要： We perform a systematic search for astronomical observatory sites in the MENA (Middle-East and North Africa) region using space-based data for all the relevant factors, i.e. altitude (DEM), cloud fraction (CF), light pollution (NTL), precipitable water vapor (PWV), aerosol optical depth (AOD), relative humidity (RH), wind speed (WS), Richardson Number (RN), and diurnal temperature range (DTR). We look for the best locations overall even where altitudes are low (the threshold that we normally consider being 1,500 m) or where the combination of the afore-mentioned determining factors had previously excluded all locations in a given country. In this aim, we use the rich data that Earth-observing satellites provide, e.g. the Terra and Aqua multi-national NASA research satellites, with their MODIS (Moderate Resolution Imaging Spectroradiometer) and AIRS (Atmospheric Infrared Sounder) instruments, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), and other products from climate diagnostics archives (e.g. MERRA). We present preliminary results on the best locations for the region.
关键词： diurnal temperature range,Richardson Number,MENA region,wind speed,precipitable water vapor,cloud fraction,relative humidity,aerosol optical depth,light pollution,astronomical observatory sites,satellite measurements
更新于2025-09-11 14:15:04
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - A Proactive Restoration Strategy for Optical Cloud Networks Based on Failure Predictions
摘要： Failure prediction based on the anomaly detection/forecasting is becoming a reality thanks to the introduction of machine learning techniques. The orchestration layer can leverage on this new feature to proactively reconfigure cloud services that might find themselves traversing an element that is about to fail. As a result, the number of cloud service interruptions can be reduced with beneficial effects in terms of cloud service availability. Based on the above intuition, this paper presents an orchestration strategy for optical cloud networks able to reconfigure vulnerable cloud services (i.e., the ones that would be disrupted if a predicted failure happens) before an actual failure takes place. Simulation results demonstrate that, with a single link failure scenario, proactive restoration can lead to up to 97% less cloud services having to be relocated. This result brings considerable benefits in terms of cloud service availability, especially in low load conditions. It is also shown that these improvements come with almost no increase in the cloud service blocking probability performance, i.e., resource efficiency is not impacted.
关键词： Cloud services,Software defined networking (SDN),Availability,Failure prediction,Orchestration,Cloud service relocation,Resiliency,Proactive recovery,Restoration
更新于2025-09-10 09:29:36
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Cloud Photogrammetry with Dense Stereo for Fisheye Cameras
摘要： In this paper, we present our approach for dense 3D cloud reconstruction using two hemispheric sky imagers with fisheye lenses in a stereo setup. Fisheye lenses follow a different projection function than classical pinhole-type cameras, which provide a large field of view with a single image, but also renders the computation of dense 3D information more complicated, such that we cannot rely entirely on standard implementations for dense 3D stereo reconstruction. In this work, we examine the epipolar rectification model, which allows the use of dense matching algorithms designed for classical perspective cameras to search for disparity information at every pixel. Together with an appropriate camera calibration, which includes internal camera geometry and global position and orientation of the stereo camera pair, we can use the disparity information for dense 3D stereo reconstruction of the a cloud and thus estimate its shape. From the obtained 3D shapes, cloud dynamics, size, motion, type and spacing can be derived and used e.g. for radiation closure under cloudy conditions. We implemented and evaluated the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with the reflectivity observations measured by a cloud radar and the cloud base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus convection in the presence of strong wind shear.
关键词： fisheye cameras,cloud photogrammetry,dense stereo,cloud dynamics,3D reconstruction
更新于2025-09-10 09:29:36
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Space lidar observations constrain longwave cloud feedback
摘要： Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.
关键词： space lidar observations,climate models,climate warming,longwave cloud radiative effect,cloud feedback
更新于2025-09-10 09:29:36
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Directional, Horizontal Inhomogeneities of Cloud Optical Thickness Fields Retrieved from Ground-Based and Airborne Spectral Imaging
摘要： Clouds exhibit significant horizontal inhomogeneities of their optical and microphysical properties, which complicate their realistic representation in weather and climate models. In order to investigate the directional, horizontal structure of cloud inhomogeneities, two-dimensional (2D) horizontal fields of optical thickness of subtropical cirrus and Arctic stratus with a spatial resolution of <10 m are investigated. The cloud optical thickness fields were derived from downward (transmitted) solar spectral radiance measurements from the ground beneath four cirrus clouds, and upward (reflected) radiances measured from aircraft above ten Arctic stratus clouds. The data were collected during the two major field campaigns Clouds, Aerosol, Radiation, and tuRbulence in the trade wInd regime over BArbados (CARRIBA) and VERtical Distribution of Ice in Arctic clouds (VERDI). Scalar one-dimensional (1D) and 2D autocorrelation functions, as well as power spectral densities are derived from the retrieved τ fields. Decorrelation lengths and scale breaks are identified and used to characterize the size range of the inhomogeneities and their influence on three-dimensional (3D) radiative effects. These studies reveal that there are considerable directional cloud inhomogeneities along and across the prevailing cloud structures. Therefore it is not sufficient to quantify horizontal cloud inhomogeneities by scalar 1D inhomogeneity parameters; 2D parameters are necessarily required.
关键词： power spectral densities,spectral imaging,autocorrelation functions,scale breaks,cloud optical thickness,decorrelation lengths,horizontal inhomogeneities,3D radiative effects
更新于2025-09-10 09:29:36
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An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets
摘要： Optically thin ice and mixed-phase clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation. Cloud-base heights can be detected by ceilometers, low-power backscatter lidars that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. The standard cloud-base detection algorithms of ceilometers are designed to detect optically thick liquid-containing clouds, while the detection of thin ice clouds requires an alternative approach. This paper presents the polar threshold (PT) algorithm that was developed to be sensitive to optically thin hydrometeor layers (minimum optical depth τ ≥ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3 × 10?4 km?1 sr?1 for cloud-base detection near the surface was derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. At higher altitudes where the average noise level is higher than the backscatter threshold, the PT algorithm becomes signal-to-noise ratio driven. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 90 m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly discriminate between clear-sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72 % (±10 %) at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid water droplets, shows a seasonal cycle at Summit with a monthly mean summer peak of 40 % (±4 %). The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 46 % (±5 %), which reduces to 12 % (±2.5 %) for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers located near the surface for both sites, with 87 % of all detections below 500 m for Summit and 80 % below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat that may be insensitive for hydrometeors near the surface. The decrease of sensitivity with height, which is an inherent limitation of the ceilometer, does not have a significant impact on our results. This study highlights the potential of the PT algorithm to extract information in polar regions from various hydrometeor layers using measurements by the robust and relatively low-cost ceilometer instrument.
关键词： optically thin ice clouds,cloud-base detection,polar regions,mixed-phase clouds,ceilometer
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deepcloud - A Fully Convolutionnal Neural Network for Cloud and Shadow Masking in Optical Satellite Images
摘要： Many cloud and shadow detection methods have been proposed already, but improvements can be made on accuracy or automation. In this study, we propose a Fully Convolutional Network model for the detection of clouds and shadows in optical satellite images. The proposed model was trained on 165 Landsat images in Finland, and tested on an independent set of images. The cloud and shadow detection accuracy reached 95%, outperforming both quantitatively and qualitatively a selection of other deep learning architectures.
关键词： optical images,Landsat,fully convolutional network,deep learning,Cloud and shadow masking
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
摘要： Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat-8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adapted to resemble Proba-V characteristics and tested on a large set of real Proba-V scenes. Developed models outperform current operational Proba-V cloud detection without being trained with any real Proba-V data. Moreover, cloud detection accuracy can be further increased if the CNN are fine-tuned using a limited amount of Proba-V data.
关键词： Proba-V,Transfer Learning,CNN,Cloud detection,Domain Adaptation,Landsat-8
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - UAV-BASED INTEGRATED MULTISENSOR PAYLOAD FOR HIGH RESOLUTION IMAGING
摘要： This paper describes the development of a multisensor UAV-based imaging platform. A sensor package consisting of a LiDAR, imaging spectrometer, and RGB camera were integrated with an inertial navigation system. This package is currently being flown on a hexacopter. A number of data acquisition missions have been conducted with this platform, including the imaging of a small active rotational mass movement, a wetland estuary, and several vineyards.
关键词： SfM point cloud,UAV,imaging spectrometer,LiDAR,INS
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Discriminative Learning of Point Cloud Feature Descriptors Based on Siamese Network
摘要： It is challenging to direct extract the feature descriptors of the object in the point cloud, although deep learning has been widely used with the classification and detection in the point cloud, those methods hidden feature presentation in the network. Since the point cloud scanned by the Laser Scanner usually have different point density, unordered and even the different occlusion, which go beyond the reach of hand-crafted descriptors, e.g. FPH, FPFH, VFH, ROPS. In this paper, we aim to direct extract the feature descriptors of the point cloud object through the raw point cloud. Inspired by the recent success of the Siamese networks[6], PointNet[7] and PointNet++[8], we propose a novel network to direct extract the feature descriptors of the whole point cloud object. We train our network with the Euclidean distance as the loss function which reflects feature descriptors similarity. The experiment object datasets were acquired by Mobile Laser Scanning (MLS) system which contains 6 categories. Experiment result shows that our network has a robust generalization, which can well direct extract the feature descriptors of the whole point cloud object.
关键词： Point cloud,mobile laser scanning,feature description,siamese network
更新于2025-09-10 09:29:36