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Academic literature on the topic 'Clouds Classification'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Clouds Classification"

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Hutchison, Keith D., Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson. "Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms." Journal of Atmospheric and Oceanic Technology 25, no. 4 (April 1, 2008): 501–18. http://dx.doi.org/10.1175/2007jtecha1004.1.

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Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.
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Urbanek, Benedikt, Silke Groß, Andreas Schäfler, and Martin Wirth. "Determining stages of cirrus evolution: a cloud classification scheme." Atmospheric Measurement Techniques 10, no. 5 (May 3, 2017): 1653–64. http://dx.doi.org/10.5194/amt-10-1653-2017.

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Abstract. Cirrus clouds impose high uncertainties on climate prediction, as knowledge on important processes is still incomplete. For instance it remains unclear how cloud microphysical and radiative properties change as the cirrus evolves. Recent studies classify cirrus clouds into categories including in situ, orographic, convective and liquid origin clouds and investigate their specific impact. Following this line, we present a novel scheme for the classification of cirrus clouds that addresses the need to determine specific stages of cirrus evolution. Our classification scheme is based on airborne Differential Absorption and High Spectral Resolution Lidar measurements of atmospheric water vapor, aerosol depolarization, and backscatter, together with model temperature fields and simplified parameterizations of freezing onset conditions. It identifies regions of supersaturation with respect to ice (ice-supersaturated regions, ISSRs), heterogeneous and homogeneous nucleation, depositional growth, and ice sublimation and sedimentation with high spatial resolution. Thus, all relevant stages of cirrus evolution can be classified and characterized. In a case study of a gravity lee-wave-influenced cirrus cloud, encountered during the ML-CIRRUS flight campaign, the applicability of our classification is demonstrated. Revealing the structure of cirrus clouds, this valuable tool might help to examine the influence of evolution stages on the cloud's net radiative effect and to investigate the specific variability of optical and microphysical cloud properties in upcoming research.
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Wang, Y., M. Penning de Vries, P. H. Xie, S. Beirle, S. Dörner, J. Remmers, A. Li, and T. Wagner. "Cloud and aerosol classification for 2 1/2 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets." Atmospheric Measurement Techniques Discussions 8, no. 5 (May 6, 2015): 4653–709. http://dx.doi.org/10.5194/amtd-8-4653-2015.

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Abstract. Multi-Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterise their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g. clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been modified, in particular in order to account for smaller solar zenith angles (SZA). Instrumental degradation is accounted for to avoid artificial trends of the cloud classification. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby AERONET station and from MODIS, visibility derived from a visibility meter; and various cloud parameters from different satellite instruments (MODIS, OMI, and GOME-2). The most important findings from these comparisons are: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective AOD ranges obtained by AERONET and MODIS, (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite indicate that the cloud classification scheme is sensitive to cloud (optical) properties, (3) separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds, (4) some clear sky conditions, especially with high aerosol load, classified from MAX-DOAS observations corresponding to the optically thin and low clouds derived by satellite observations probably indicate that the satellite cloud products contain valuable information on aerosols.
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Marchant, Benjamin, Steven Platnick, Kerry Meyer, G. Thomas Arnold, and Jérôme Riedi. "MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP." Atmospheric Measurement Techniques 9, no. 4 (April 11, 2016): 1587–99. http://dx.doi.org/10.5194/amt-9-1587-2016.

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Abstract. Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
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Marchant, B., S. Platnick, K. Meyer, G. T. Arnold, and J. Riedi. "MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP." Atmospheric Measurement Techniques Discussions 8, no. 11 (November 16, 2015): 11893–924. http://dx.doi.org/10.5194/amtd-8-11893-2015.

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Abstract. Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
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Gryspeerdt, Edward, Johannes Quaas, Tom Goren, Daniel Klocke, and Matthias Brueck. "An automated cirrus classification." Atmospheric Chemistry and Physics 18, no. 9 (May 3, 2018): 6157–69. http://dx.doi.org/10.5194/acp-18-6157-2018.

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Abstract. Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model–observation comparisons and leading to improved parametrisations of cirrus cloud processes.
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Chen, Xidong, Liangyun Liu, Yuan Gao, Xiao Zhang, and Shuai Xie. "A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images." Remote Sensing 12, no. 15 (July 23, 2020): 2365. http://dx.doi.org/10.3390/rs12152365.

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Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.
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Wang, Y., M. Penning de Vries, P. H. Xie, S. Beirle, S. Dörner, J. Remmers, A. Li, and T. Wagner. "Cloud and aerosol classification for 2.5 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets." Atmospheric Measurement Techniques 8, no. 12 (December 10, 2015): 5133–56. http://dx.doi.org/10.5194/amt-8-5133-2015.

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Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterize their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g., clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long-term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been adapted to the characteristics of the Wuxi instrument, and extended towards smaller solar zenith angles (SZA). Moreover, a method for the determination and correction of instrumental degradation is developed to avoid artificial trends of the cloud classification results. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby Aerosol Robotic Network (AERONET) station and from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, visibility derived from a visibility meter and various cloud parameters from different satellite instruments (MODIS, the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment (GOME-2)). Here it should be noted that no quantitative comparison between the MAX-DOAS results and the independent data sets is possible, because (a) not exactly the same quantities are measured, and (b) the spatial and temporal sampling is quite different. Thus our comparison is performed in a semi-quantitative way: the MAX-DOAS cloud classification results are studied as a function of the external quantities. The most important findings from these comparisons are as follows: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective aerosol optical depth (AOD) ranges obtained by AERONET and MODIS; (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite confirm that the MAX-DOAS cloud classification scheme is sensitive to cloud (optical) properties; (3) the separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds; (4) for some cloud-free conditions, especially with high aerosol load, the coincident satellite observations indicated optically thin and low clouds. This finding indicates that the satellite cloud products contain valuable information on aerosols.
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Wagner, T., S. Beirle, S. Dörner, U. Friess, J. Remmers, and R. Shaiganfar. "Cloud detection and classification based on MAX-DOAS observations." Atmospheric Measurement Techniques Discussions 6, no. 6 (December 3, 2013): 10297–360. http://dx.doi.org/10.5194/amtd-6-10297-2013.

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Abstract. Multi-AXis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like the radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, also other quantities and viewing directions are considered, which allows sub-classifications like e.g. thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the CINDI campaign in the Netherlands in Summer 2009 and found very good agreement with sky images taken from ground.
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Behrangi, Ali, Terry Kubar, and Bjorn Lambrigtsen. "Phenomenological Description of Tropical Clouds Using CloudSat Cloud Classification." Monthly Weather Review 140, no. 10 (October 1, 2012): 3235–49. http://dx.doi.org/10.1175/mwr-d-11-00247.1.

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Abstract Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 2° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds.
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Dissertations / Theses on the topic "Clouds Classification"

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Liao, Nilsson Sunny, and Martin Norrbom. "CLASSIFICATION OF BRIDGES IN LASER POINT CLOUDS USING MACHINE LEARNING." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55067.

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In this work, machine learning was being used for bridge detection in point clouds. To estimate the performance, it was compared to an existing algorithm based on traditional methods for point classification. The purpose of this work was to use machine learning for bridge classification in point clouds. To see how today's machine learning algorithms perform and find the challenges of using machine learning in point classification. The point clouds used are based on airborne laser scanning and represent the land area over Sweden. To get satisfactory results, several different testing areas were used with varying landscapes. For comparing the two different algorithms, both statistical and visual analysis were made to identify the algorithms' behaviours, strengths, and weaknesses. The machine learning algorithm tested was PointNet++, and it was compared to the current algorithm that the Swedish mapping, cadastral and land registration authority use for bridge classification in point clouds. Based on the results, the current method had higher accuracy in the classification of bridge points, but the machine learning approach could detect more bridges. Thus, it was concluded that there are potential for this machine learning approach, but there are still needs for improvements.
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Kanngießer, Franz, André Ehrlich, and Manfred Wendisch. "Observations of glories above arctic boundary layer clouds to identify cloud phase." Universität Leipzig, 2017. https://ul.qucosa.de/id/qucosa%3A16743.

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The glory is an optical phenomenon observed above liquid water clouds and consists of coloured rings around the anti-solar point. Since the glory is caused by scattering on spherical particles it can be used as a proxy to identify liquid water at the cloud top. Images taken with a CANON digital camera equipped with a fish-eye lens on board the research aircraft Polar 5 during the measurement campaign Radiation-Aerosol-Cloud Experiment in the Arctic Circle (RACEPAC) were analysed for glories. To identify glories an algorithm consisting of five criteria was developed by using simulations of the scattering angle dependent radiance and a test data set of measurements. The algorithm was tested and proved to be able to distinguish between images showing a glory and images not showing any glory.
Die Glorie ist eine optische Erscheinung, die über Flüssigwasserwolken beobachtet werden kann und aus farbigen Ringen um den Gegensonnenpunkt besteht. Da die Glorie durch Streuung an sphärischen Partikeln entsteht, kann sie zur Identifikation von Flüssigwasser am Wolkenoberrand genutzt werden. Bilder, die mit einer CANON Digitalkamera, die mit einem Fischaugenobjektiv ausgestattet war, von Bord des Forschungsflugzeugs Polar 5 während der Messkampagne RACEPAC aufgenommen worden, wurden auf das Auftreten von Glorien untersucht. Zur Identifikation wurde ein Algorithmus mit fünf Kriterien entwickelt, die mit Hilfe von Simulationen der streuwinkelabhängigen Radianz und einem Testdatensatz der Messungen erstellt wurden. Der Algorithmus wurde getestet und ist in der Lage zwischen Bildern mit und ohne Glorie zu unterscheiden.
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Wiklander, Marcus. "Classification of tree species from 3D point clouds using convolutional neural networks." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-174662.

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In forest management, knowledge about a forest's distribution of tree species is key. Being able to automate tree species classification for large forest areas is of great interest, since it is tedious and costly labour doing it manually. In this project, the aim was to investigate the efficiency of classifying individual tree species (pine, spruce and deciduous forest) from 3D point clouds acquired by airborne laser scanning (ALS), using convolutional neural networks. Raw data consisted of 3D point clouds and photographic images of forests in northern Sweden, collected from a helicopter flying at low altitudes. The point cloud of each individual tree was connected to its representation in the photos, which allowed for manual labeling of training data to be used for training of convolutional neural networks. The training data consisted of labels and 2D projections created from the point clouds, represented as images. Two different convolutional neural networks were trained and tested; an adaptation of the LeNet architecture and the ResNet architecture. Both networks reached an accuracy close to 98 %, the LeNet adaptation having a slightly lower loss score for both validation and test data compared to that of ResNet. Confusion matrices for both networks showed similar F1 scores for all tree species, between 97 % and 98 %. The accuracies computed for both networks were found higher than those achieved in similar studies using ALS data to classify individual tree species. However, the results in this project were never tested against a true population sample to confirm the accuracy. To conclude, the use of convolutional neural networks is indeed an efficient method for classification of tree species, but further studies on unbiased data is needed to validate these results.
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König, Sören, and Stefan Gumhold. "Robust Surface Reconstruction from Point Clouds." Technische Universität Dresden, 2013. https://tud.qucosa.de/id/qucosa%3A27391.

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The problem of generating a surface triangulation from a set of points with normal information arises in several mesh processing tasks like surface reconstruction or surface resampling. In this paper we present a surface triangulation approach which is based on local 2d delaunay triangulations in tangent space. Our contribution is the extension of this method to surfaces with sharp corners and creases. We demonstrate the robustness of the method on difficult meshing problems that include nearby sheets, self intersecting non manifold surfaces and noisy point samples.
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Truong, Quoc Hung. "Knowledge-based 3D point clouds processing." Phd thesis, Université de Bourgogne, 2013. http://tel.archives-ouvertes.fr/tel-00977434.

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The modeling of real-world scenes through capturing 3D digital data has proven to be both useful andapplicable in a variety of industrial and surveying applications. Entire scenes are generally capturedby laser scanners and represented by large unorganized point clouds possibly along with additionalphotogrammetric data. A typical challenge in processing such point clouds and data lies in detectingand classifying objects that are present in the scene. In addition to the presence of noise, occlusionsand missing data, such tasks are often hindered by the irregularity of the capturing conditions bothwithin the same dataset and from one data set to another. Given the complexity of the underlyingproblems, recent processing approaches attempt to exploit semantic knowledge for identifying andclassifying objects. In the present thesis, we propose a novel approach that makes use of intelligentknowledge management strategies for processing of 3D point clouds as well as identifying andclassifying objects in digitized scenes. Our approach extends the use of semantic knowledge to allstages of the processing, including the guidance of the individual data-driven processing algorithms.The complete solution consists in a multi-stage iterative concept based on three factors: the modeledknowledge, the package of algorithms, and a classification engine. The goal of the present work isto select and guide algorithms following an adaptive and intelligent strategy for detecting objects inpoint clouds. Experiments with two case studies demonstrate the applicability of our approach. Thestudies were carried out on scans of the waiting area of an airport and along the tracks of a railway.In both cases the goal was to detect and identify objects within a defined area. Results show that ourapproach succeeded in identifying the objects of interest while using various data types
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Gasslander, Maja. "Segmentation of Clouds in Satellite Images." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128802.

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The usage of 3D modelling is increasing fast, both for civilian and military areas, such as navigation, targeting and urban planning. When creating a 3D model from satellite images, clouds canbe problematic. Thus, automatic detection ofclouds inthe imagesis ofgreat use. This master thesis was carried out at Vricon, who produces 3D models of the earth from satellite images.This thesis aimed to investigate if Support Vector Machines could classify pixels into cloud or non-cloud, with a combination of texture and color as features. To solve the stated goal, the task was divided into several subproblems, where the first part was to extract features from the images. Then the images were preprocessed before fed to the classifier. After that, the classifier was trained, and finally evaluated.The two methods that gave the best results in this thesis had approximately 95 % correctly classified pixels. This result is better than the existing cloud segmentation method at Vricon, for the tested terrain and cloud types.
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Ruhe, Jakob, and Johan Nordin. "Classification of Points Acquired by Airborne Laser Systems." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10485.

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During several years research has been performed at the Department of Laser Systems, the Swedish Defense Research Agency (FOI), to develop methods to produce high resolution 3D environment models based on data acquired with airborne laser systems. The 3D models are used for several purposes, both military and civilian applications, for example mission planning, crisis management analysis and planning of infrastructure.

We have implemented a new format to store laser point data. Instead of storing rasterized images of the data this new format stores the original location of each point. We have also implemented a new method to detect outliers, methods to estimate the ground surface and also to divide the remaining data into two classes: buildings and vegetation.

It is also shown that it is possible to get more accurate results by analyzing the points directly instead of only using rasterized images and image processing algorithms. We show that these methods can be implemented without increasing the computational complexity.

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Ehrlich, André, Eike Bierwirth, and Manfred Wendisch. "Airborne remote sensing of Arctic boundary-layer mixed-phase clouds." Universität Leipzig, 2010. https://ul.qucosa.de/id/qucosa%3A16357.

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This article gives an overview on the investigations on Artic boundary-layer mixed-phase clouds conducted within the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) in spring 2007. In particular the horizontal and vertical disribution of ice crystals within the clouds was determined by three independent airborne instruments (lidar, in situ and solar radiation measurements). Spectral measurements of cloud top reflectivity have been utilized to retrieve information on the ice phase by analyzing the spectral pattern of the cloud top reflectance in the wavelength range dominated by liquid water and ice absorption (1400-1700 nm). A new algorithm to derive an ice index which distinguishes pure ice, liquid water, and mixed-phase clouds was developed. The horizontal distribution of the ice index, observed during ASTAR 2007, agrees with airborne lidar and in situ measurements showing patches of glaciated clouds at an air mass transition zone within the investigated mixed-phase cloud fields. Information on the vertical distribution of ice crystals in mixed-phase clouds was derived by comparing the measured cloud top reflectivity in the wavelength band 1400-1700 nm to radiative transfer simulations. To interpret the data, the vertical weighting of the measurements was calculated. In the investigated wavelength range the weightings differ according to the spectral absorption of ice and liquid water. From the observed spectral cloud reflectivity with low values in the ice absorption maximum (1400 nm) and higher values at the liquid water absorption maximum (1700 nm) it was concluded that ice crystals were present in the otherwise liquid dominated cloud top layer. Although in situ measurements (limited due to vertical resolution and detection limits) did confirm these findings only in certain limits, the retrieved vertical structure is in agreement with published ground based remote sensing measurements.
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Koch, Dorothy, Yves Balkanski, Susanne E. Bauer, Richard C. Easter, Sylvaine Ferrachat, Steven J. Ghan, Corinna Hoose, et al. "Soot microphysical effects on liquid clouds, a multi-model investigation." Copernicus Publication, 2011. https://ul.qucosa.de/id/qucosa%3A13767.

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We use global models to explore the microphysical effects of carbonaceous aerosols on liquid clouds. Although absorption of solar radiation by soot warms the atmosphere, soot may cause climate cooling due to its contribution to cloud condensation nuclei (CCN) and therefore cloud brightness. Six global models conducted three soot experiments; four of the models had detailed aerosol microphysical schemes. The average cloud radiative response to biofuel soot (black and organic carbon), including both indirect and semi-direct effects, is −0.11Wm−2, comparable in size but opposite in sign to the respective direct effect. In a more idealized fossil fuel black carbon experiment, some models calculated a positive cloud response because soot provides a deposition sink for sulfuric and nitric acids and secondary organics, decreasing nucleation and evolution of viable CCN. Biofuel soot particles were also typically assumed to be larger and more hygroscopic than for fossil fuel soot and therefore caused more negative forcing, as also found in previous studies. Diesel soot (black and organic carbon) experiments had relatively smaller cloud impacts with five of the models <±0.06Wm−2 from clouds. The results are subject to the caveats that variability among models, and regional and interrannual variability for each model, are large. This comparison together with previously published results stresses the need to further constrain aerosol microphysical schemes. The non-linearities resulting from the competition of opposing effects on the CCN population make it difficult to extrapolate from idealized experiments to likely impacts of realistic potential emission changes.
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Cremer, Roxana, Johannes Quaas, and Johannes Mülmenstädt. "Interactions between clouds and sea ice in the Arctic." Universität Leipzig, 2017. https://ul.qucosa.de/id/qucosa%3A16773.

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The feedback between clouds and sea ice got more importance in the last years, because of the declining Arctic sea ice extent. Previous observations show the formation of low clouds over newly formed open water. These low clouds are very important for the Arctic Energy Budget, because they warm the surface. This leads to increasing temperatures and stronger sea ice loss. To assess the relationship between sea ice cover and cloudiness, satellite observations by DARDAR were compared with both global climate reanalyses ERA–Interim and MACC. The analysis focuses on 2007 – 2010 and the relationship between different parameters from the different datasets. It is found that the reanalyses only poorly approximate the cloud cover in the Arctic. Consequently no strong correlation was found for the time period 2007 – 2010.
Das Wolken–Albedo–Feedback in der Arktis gewann in den letzten Jahren immer mehr an Bedeutung aufgrund des Rückganges der Meereisfläche. Vorhergehende Arbeiten zeigten die Bildung von tiefer Bewölkung über kürzlich aufgebrochenen Meereisstellen. Diese tiefen Wolken sind sehr wichtig für das arktische Energiebudget, wegen des Erwärmens der Oberfläche. Daraus folgt ein Anstieg in der bodennahen Temperatur und ein verstärkter Rückgang des Meereises. Um den Einfluss der Meereiskonzentration auf die Wolkenbildung zu untersuchen, werden in dieser Arbeit Satellitendaten von DARDAR mit den beiden globalen Klimareanalysen Era–interim und MACC verglichen. Analysiert werden Daten aus den Jahren 2007 bis 2010 und für verschiedene Oberflächenbedingungen werden Korrelationen der einzelnen Datensätze erstellt. Es hat sich gezeigt, dass die Darstellung der Wolkenbedeckung in der Arktis durch die Reanalyse Daten nicht geeignet ist. Aus diesem Grund wurden keine signifikanten Korrelationen in der Zeitspanne von 2007 bis 2010 gefunden.
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Books on the topic "Clouds Classification"

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Welch, Ronald M. The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset: Semi-annual progress report, period: January-June 1996. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Collins, Michael B. Clovis blade technology: A comparative study of the Keven Davis Cache, Texas. Austin: University of Texas Press, 1999.

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Haynes, Gary. The early settlement of North America: The Clovis era. New York: Cambridge University Press, 2002.

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Thelin, Marta, and Julienne Maheux. Clouds: Classification, Microbiology and Environmental Effects. Nova Science Publishers, Incorporated, 2013.

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Cloud classsification from satellite data using a fuzzy sets algorithm: A polar example. [Washington, DC: National Aeronautics and Space Administration, 1989.

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M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Professor, Baby. Classification of Clouds Atmosphere, Weather and Climate Grade 5 Children's Science Education Books. Speedy Publishing LLC, 2021.

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Professor, Baby. Classification of Clouds Atmosphere, Weather and Climate Grade 5 Children's Science Education Books. Speedy Publishing LLC, 2021.

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Series, Michigan Historical Reprint. Storms: Their nature, classification and laws. With the means of predicting them by their embodiments the clouds. Scholarly Publishing Office, University of Michigan Library, 2005.

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United States. National Aeronautics and Space Administration., ed. TRMM final report for the first three years of NASA grant NAG5-1586: TRMM-related research tropical rainfall and energy analysis experiment. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Book chapters on the topic "Clouds Classification"

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Rangno, Arthur L. "The Classification of Clouds." In Handbook of Weather, Climate, and Water, 387–405. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/0471721603.ch21.

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Briskilal, J., and C. N. Subalalitha. "Classification of Idiomatic Sentences Using AWD-LSTM." In Expert Clouds and Applications, 113–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2126-0_11.

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Dhulavvagol, Praveen M., S. G. Totad, Ashwin Shirodkar, Amulya Hiremath, Apoorva Bansode, and J. Divya. "Performance Analysis of Classification Algorithm Using Stacking and Ensemble Techniques." In Expert Clouds and Applications, 615–29. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2500-9_46.

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Calisse, P., G. Dall’Oglio, M. T. Di Bari, A. Iacoangeli, L. Martinis, P. Merluzzi, L. Piccirillo, L. Pizzo, L. Rossi, and C. Santillo. "MM Observations of the Magellanic Clouds from Antarctica." In Morphological and Physical Classification of Galaxies, 449–50. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2522-2_64.

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Mangla, Aditya, J. Briskilal, and D. Senthil Kumar. "Image Classification of Indian Rural Development Projects Using Transfer Learning and CNN." In Expert Clouds and Applications, 17–29. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2500-9_2.

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Laube, Pascal. "Classification of Geometric Primitives in Point Clouds." In Machine Learning Methods for Reverse Engineering of Defective Structured Surfaces, 97–120. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29017-7_4.

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Kishore Kumar, K., and H. Venkateswerareddy. "A Detailed Survey on Deep Learning Techniques for Real-Time Image Classification, Recognition and Analysis." In Expert Clouds and Applications, 349–60. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2126-0_30.

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Prabha, P. Lakshmi, A. K. Jayanthy, and Kumar Janardanan. "M-mode Carotid Artery Image Classification and Risk Analysis Based on Machine Learning and Deep Learning Techniques." In Expert Clouds and Applications, 675–89. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2500-9_50.

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Waldhauser, Christoph, Ronald Hochreiter, Johannes Otepka, Norbert Pfeifer, Sajid Ghuffar, Karolina Korzeniowska, and Gerald Wagner. "Automated Classification of Airborne Laser Scanning Point Clouds." In Solving Computationally Expensive Engineering Problems, 269–92. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08985-0_12.

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Cholewa, Michał, and Przemysław Sporysz. "Classification of Dynamic Sequences of 3D Point Clouds." In Artificial Intelligence and Soft Computing, 672–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07173-2_57.

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Conference papers on the topic "Clouds Classification"

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Ekhtari, Nima, Craig Glennie, and Juan Carlos Fernandez-Diaz. "Classification of multispectral lidar point clouds." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127568.

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Li, Yingfei, and Huimin Lu. "3D object classification from point clouds." In Seventh International Symposium on Artificial Intelligence and Robotics 2022, edited by Huimin Lu, Jintong Cai, and Yuchao Zheng. SPIE, 2022. http://dx.doi.org/10.1117/12.2658785.

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Paralic, Martin. "Statistical Approach for Sky Clouds Density Classification." In 2020 New Trends in Signal Processing (NTSP). IEEE, 2020. http://dx.doi.org/10.1109/ntsp49686.2020.9229538.

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Stojanovic, Vladeta, Matthias Trapp, Jürgen Döllner, and Rico Richter. "Classification of Indoor Point Clouds Using Multiviews." In Web3D '19: The 24th International Conference on 3D Web Technology. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3329714.3338129.

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Haurum, Joakim, Moaaz Allahham, Mathias Lynge, Kasper Henriksen, Ivan Nikolov, and Thomas Moeslund. "Sewer Defect Classification using Synthetic Point Clouds." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010207908910900.

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Xiao, Xian, Changsheng Xu, and Jinqiao Wang. "Landmark image classification using 3D point clouds." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1873951.1874061.

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Medina, F. Patricia, and Randy Paffenroth. "Classification frameworks comparison on 3D point clouds." In 2021 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2021. http://dx.doi.org/10.1109/hpec49654.2021.9622842.

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Wang, Zhichao, and David Rosen. "Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89307.

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Abstract Given a design part, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate results based on a combination of a convolutional neural network (CNN) and the heat kernel signature (HKS) for triangle mesh. In this paper, we constructed a classification method based on rotation-invariant shape descriptors and a neural network for point clouds, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation-invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provided two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performance was consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that required one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.
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Kishimoto, Tomoe, Masahiro Morinaga, Masahiko Saito, and Junichi Tanaka. "Application of transfer learning to event classification in collider physics." In International Symposium on Grids & Clouds 2022. Trieste, Italy: Sissa Medialab, 2022. http://dx.doi.org/10.22323/1.415.0016.

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Aldeeb, Nader H., and Olaf Hellwich. "Detection and Classification of Holes in Point Clouds." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006296503210330.

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Reports on the topic "Clouds Classification"

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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
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Flynn, Donna, Yan Shi, K.-S. Lim, and Laura Riihimaki. Cloud Type Classification (cldtype) Value-Added Product. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1377405.

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Moyer, Elisabeth, Ian Foster, James Franke, Rob Jacob, Rebecca Willett, and Takuya Kuihana. New Understanding of Cloud Processes via Unsupervised Cloud Classification in Satellite Images. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769754.

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Hawkins, Rupert S., K. F. Heideman, and Ira G. Smotroff. Cloud Data Set for Neural Network Classification Studies. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada256181.

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Wood, Norman. Data product development for cold cloud and precipitation process analysis/Snow regime classifications from the NSA snow product. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1725814.

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Nechypurenko, Pavlo, Tetiana Selivanova, and Maryna Chernova. Using the Cloud-Oriented Virtual Chemical Laboratory VLab in Teaching the Solution of Experimental Problems in Chemistry of 9th Grade Students. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3175.

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The article discusses the importance of the skills of primary school students to solve experimental problems in chemistry and the conditions for the use of virtual chemical laboratories in the process of the formation of these skills. The concept of “experimental chemical problem” was analyzed, classifications were considered, and methodological conditions for using experimental chemical problems in the process of teaching chemistry were described. The essence of the concept of “virtual chemical laboratories” is considered and their main types, advantages and disadvantages that define the methodically reasonable limits of the use of these software products in the process of teaching chemistry, in particular, to support the educational chemical experiment are described. The capabilities of the virtual chemical laboratory VLab to support the process of solving experimental problems in chemistry in grade 9 have been determined. The main advantages and disadvantages of the virtual chemical laboratory VLab on the modeling of chemical processes necessary for the creation of virtual experimental problems in chemistry are analyzed. The features of the virtual chemical laboratory VLab, the essence of its work and the creation of virtual laboratory work in it are described. The results of the study is the development of a set of experimental tasks in chemistry for students in grade 9 on the topic “Solutions” in the cloud-oriented virtual chemical laboratory VLab.
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Kiianovska, N. M. The development of theory and methods of using cloud-based information and communication technologies in teaching mathematics of engineering students in the United States. Видавничий центр ДВНЗ «Криворізький національний університет», December 2014. http://dx.doi.org/10.31812/0564/1094.

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The purpose of the study is the analysis of the development of the theory and methods of ICT usage while teaching higher mathematics engineering students in the United States. It was determined following tasks: to analyze the problem source, to identify the state of its elaboration, to identify key trends in the development of theory and methods of ICT usage while teaching higher mathematics engineering students in the United States, the object of study – the use of ICT in teaching engineering students, the research methods are: analysis of scientific, educational, technical, historical sources; systematization and classification of scientific statements on the study; specification, comparison, analysis and synthesis, historical and pedagogical analysis of the sources to establish the chronological limits and implementation of ICT usage in educational practice of U.S. technical colleges. In article was reviewed a modern ICT tools used in learning of fundamental subjects for future engineers in the United States, shown the evolution and convergence of ICT learning tools. Discussed experience of the «best practices» using online ICT in higher engineering education at United States. Some of these are static, while others are interactive or dynamic, giving mathematics learners opportunities to develop visualization skills, explore mathematical concepts, and obtain solutions to self-selected problems. Among ICT tools are the following: tools to transmit audio and video data, tools to collaborate on projects, tools to support object-oriented practice. The analysis leads to the following conclusion: using cloud-based tools of learning mathematic has become the leading trend today. Therefore, university professors are widely considered to implement tools to assist the process of learning mathematics such properties as mobility, continuity and adaptability.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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Lers, Amnon, E. Lomaniec, S. Burd, A. Khalchitski, L. Canetti, and Pamela J. Green. Analysis of Senescence Inducible Ribonuclease in Tomato: Gene Regulation and Function. United States Department of Agriculture, February 2000. http://dx.doi.org/10.32747/2000.7570563.bard.

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Natural leaf senescence has a negative influence on yield. Postharvest induced senescence contributes to the losses of quality in flowers, foliage, and vegetables. Strategies designed to control the senescence process in crop plants could therefore have great applied significance. Senescence is regulated by differential gene expression yet, functional characterization of the genes specifically induced and study of their expression control, is still in its infancy. Study of senescence-specific genes is required to allow identification of regulatory elements participating in senescence-induced expression and thus provide insights into the genetic regulation of senescence. A main feature of senescence is the hydrolysis of macromolecules by hydrolases of various types such as RNases and proteases. This study was aimed a analysis of senescence-inducible RNases in tomato with the following objectives: Isolation of senescence-inducible RNase cDNA clones; Expression analyses of RNase genes during senescence; Identification of sequences required for senescence-induced gene expression; Functional analyses of senescence-inducible RNases. We narrowed our aims somewhat to focus on the first three objectives because the budget we were awarded was reduced from that requested. We have expanded our research for identification senescence-related RNase/nuclease activities as we thought it will direct us to new RNase/nuclease genes. We have also carried out research in Arabidopsis and parsley, which enabled us to draw mire general conclusions. We completed the first and second objectives and have made considerable progress on the remaining two. We have defined growth conditions suitable for this research and defined the physiological and biochemical parameters characteristic to the advance of leaf senescence. In tomato and arabidopsis we have focused on natural leaf senescence. Parsley was used mainly for study of postharvest senescence in detached leaves. We have identified a 41-kD a tomato nuclease, LeNUCI, specifically induced during senescence which can degrade both RNA and DNA. This activity could be induced by ethylene in young leaves and was subjected to detailed analysis, which enabled its classification as Nuclease I enzyme. LeNUCI may be involved in nucleic acid metabolism during tomato leaf senescence. In parsley senescing leaves we identified 2 main senescence-related nuclease activities of 41 and 39-kDa. These activities were induced in both naturally or artificially senescing leaves, could degrade both DNA and RNA and were very similar in their characteristics to the LeNUCI. Two senescence-induced RNase cDNAs were cloned from tomato. One RNase cDNA was identical to the tomato LX RNase while the second corresponded to the LE RNase. Both were demonstrated before to be induced following phosphate starvation of tomato cell culture but nothing was known about their expression or function in plants. LX gene expression was much more senescence specific and ethylene could activate it in detached young leaves. LE gene expression, which could be transiently induced by wounding, appeared to be activated by abscisic acid. We suggest that the LX RNase has a role in RNA catabolism in the final stage of senescence, and LE may be a defense-related protein. Transgenic plants were generated for altering LX gene expression. No major visible alterations in the phenotype were observed so far. Detailed analysis of senescence in these plants is performed currently. The LX promoter was cloned and its analysis is performed currently for identification of senescence-specific regulatory elements. In Arabidopsis we have identified and characterized a senescence-associated nuclease 1 gene, BFN1, which is highly expressed during leaf and stem senescence. BFN1, is the first example of a senescence- associated gene encoding a nuclease I enzyme as well as the first nuclease I cloned and characterized from Arabidopsis. Our progress should provide excellent tools for the continued analysis of regulation and function of senescence-inducible ribonucleases and nucleases in plants. The cloned genes can be used in reverse genetic approaches, already initiated, which can yield a more direct evidence for the function of these enzymes. Another contribution of this research will be in respect to the molecular mechanism, which controls senescence. We had already initiated in this project and will continue to identify and characterize regulatory elements involved in senescence-specific expression of the genes isolated in this work.
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