Improving the Accuracy of Cloud Detection Using Machine Learning

NASA Astrophysics Data System (ADS)

Craddock, M. E.; Alliss, R. J.; Mason, M.

2017-12-01

Cloud detection from geostationary satellite imagery has long been accomplished through multi-spectral channel differencing in comparison to the Earth's surface. The distinction of clear/cloud is then determined by comparing these differences to empirical thresholds. Using this methodology, the probability of detecting clouds exceeds 90% but performance varies seasonally, regionally and temporally. The Cloud Mask Generator (CMG) database developed under this effort, consists of 20 years of 4 km, 15minute clear/cloud images based on GOES data over CONUS and Hawaii. The algorithms to determine cloudy pixels in the imagery are based on well-known multi-spectral techniques and defined thresholds. These thresholds were produced by manually studying thousands of images and thousands of man-hours to determine the success and failure of the algorithms to fine tune the thresholds. This study aims to investigate the potential of improving cloud detection by using Random Forest (RF) ensemble classification. RF is the ideal methodology to employ for cloud detection as it runs efficiently on large datasets, is robust to outliers and noise and is able to deal with highly correlated predictors, such as multi-spectral satellite imagery. The RF code was developed using Python in about 4 weeks. The region of focus selected was Hawaii and includes the use of visible and infrared imagery, topography and multi-spectral image products as predictors. The development of the cloud detection technique is realized in three steps. First, tuning of the RF models is completed to identify the optimal values of the number of trees and number of predictors to employ for both day and night scenes. Second, the RF models are trained using the optimal number of trees and a select number of random predictors identified during the tuning phase. Lastly, the model is used to predict clouds for an independent time period than used during training and compared to truth, the CMG cloud mask. Initial results show 97% accuracy during the daytime, 94% accuracy at night, and 95% accuracy for all times. The total time to train, tune and test was approximately one week. The improved performance and reduced time to produce results is testament to improved computer technology and the use of machine learning as a more efficient and accurate methodology of cloud detection.

Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements

NASA Astrophysics Data System (ADS)

Wiensz, J. T.; Degenstein, D. A.; Lloyd, N. D.; Bourassa, A. E.

2013-01-01

We present a technique for estimating the optical thickness of subvisual cirrus clouds detected by OSIRIS (Optical Spectrograph and Infrared Imaging System), a limb-viewing satellite instrument that measures scattered radiances from the UV to the near-IR. The measurement set is composed of a ratio of limb radiance profiles at two wavelengths that indicates the presence of cloud-scattering regions. Cross-sections and phase functions from an in situ database are used to simulate scattering by cloud-particles. With appropriate configurations discussed in this paper, the SASKTRAN successive-orders of scatter radiative transfer model is able to simulate accurately the in-cloud radiances from OSIRIS. Configured in this way, the model is used with a multiplicative algebraic reconstruction technique (MART) to retrieve the cloud extinction profile for an assumed effective cloud particle size. The sensitivity of these retrievals to key auxiliary model parameters is shown, and it is shown that the retrieved extinction profile, for an assumed effective cloud particle size, models well the measured in-cloud radiances from OSIRIS. The greatest sensitivity of the retrieved optical thickness is to the effective cloud particle size. Since OSIRIS has an 11-yr record of subvisual cirrus cloud detections, the work described in this manuscript provides a very useful method for providing a long-term global record of the properties of these clouds.

Cloud properties inferred from 8-12 micron data

NASA Technical Reports Server (NTRS)

Strabala, Kathleen I.; Ackerman, Steven A.; Menzel, W. Paul

1994-01-01

A trispectral combination of observations at 8-, 11-, and 12-micron bands is suggested for detecting cloud and cloud properties in the infrared. Atmospheric ice and water vapor absorption peak in opposite halves of the window region so that positive 8-minus-11-micron brightness temperature differences indicate cloud, while near-zero or negative differences indicate clear regions. The absorption coefficient for water increases more between 11 and 12 microns than between 8 and 11 microns, while for ice, the reverse is true. Cloud phases is determined by a scatter diagram of 8-minus-11-micron versus 11-minus-12-micron brightness temperature differences; ice cloud shows a slope greater than 1 and water cloud less than 1. The trispectral brightness temperature method was tested upon high-resolution interferometer data resulting in clear-cloud and cloud-phase delineation. Simulations using differing 8-micron bandwidths revealed no significant degradation of cloud property detection. Thus, the 8-micron bandwidth for future satellites can be selected based on the requirements of other applications, such as surface characterization studies. Application of the technique to current polar-orbiting High-Resolution Infrared Sounder (HIRS)-Advanced Very High Resolution Radiometer (AVHRR) datasets is constrained by the nonuniformity of the cloud scenes sensed within the large HIRS field of view. Analysis of MAS (MODIS Airborne Simulator) high-spatial resolution (500 m) data with all three 8-, 11-, and 12-micron bands revealed sharp delineation of differing cloud and background scenes, from which a simple automated threshold technique was developed. Cloud phase, clear-sky, and qualitative differences in cloud emissivity and cloud height were identified on a case study segment from 24 November 1991, consistent with the scene. More rigorous techniques would allow further cloud parameter clarification. The opportunities for global cloud delineation with the Moderate-Resolution Imaging Spectrometer (MODIS) appear excellent. The spectral selection, the spatial resolution, and the global coverage are all well suited for significant advances.

Near Real Time Detection and Tracking of the EYJAFJÖLL (iceland) Ash Cloud by the RST (robust Satellite Technique) Approach

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Filizzola, C.; Marchese, F.; Paciello, R.; Pergola, N.; Sannazzaro, F.

2010-12-01

Volcanic ash clouds, besides to be an environmental issue, represent a serious problem for air traffic and an important economic threat for aviation companies. During the recent volcanic crisis due to the April-May 2010 eruption of Eyjafjöll (Iceland), ash clouds became a real problem for common citizens as well: during the first days of the eruption thousands of flights were cancelled disrupting hundred of thousands of passengers. Satellite remote sensing confirmed to be a crucial tool for monitoring this kind of events, spreading for thousands of kilometres with a very rapid space-time dynamics. Especially weather satellites, thanks to their high temporal resolution, may furnish a fundamental contribution, providing frequently updated information. However, in this particular case ash cloud was accompanied by a sudden and significant emission of water vapour, due to the ice melting of Eyjafjallajökull glacier, making satellite ash detection and discrimination very hard, especially in the first few days of the eruption, exactly when accurate information were mostly required in order to support emergency management. Among the satellite-based techniques for near real-time detection and tracking of ash clouds, the RST (Robust Satellite Technique) approach, formerly named RAT - Robust AVHRR Technique, has been long since proposed, demonstrating high performances both in terms of reliability and sensitivity. In this paper, results achieved by using RST-based detection schemes, applied during the Eyjafjöll eruption were presented. MSG-SEVIRI (Meteosat Second Generation - Spinning Enhanced and Visible Infrared Imager) records, with a temporal sampling of 15 minutes, were used applying a standard as well as an advanced RST configuration, which includes the use of SO2 absorption band together with TIR and MIR channels. Main outcomes, limits and possible future improvements were also discussed.

Phase-partitioning in mixed-phase clouds - An approach to characterize the entire vertical column

NASA Astrophysics Data System (ADS)

Kalesse, H.; Luke, E. P.; Seifert, P.

2017-12-01

The characterization of the entire vertical profile of phase-partitioning in mixed-phase clouds is a challenge which can be addressed by synergistic profiling measurements with ground-based polarization lidars and cloud radars. While lidars are sensitive to small particles and can thus detect supercooled liquid (SCL) layers, cloud radar returns are dominated by larger particles (like ice crystals). The maximum lidar observation height is determined by complete signal attenuation at a penetrated optical depth of about three. In contrast, cloud radars are able to penetrate multiple liquid layers and can thus be used to expand the identification of cloud phase to the entire vertical column beyond the lidar extinction height, if morphological features in the radar Doppler spectrum can be related to the existence of SCL. Relevant spectral signatures such as bimodalities and spectral skewness can be related to cloud phase by training a neural network appropriately in a supervised learning scheme, with lidar measurements functioning as supervisor. The neural network output (prediction of SCL location) derived using cloud radar Doppler spectra can be evaluated with several parameters such as liquid water path (LWP) detected by microwave radiometer (MWR) and (liquid) cloud base detected by ceilometer or Raman lidar. The technique has been previously tested on data from Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) instruments in Barrow, Alaska and is in this study utilized for observations from the Leipzig Aerosol and Cloud Remote Observations System (LACROS) during the Analysis of the Composition of Clouds with Extended Polarization Techniques (ACCEPT) field experiment in Cabauw, Netherlands in Fall 2014. Comparisons to supercooled-liquid layers as classified by CLOUDNET are provided.

Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar

NASA Astrophysics Data System (ADS)

Wang, Zhe; Wang, Zhenhui; Cao, Xiaozhong; Tao, Fa

2018-01-01

Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds-such as the cloud base height (CBH), CTH and the cloud thickness-and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.

A novel technique for evaluating the volcanic cloud top altitude using GPS Radio Occultation data

NASA Astrophysics Data System (ADS)

Biondi, Riccardo; Corradini, Stefano; Guerrieri, Lorenzo; Merucci, Luca; Stelitano, Dario; Pugnaghi, Sergio

2017-04-01

Volcanic ash and sulfuric gases are a major hazards to aviation since they damage the aircraft engines also at large distance from the eruption. Many challenges given by volcanic explosive eruptions are still discussed and several issues are far from being solved. The cloud top altitude can be detected with different techniques, but the accuracy is still quite coarse. This parameter is important for the air traffic to know what altitude can be ash free, and it assumes a key role for the contribution of the eruption to the climate change. Moreover, the cloud top altitude is also strictly related to the mass ejected by the eruption and represent a key parameter for the ash and SO2 retrievals by using several techniques. The Global Positioning System (GPS) Radio Occultation (RO) technique enables real time measurement of atmospheric density structure in any meteorological condition, in remote areas and during extreme atmospheric events with high vertical resolution and accuracy and this makes the RO an interesting tool for this kind of studies. In this study we have tracked the Eyjafjöll 2010 eruption by using MODIS satellite measurements and retrieved the volcanic cloud top altitudes by using two different procedures exploiting the thermal infrared CO2 absorption bands around 13.4 micrometers. The first approach is a modification of the standard CO2 slicing method while the second is based on look up tables computations. We have then selected all the RO profiles co-located with the volcanic cloud and implemented an algorithm based on the variation of the bending angle for detecting the cloud top altitude with high accuracy. The results of the comparison between the MODIS and RO volcanic height retrievals are encouraging and suggesting that, due to their independence from weather conditions and due to their high vertical resolution, the RO observations can contribute to improved detection and monitoring of volcanic clouds and to support warning systems.

Person detection and tracking with a 360° lidar system

NASA Astrophysics Data System (ADS)

Hammer, Marcus; Hebel, Marcus; Arens, Michael

2017-10-01

Today it is easily possible to generate dense point clouds of the sensor environment using 360° LiDAR (Light Detection and Ranging) sensors which are available since a number of years. The interpretation of these data is much more challenging. For the automated data evaluation the detection and classification of objects is a fundamental task. Especially in urban scenarios moving objects like persons or vehicles are of particular interest, for instance in automatic collision avoidance, for mobile sensor platforms or surveillance tasks. In literature there are several approaches for automated person detection in point clouds. While most techniques show acceptable results in object detection, the computation time is often crucial. The runtime can be problematic, especially due to the amount of data in the panoramic 360° point clouds. On the other hand, for most applications an object detection and classification in real time is needed. The paper presents a proposal for a fast, real-time capable algorithm for person detection, classification and tracking in panoramic point clouds.

Comments on "Failures in detecting volcanic ash from a satellite-based technique"

USGS Publications Warehouse

Prata, F.; Bluth, G.; Rose, B.; Schneider, D.; Tupper, A.

2001-01-01

The recent paper by Simpson et al. [Remote Sens. Environ. 72 (2000) 191.] on failures to detect volcanic ash using the 'reverse' absorption technique provides a timely reminder of the danger that volcanic ash presents to aviation and the urgent need for some form of effective remote detection. The paper unfortunately suffers from a fundamental flaw in its methodology and numerous errors of fact and interpretation. For the moment, the 'reverse' absorption technique provides the best means for discriminating volcanic ash clouds from meteorological clouds. The purpose of our comment is not to defend any particular algorithm; rather, we point out some problems with Simpson et al.'s analysis and re-state the conditions under which the 'reverse' absorption algorithm is likely to succeed. ?? 2001 Elsevier Science Inc. All rights reserved.

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

NASA Technical Reports Server (NTRS)

Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

2015-01-01

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

Determination of Cloud Base Height, Wind Velocity, and Short-Range Cloud Structure Using Multiple Sky Imagers Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Dong; Schwartz, Stephen E.; Yu, Dantong

Clouds are a central focus of the U.S. Department of Energy (DOE)’s Atmospheric System Research (ASR) program and Atmospheric Radiation Measurement (ARM) Climate Research Facility, and more broadly are the subject of much investigation because of their important effects on atmospheric radiation and, through feedbacks, on climate sensitivity. Significant progress has been made by moving from a vertically pointing (“soda-straw”) to a three-dimensional (3D) view of clouds by investing in scanning cloud radars through the American Recovery and Reinvestment Act of 2009. Yet, because of the physical nature of radars, there are key gaps in ARM's cloud observational capabilities. Formore » example, cloud radars often fail to detect small shallow cumulus and thin cirrus clouds that are nonetheless radiatively important. Furthermore, it takes five to twenty minutes for a cloud radar to complete a 3D volume scan and clouds can evolve substantially during this period. Ground-based stereo-imaging is a promising technique to complement existing ARM cloud observation capabilities. It enables the estimation of cloud coverage, height, horizontal motion, morphology, and spatial arrangement over an extended area of up to 30 by 30 km at refresh rates greater than 1 Hz (Peng et al. 2015). With fine spatial and temporal resolution of modern sky cameras, the stereo-imaging technique allows for the tracking of a small cumulus cloud or a thin cirrus cloud that cannot be detected by a cloud radar. With support from the DOE SunShot Initiative, the Principal Investigator (PI)’s team at Brookhaven National Laboratory (BNL) has developed some initial capability for cloud tracking using multiple distinctly located hemispheric cameras (Peng et al. 2015). To validate the ground-based cloud stereo-imaging technique, the cloud stereo-imaging field campaign was conducted at the ARM Facility’s Southern Great Plains (SGP) site in Oklahoma from July 15 to December 24. As shown in Figure 1, the cloud stereo-imaging system consisted of two inexpensive high-definition (HD) hemispheric cameras (each cost less than $1,500) and ARM’s Total Sky Imager (TSI). Together with other co-located ARM instrumentation, the campaign provides a promising opportunity to validate stereo-imaging-based cloud base height and, more importantly, to examine the feasibility of cloud thickness retrieval for low-view-angle clouds.« less

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

NASA Astrophysics Data System (ADS)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

Evaluation of multi-layer cloud detection based on MODIS CO2-slicing algorithm with CALIPSO-CloudSat measurements.

NASA Astrophysics Data System (ADS)

Viudez-Mora, A.; Kato, S.; Smith, W. L., Jr.; Chang, F. L.

2016-12-01

Knowledge of the vertical cloud distribution is important for a variety of climate and weather applications. The cloud overlapping variations greatly influence the atmospheric heating/cooling rates, with implications for the surface-troposphere radiative balance, global circulation and precipitation. Additionally, an accurate knowledge of the multi-layer cloud distribution in real-time can be used in applications such safety condition for aviation through storms and adverse weather conditions. In this study, we evaluate a multi-layered cloud algorithm (Chang et al. 2005) based on MODIS measurements aboard Aqua satellite (MCF). This algorithm uses the CO2-slicing technique combined with cloud properties determined from VIS, IR and NIR channels to locate high thin clouds over low-level clouds, and retrieve the τ of each layer. We use CALIPSO (Winker et. al, 2010) and CloudSat (Stephens et. al, 2002) (CLCS) derived cloud vertical profiles included in the C3M data product (Kato et al. 2010) to evaluate MCF derived multi-layer cloud properties. We focus on 2 layer overlapping and 1-layer clouds identified by the active sensors and investigate how well these systems are identified by the MODIS multi-layer technique. The results show that for these multi-layered clouds identified by CLCS, the MCF correctly identifies about 83% of the cases as multi-layer. However, it is found that the upper CTH is underestimated by about 2.6±0.4 km, because the CO2-slicing technique is not as sensitive to the cloud physical top as the CLCS. The lower CTH agree better with differences found to be about 1.2±0.5 km. Another outstanding issue for the MCF approach is the large number of multi-layer false alarms that occur in single-layer conditions. References: Chang, F.-L., and Z. Li, 2005: A new method for detection of cirrus overlapping water clouds and determination of their optical properties. J. Atmos. Sci., 62. Kato, S., et al. (2010), Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles, J. Geophys. Res., 115. Stephens, G. L., et al. (2002), The CloudSat mission and A-Train, Bull. Am. Meteorol. Soc., 83. Winker, D. M., et al., 2010: The CALIPSO Mission: A global 3D view of aerosols and clouds. Bull. Amer. Meteor. Soc., 91.

Rainbows, polarization, and the search for habitable planets.

PubMed

Bailey, Jeremy

2007-04-01

Current proposals for the characterization of extrasolar terrestrial planets rest primarily on the use of spectroscopic techniques. While spectroscopy is effective in detecting the gaseous components of a planet's atmosphere, it provides no way of detecting the presence of liquid water, the defining characteristic of a habitable planet. In this paper, I investigate the potential of an alternative technique for characterizing the atmosphere of a planet using polarization. By looking for a polarization peak at the "primary rainbow" scattering angle, it is possible to detect the presence of liquid droplets in a planet's atmosphere and constrain the nature of the liquid through its refractive index. Single scattering calculations are presented to show that a well-defined rainbow scattering peak is present over the full range of likely cloud droplet sizes and clearly distinguishes the presence of liquid droplets from solid particles such as ice or dust. Rainbow scattering has been used in the past to determine the nature of the cloud droplets in the Venus atmosphere and by the POLarization and Directionality of Earth Reflectances (POLDER) instrument to distinguish between liquid and ice clouds in the Earth atmosphere. While the presence of liquid water clouds does not guarantee the presence of water at the surface, this technique could complement spectroscopic techniques for characterizing the atmospheres of potential habitable planets. The disk-integrated rainbow peak for Earth is estimated to be at a degree of polarization of 12.7% or 15.5% for two different cloud cover scenarios. The observation of this rainbow peak is shown to be feasible with the proposed Terrestrial Planet Finder Coronograph mission in similar total integration times to those required for spectroscopic characterization.

A new cloud and aerosol layer detection method based on micropulse lidar measurements

NASA Astrophysics Data System (ADS)

Zhao, Chuanfeng; Wang, Yuzhao; Wang, Qianqian; Li, Zhanqing; Wang, Zhien; Liu, Dong

2014-06-01

This paper introduces a new algorithm to detect aerosols and clouds based on micropulse lidar measurements. A semidiscretization processing technique is first used to inhibit the impact of increasing noise with distance. The value distribution equalization method which reduces the magnitude of signal variations with distance is then introduced. Combined with empirical threshold values, we determine if the signal waves indicate clouds or aerosols. This method can separate clouds and aerosols with high accuracy, although differentiation between aerosols and clouds are subject to more uncertainties depending on the thresholds selected. Compared with the existing Atmospheric Radiation Measurement program lidar-based cloud product, the new method appears more reliable and detects more clouds with high bases. The algorithm is applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu sites. At the SGP site, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring and shows bimodal vertical distributions with maximum occurrences at around 3-6 km and 8-12 km. The annual averaged cloud frequency is about 50%. The dominant clouds are stratiform in winter and convective in summer. By contrast, the cloud frequency at the Taihu site shows no clear seasonal variation and the maximum occurrence is at around 1 km. The annual averaged cloud frequency is about 15% higher than that at the SGP site. A seasonal analysis of cloud base occurrence frequency suggests that stratiform clouds dominate at the Taihu site.

Global cloud top height retrieval using SCIAMACHY limb spectra: model studies and first results

NASA Astrophysics Data System (ADS)

Eichmann, Kai-Uwe; Lelli, Luca; von Savigny, Christian; Sembhi, Harjinder; Burrows, John P.

2016-03-01

Cloud top heights (CTHs) are retrieved for the period 1 January 2003 to 7 April 2012 using height-resolved limb spectra measured with the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on board ENVISAT (ENVIronmental SATellite). In this study, we present the retrieval code SCODA (SCIAMACHY cloud detection algorithm) based on a colour index method and test the accuracy of the retrieved CTHs in comparison to other methods. Sensitivity studies using the radiative transfer model SCIATRAN show that the method is capable of detecting cloud tops down to about 5 km and very thin cirrus clouds up to the tropopause. Volcanic particles can be detected that occasionally reach the lower stratosphere. Upper tropospheric ice clouds are observable for a nadir cloud optical thickness (COT) ≥ 0.01, which is in the subvisual range. This detection sensitivity decreases towards the lowermost troposphere. The COT detection limit for a water cloud top height of 5 km is roughly 0.1. This value is much lower than thresholds reported for passive cloud detection methods in nadir-viewing direction. Low clouds at 2 to 3 km can only be retrieved under very clean atmospheric conditions, as light scattering of aerosol particles interferes with the cloud particle scattering. We compare co-located SCIAMACHY limb and nadir cloud parameters that are retrieved with the Semi-Analytical CloUd Retrieval Algorithm (SACURA). Only opaque clouds (τN,c > 5) are detected with the nadir passive retrieval technique in the UV-visible and infrared wavelength ranges. Thus, due to the frequent occurrence of thin clouds and subvisual cirrus clouds in the tropics, larger CTH deviations are detected between both viewing geometries. Zonal mean CTH differences can be as high as 4 km in the tropics. The agreement in global cloud fields is sufficiently good. However, the land-sea contrast, as seen in nadir cloud occurrence frequency distributions, is not observed in limb geometry. Co-located cloud top height measurements of the limb-viewing Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT are compared for the period from January 2008 to March 2012. The global CTH agreement of about 1 km is observed, which is smaller than the vertical field of view of both instruments. Lower stratospheric aerosols from volcanic eruptions occasionally interfere with the cloud retrieval and inhibit the detection of tropospheric clouds. The aerosol impact on cloud retrievals was studied for the volcanoes Kasatochi (August 2008), Sarychev Peak (June 2009), and Nabro (June 2011). Long-lasting aerosol scattering is detected after these events in the Northern Hemisphere for heights above 12.5 km in tropical and polar latitudes. Aerosol top heights up to about 22 km are found in 2009 and the enhanced lower stratospheric aerosol layer persisted for about 7 months. In August 2009 about 82 % of the lower stratosphere between 30 and 70° N was filled with scattering particles and nearly 50 % in October 2008.

Remote Sensing of Multiple Cloud Layer Heights Using Multi-Angular Measurements

NASA Technical Reports Server (NTRS)

Sinclair, Kenneth; Van Diedenhoven, Bastiaan; Cairns, Brian; Yorks, John; Wasilewski, Andrzej; Mcgill, Matthew

2017-01-01

Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASAs airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross-correlations between this set and co-located sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allow retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSPs CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC(exp. 4)RS) campaign. RSP retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 nm and 1880 nm and their combination. The 1880-nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption.

Building Change Detection from LIDAR Point Cloud Data Based on Connected Component Analysis

NASA Astrophysics Data System (ADS)

Awrangjeb, M.; Fraser, C. S.; Lu, G.

2015-08-01

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

Comparison of monthly nighttime cloud fraction products from MODIS and AIRS and ground-based camera over Manila Observatory (14.64N, 121.07E)

NASA Astrophysics Data System (ADS)

Gacal, G. F. B.; Lagrosas, N.

2017-12-01

Cloud detection nowadays is primarily achieved by the utilization of various sensors aboard satellites. These include MODIS Aqua, MODIS Terra, and AIRS with products that include nighttime cloud fraction. Ground-based instruments are, however, only secondary to these satellites when it comes to cloud detection. Nonetheless, these ground-based instruments (e.g., LIDARs, ceilometers, and sky-cameras) offer significant datasets about a particular region's cloud cover values. For nighttime operations of cloud detection instruments, satellite-based instruments are more reliably and prominently used than ground-based ones. Therefore if a ground-based instrument for nighttime operations is operated, it ought to produce reliable scientific datasets. The objective of this study is to do a comparison between the results of a nighttime ground-based instrument (sky-camera) and that of MODIS Aqua and MODIS Terra. A Canon Powershot A2300 is placed ontop of Manila Observatory (14.64N, 121.07E) and is configured to take images of the night sky at 5min intervals. To detect pixels with clouds, the pictures are converted to grayscale format. Thresholding technique is used to screen pixels with cloud and pixels without clouds. If the pixel value is greater than 17, it is considered as a cloud; otherwise, a noncloud (Gacal et al., 2016). This algorithm is applied to the data gathered from Oct 2015 to Oct 2016. A scatter plot between satellite cloud fraction in the area covering the area 14.2877N, 120.9869E, 14.7711N and 121.4539E and ground cloud cover is graphed to find the monthly correlation. During wet season (June - November), the satellite nighttime cloud fraction vs ground measured cloud cover produce an acceptable R2 (Aqua= 0.74, Terra= 0.71, AIRS= 0.76). However, during dry season, poor R2 values are obtained (AIRS= 0.39, Aqua & Terra = 0.01). The high correlation during wet season can be attributed to a high probability that the camera and satellite see the same clouds. However during dry season, the satellite sees high altitude clouds and the camera can not detect these clouds from the ground as it relies on city lights reflected from low level clouds. With this acknowledged disparity, the ground-based camera has the advantage of detecting haze and thin clouds near the ground that are hardly or not detected by the satellites.

Characterization of the visibility of wildfire smoke clouds

NASA Astrophysics Data System (ADS)

de Vries, Jan S.; den Breejen, Eric

1993-09-01

In order to investigate the smoke cloud visibility of small wildfires a series of controlled biomass burning experiments has been carried out to investigate the characteristics of smoke clouds using various remote sensing techniques. These techniques include simultaneous scattering and transmission measurements in four wavelength bands, near-, mid-, and far- infrared video imagery, high resolution Fourier spectrometry, and particle size distribution measurements. The characterization and, in particular, knowledge on the contrast of smoke from small, beginning wildfires against a vegetation background is required in order to predict the performance of autonomous surveillance systems. This paper describes the preliminary analysis of experiments which have been carried out in Ypenburg (the Netherlands) in 1992. The results of these experiments are used to estimate the wildfire detection efficiency of a demonstration sensor which is being developed in a project financed by the Commission of the European Communities and by Bosschap. The autonomous wildfire detection sensor is described.

Concept, Simulation, and Instrumentation for Radiometric Inflight Icing Detection

NASA Technical Reports Server (NTRS)

Ryerson, Charles; Koenig, George G.; Reehorst, Andrew L.; Scott, Forrest R.

2009-01-01

The multi-agency Flight in Icing Remote Sensing Team (FIRST), a consortium of the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), the National Center for Atmospheric Research (NCAR), the National Oceanographic and Atmospheric Administration (NOAA), and the Army Corps of Engineers (USACE), has developed technologies for remotely detecting hazardous inflight icing conditions. The USACE Cold Regions Research and Engineering Laboratory (CRREL) assessed the potential of onboard passive microwave radiometers for remotely detecting icing conditions ahead of aircraft. The dual wavelength system differences the brightness temperature of Space and clouds, with greater differences potentially indicating closer and higher magnitude cloud liquid water content (LWC). The Air Force RADiative TRANsfer model (RADTRAN) was enhanced to assess the flight track sensing concept, and a 'flying' RADTRAN was developed to simulate a radiometer system flying through simulated clouds. Neural network techniques were developed to invert brightness temperatures and obtain integrated cloud liquid water. In addition, a dual wavelength Direct-Detection Polarimeter Radiometer (DDPR) system was built for detecting hazardous drizzle drops. This paper reviews technology development to date and addresses initial polarimeter performance.

Electromagnetic Methods of Lightning Detection

NASA Astrophysics Data System (ADS)

Rakov, V. A.

2013-11-01

Both cloud-to-ground and cloud lightning discharges involve a number of processes that produce electromagnetic field signatures in different regions of the spectrum. Salient characteristics of measured wideband electric and magnetic fields generated by various lightning processes at distances ranging from tens to a few hundreds of kilometers (when at least the initial part of the signal is essentially radiation while being not influenced by ionospheric reflections) are reviewed. An overview of the various lightning locating techniques, including magnetic direction finding, time-of-arrival technique, and interferometry, is given. Lightning location on global scale, when radio-frequency electromagnetic signals are dominated by ionospheric reflections, is also considered. Lightning locating system performance characteristics, including flash and stroke detection efficiencies, percentage of misclassified events, location accuracy, and peak current estimation errors, are discussed. Both cloud and cloud-to-ground flashes are considered. Representative examples of modern lightning locating systems are reviewed. Besides general characterization of each system, the available information on its performance characteristics is given with emphasis on those based on formal ground-truth studies published in the peer-reviewed literature.

Detecting High Ice Water Content Cloud Regions Using Airborne and Satellite Observations

NASA Astrophysics Data System (ADS)

Kheyrollah Pour, H.; Korolev, A.; Barker, H.; Wolde, M.; Heckman, I.; Duguay, C. R.

2016-12-01

Tropical mesoscale convective systems (MCS) have significant impacts on local and global hydrological cycles and radiation budgets. Moreover, high ice water content (HIWC) found inside MCS clouds at altitudes above 7 km have been identified as hazardous for aviation safety. The environment inside HIWC cloud regions may cause icing of aircraft engines resulting in uncontrolled engine power loss or damage. This phenomenon is known as ice crystal icing (ICI). International aviation regulatory agencies are now attempting to define techniques that enable prediction and detection of potential ICI environments. Such techniques range from on-board HIWC detection to nowcasting of ice crystal weather using satellite data and numerical weather prediction models. The most practical way to monitor continuously for areas of HIWC is by remote sensing with passive radiometers on geostationary satellites. Establishing correlations between HIWC cloud regions and radiances is, however, a challenging problem. This is because regions of HIWC can occur several kilometers below cloud top, while passive satellite radiometers response mainly to the upper kilometers of MCS clouds. The High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) field campaigns in Cayenne, French Guiana collected a rich dataset from aboard the Canadian NRC Convair-580 that was equipped with a suite of in-situ microphysical instruments and Dopplerized W- and X-band radars with vertically- and horizontally-directed antenna. This paper aims to describe an algorithm that has been developed to establish relationships between satellite radiances and locations of HIWC regions identified from in-situ measurements of microphysical properties, Doppler velocities, and vertical and horizontal radar reflectivity.

Cloud-point detection using a portable thickness shear mode crystal resonator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mansure, A.J.; Spates, J.J.; Germer, J.W.

1997-08-01

The Thickness Shear Mode (TSM) crystal resonator monitors the crude oil by propagating a shear wave into the oil. The coupling of the shear wave and the crystal vibrations is a function of the viscosity of the oil. By driving the crystal with circuitry that incorporates feedback, it is possible to determine the change from Newtonian to non-Newtonian viscosity at the cloud point. A portable prototype TSM Cloud Point Detector (CPD) has performed flawlessly during field and lab tests proving the technique is less subjective or operator dependent than the ASTM standard. The TSM CPD, in contrast to standard viscositymore » techniques, makes the measurement in a closed container capable of maintaining up to 100 psi. The closed container minimizes losses of low molecular weight volatiles, allowing samples (25 ml) to be retested with the addition of chemicals. By cycling/thermal soaking the sample, the effects of thermal history can be investigated and eliminated as a source of confusion. The CPD is portable, suitable for shipping the field offices for use by personnel without special training or experience in cloud point measurements. As such, it can make cloud point data available without the delays and inconvenience of sending samples to special labs. The crystal resonator technology can be adapted to in-line monitoring of cloud point and deposition detection.« less

Determination of cloud fields from analysis of HIRS2/MSU sounding data. [20 channel infrared and 4 channel microwave atmospheric sounders

NASA Technical Reports Server (NTRS)

Susskind, J.; Reuter, D.

1986-01-01

IR and microwave remote sensing data collected with the HIRS2 and MSU sensors on the NOAA polar-orbiting satellites were evaluated for their effectiveness as bases for determining the cloud cover and cloud physical characteristics. Techniques employed to adjust for day-night alterations in the radiance fields are described, along with computational procedures applied to compare scene pixel values with reference values for clear skies. Sample results are provided for the mean cloud coverage detected over South America and Africa June 1979, with attention given to concurrent surface pressure and cloud top pressure values.

Discrimination of Biomass Burning Smoke and Clouds in MAIAC Algorithm

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Korkin, S.; Wang, Y.; Quayle, B.; Laszlo, I.

2012-01-01

The multi-angle implementation of atmospheric correction (MAIAC) algorithm makes aerosol retrievals from MODIS data at 1 km resolution providing information about the fine scale aerosol variability. This information is required in different applications such as urban air quality analysis, aerosol source identification etc. The quality of high resolution aerosol data is directly linked to the quality of cloud mask, in particular detection of small (sub-pixel) and low clouds. This work continues research in this direction, describing a technique to detect small clouds and introducing the smoke test to discriminate the biomass burning smoke from the clouds. The smoke test relies on a relative increase of aerosol absorption at MODIS wavelength 0.412 micrometers as compared to 0.47-0.67 micrometers due to multiple scattering and enhanced absorption by organic carbon released during combustion. This general principle has been successfully used in the OMI detection of absorbing aerosols based on UV measurements. This paper provides the algorithm detail and illustrates its performance on two examples of wildfires in US Pacific North-West and in Georgia/Florida of 2007.

Analysis of diffential absorption lidar technique for measurements of anhydrous hydrogen chloride from solid rocket motors using a deuterium fluoride laser

NASA Technical Reports Server (NTRS)

Bair, C. H.; Allario, F.

1977-01-01

An active optical technique (differential absorption lidar (DIAL)) for detecting, ranging, and quantifying the concentration of anhydrous HCl contained in the ground cloud emitted by solid rocket motors (SRM) is evaluated. Results are presented of an experiment in which absorption coefficients of HCl were measured for several deuterium fluoride (DF) laser transitions demonstrating for the first time that a close overlap exists between the 2-1 P(3) vibrational transition of the DF laser and the 1-0 P(6) absorption line of HCl, with an absorption coefficient of 5.64 (atm-cm) to the -1 power. These measurements show that the DF laser can be an appropriate radiation source for detecting HCl in a DIAL technique. Development of a mathematical computer model to predict the sensitivity of DIAL for detecting anhydrous HCl in the ground cloud is outlined, and results that assume a commercially available DF laser as the radiation source are presented.

Infrared cloud imaging in support of Earth-space optical communication.

PubMed

Nugent, Paul W; Shaw, Joseph A; Piazzolla, Sabino

2009-05-11

The increasing need for high data return from near-Earth and deep-space missions is driving a demand for the establishment of Earth-space optical communication links. These links will require a nearly obstruction-free path to the communication platform, so there is a need to measure spatial and temporal statistics of clouds at potential ground-station sites. A technique is described that uses a ground-based thermal infrared imager to provide continuous day-night cloud detection and classification according to the cloud optical depth and potential communication channel attenuation. The benefit of retrieving cloud optical depth and corresponding attenuation is illustrated through measurements that identify cloudy times when optical communication may still be possible through thin clouds.

Massive Gas Cloud Around Jupiter

NASA Technical Reports Server (NTRS)

2003-01-01

An innovative instrument on NASA's Cassini spacecraft makes the space environment around Jupiter visible, revealing a donut-shaped gas cloud encircling the planet.

The image was taken with the energetic neutral atom imaging technique by the Magnetospheric Imaging Instrument on Cassini as the spacecraft flew past Jupiter in early 2001 at a distance of about 10 million kilometers (6 million miles). This technique provides information about a source by detecting neutral atoms emitted by the source, comparable to how a camera reveals information about an object by detecting photons coming from the object.

The central object in this image represents energetic neutral atom emissions from Jupiter itself. The outer two objects represent emissions from a donut-shaped cloud, or torus, that shares an orbit with Jupiter's moon Europa. The cloud's emissions appear dot-like because of the viewing angle. The torus is viewed edge-on, and the image is brightest at the line-of-sight angles that pass through the greatest volume of it.

Cassini is a cooperative project of NASA, the European Space Agency and the Italian Space Agency. The Jet Propulsion Laboratory, a division of the California Institute of Technology in Pasadena, Calif., manages Cassini for NASA's Office of Space Science, Washington, D.C.

Techniques for the measurements of the line of sight velocity of high altitude Barium clouds

NASA Technical Reports Server (NTRS)

Mende, S. B.

1981-01-01

It is demonstrated that for maximizing the scientific output of future ion cloud release experiments a new type of instrument is required which will measure the line of sight velocity of the ion cloud by the Doppler Technique. A simple instrument was constructed using a 5 cm diameter solid Fabry-Perot etalon coupled to a low light level integrating television camera. It was demonstrated that the system has both the sensitivity and spectral resolution for the detection of ion clouds and the measurement of their line of sight Doppler velocity. The tests consisted of (1) a field experiment using a rocket barium cloud release to check the sensitivity, (2) laboratory experiments to show the spectral resolving capabilities of the system. The instrument was found to be operational if the source was brighter than about 1 kilorayleigh and it had a wavelength resolution much better than .2A which corresponds to about 12 km/sec or an acceleration potential of 100 volts.

Modeling the performance of direct-detection Doppler lidar systems including cloud and solar background variability.

PubMed

McGill, M J; Hart, W D; McKay, J A; Spinhirne, J D

1999-10-20

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar system: the double-edge and the multichannel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only approximately 10-20% compared with nighttime performance, provided that a proper solar filter is included in the instrument design.

Static Memory Deduplication for Performance Optimization in Cloud Computing.

PubMed

Jia, Gangyong; Han, Guangjie; Wang, Hao; Yang, Xuan

2017-04-27

In a cloud computing environment, the number of virtual machines (VMs) on a single physical server and the number of applications running on each VM are continuously growing. This has led to an enormous increase in the demand of memory capacity and subsequent increase in the energy consumption in the cloud. Lack of enough memory has become a major bottleneck for scalability and performance of virtualization interfaces in cloud computing. To address this problem, memory deduplication techniques which reduce memory demand through page sharing are being adopted. However, such techniques suffer from overheads in terms of number of online comparisons required for the memory deduplication. In this paper, we propose a static memory deduplication (SMD) technique which can reduce memory capacity requirement and provide performance optimization in cloud computing. The main innovation of SMD is that the process of page detection is performed offline, thus potentially reducing the performance cost, especially in terms of response time. In SMD, page comparisons are restricted to the code segment, which has the highest shared content. Our experimental results show that SMD efficiently reduces memory capacity requirement and improves performance. We demonstrate that, compared to other approaches, the cost in terms of the response time is negligible.

Static Memory Deduplication for Performance Optimization in Cloud Computing

PubMed Central

Jia, Gangyong; Han, Guangjie; Wang, Hao; Yang, Xuan

2017-01-01

In a cloud computing environment, the number of virtual machines (VMs) on a single physical server and the number of applications running on each VM are continuously growing. This has led to an enormous increase in the demand of memory capacity and subsequent increase in the energy consumption in the cloud. Lack of enough memory has become a major bottleneck for scalability and performance of virtualization interfaces in cloud computing. To address this problem, memory deduplication techniques which reduce memory demand through page sharing are being adopted. However, such techniques suffer from overheads in terms of number of online comparisons required for the memory deduplication. In this paper, we propose a static memory deduplication (SMD) technique which can reduce memory capacity requirement and provide performance optimization in cloud computing. The main innovation of SMD is that the process of page detection is performed offline, thus potentially reducing the performance cost, especially in terms of response time. In SMD, page comparisons are restricted to the code segment, which has the highest shared content. Our experimental results show that SMD efficiently reduces memory capacity requirement and improves performance. We demonstrate that, compared to other approaches, the cost in terms of the response time is negligible. PMID:28448434

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Matthews, Bryan L.

2011-01-01

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Optical cloud detection from a disposable airborne sensor

NASA Astrophysics Data System (ADS)

Nicoll, Keri; Harrison, R. Giles; Brus, David

2016-04-01

In-situ measurement of cloud droplet microphysical properties is most commonly made from manned aircraft platforms due to the size and weight of the instrumentation, which is both costly and typically limited to sampling only a few clouds. This work describes the development of a small, lightweight (<200g), disposable, optical cloud sensor which is designed for use on routine radiosonde balloon flights and also small unmanned aerial vehicle (UAV) platforms. The sensor employs the backscatter principle, using an ultra-bright LED as the illumination source, with a photodiode detector. Scattering of the LED light by cloud droplets generates a small optical signal which is separated from background light fluctuations using a lock-in technique. The signal to noise obtained permits cloud detection using the scattered LED light, even in daytime. During recent field tests in Pallas, Finland, the retrieved optical sensor signal has been compared with the DMT Cloud and Aerosol Spectrometer (CAS) which measures cloud droplets in the size range from 0.5 to 50 microns. Both sensors were installed at the hill top observatory of Sammaltunturi during a field campaign in October and November 2015, which experienced long periods of immersion inside cloud. Preliminary analysis shows very good agreement between the CAPS and the disposable cloud sensor for cloud droplets >5micron effective diameter. Such data and calibration of the sensor will be discussed here, as will simultaneous balloon launches of the optical cloud sensor through the same cloud layers.

Ice crystal characterization in cirrus clouds: a sun-tracking camera system and automated detection algorithm for halo displays

NASA Astrophysics Data System (ADS)

Forster, Linda; Seefeldner, Meinhard; Wiegner, Matthias; Mayer, Bernhard

2017-07-01

Halo displays in the sky contain valuable information about ice crystal shape and orientation: e.g., the 22° halo is produced by randomly oriented hexagonal prisms while parhelia (sundogs) indicate oriented plates. HaloCam, a novel sun-tracking camera system for the automated observation of halo displays is presented. An initial visual evaluation of the frequency of halo displays for the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) field campaign from October to mid-November 2014 showed that sundogs were observed more often than 22° halos. Thus, the majority of halo displays was produced by oriented ice crystals. During the campaign about 27 % of the cirrus clouds produced 22° halos, sundogs or upper tangent arcs. To evaluate the HaloCam observations collected from regular measurements in Munich between January 2014 and June 2016, an automated detection algorithm for 22° halos was developed, which can be extended to other halo types as well. This algorithm detected 22° halos about 2 % of the time for this dataset. The frequency of cirrus clouds during this time period was estimated by co-located ceilometer measurements using temperature thresholds of the cloud base. About 25 % of the detected cirrus clouds occurred together with a 22° halo, which implies that these clouds contained a certain fraction of smooth, hexagonal ice crystals. HaloCam observations complemented by radiative transfer simulations and measurements of aerosol and cirrus cloud optical thickness (AOT and COT) provide a possibility to retrieve more detailed information about ice crystal roughness. This paper demonstrates the feasibility of a completely automated method to collect and evaluate a long-term database of halo observations and shows the potential to characterize ice crystal properties.

Evaluation of wind field statistics near and inside clouds using a coherent Doppler lidar

NASA Astrophysics Data System (ADS)

Lottman, Brian Todd

1998-09-01

This work proposes advanced techniques for measuring the spatial wind field statistics near and inside clouds using a vertically pointing solid state coherent Doppler lidar on a fixed ground based platform. The coherent Doppler lidar is an ideal instrument for high spatial and temporal resolution velocity estimates. The basic parameters of lidar are discussed, including a complete statistical description of the Doppler lidar signal. This description is extended to cases with simple functional forms for aerosol backscatter and velocity. An estimate for the mean velocity over a sensing volume is produced by estimating the mean spectra. There are many traditional spectral estimators, which are useful for conditions with slowly varying velocity and backscatter. A new class of estimators (novel) is introduced that produces reliable velocity estimates for conditions with large variations in aerosol backscatter and velocity with range, such as cloud conditions. Performance of traditional and novel estimators is computed for a variety of deterministic atmospheric conditions using computer simulated data. Wind field statistics are produced for actual data for a cloud deck, and for multi- layer clouds. Unique results include detection of possible spectral signatures for rain, estimates for the structure function inside a cloud deck, reliable velocity estimation techniques near and inside thin clouds, and estimates for simple wind field statistics between cloud layers.

Ubiquity and impact of thin mid-level clouds in the tropics

PubMed Central

Bourgeois, Quentin; Ekman, Annica M. L.; Igel, Matthew R.; Krejci, Radovan

2016-01-01

Clouds are crucial for Earth's climate and radiation budget. Great attention has been paid to low, high and vertically thick tropospheric clouds such as stratus, cirrus and deep convective clouds. However, much less is known about tropospheric mid-level clouds as these clouds are challenging to observe in situ and difficult to detect by remote sensing techniques. Here we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite observations to show that thin mid-level clouds (TMLCs) are ubiquitous in the tropics. Supported by high-resolution regional model simulations, we find that TMLCs are formed by detrainment from convective clouds near the zero-degree isotherm. Calculations using a radiative transfer model indicate that tropical TMLCs have a cooling effect on climate that could be as large in magnitude as the warming effect of cirrus. We conclude that more effort has to be made to understand TMLCs, as their influence on cloud feedbacks, heat and moisture transport, and climate sensitivity could be substantial. PMID:27530236

Ubiquity and impact of thin mid-level clouds in the tropics.

PubMed

Bourgeois, Quentin; Ekman, Annica M L; Igel, Matthew R; Krejci, Radovan

2016-08-17

Clouds are crucial for Earth's climate and radiation budget. Great attention has been paid to low, high and vertically thick tropospheric clouds such as stratus, cirrus and deep convective clouds. However, much less is known about tropospheric mid-level clouds as these clouds are challenging to observe in situ and difficult to detect by remote sensing techniques. Here we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite observations to show that thin mid-level clouds (TMLCs) are ubiquitous in the tropics. Supported by high-resolution regional model simulations, we find that TMLCs are formed by detrainment from convective clouds near the zero-degree isotherm. Calculations using a radiative transfer model indicate that tropical TMLCs have a cooling effect on climate that could be as large in magnitude as the warming effect of cirrus. We conclude that more effort has to be made to understand TMLCs, as their influence on cloud feedbacks, heat and moisture transport, and climate sensitivity could be substantial.

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Tovinkere, Vasanth; Titlow, James; Baum, Bryan A.

1996-01-01

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

All-sky photogrammetry techniques to georeference a cloud field

NASA Astrophysics Data System (ADS)

Crispel, Pierre; Roberts, Gregory

2018-01-01

In this study, we present a novel method of identifying and geolocalizing cloud field elements from a portable all-sky camera stereo network based on the ground and oriented towards zenith. The methodology is mainly based on stereophotogrammetry which is a 3-D reconstruction technique based on triangulation from corresponding stereo pixels in rectified images. In cases where clouds are horizontally separated, identifying individual positions is performed with segmentation techniques based on hue filtering and contour detection algorithms. Macroscopic cloud field characteristics such as cloud layer base heights and velocity fields are also deduced. In addition, the methodology is fitted to the context of measurement campaigns which impose simplicity of implementation, auto-calibration, and portability. Camera internal geometry models are achieved a priori in the laboratory and validated to ensure a certain accuracy in the peripheral parts of the all-sky image. Then, stereophotogrammetry with dense 3-D reconstruction is applied with cameras spaced 150 m apart for two validation cases. The first validation case is carried out with cumulus clouds having a cloud base height at 1500 m a.g.l. The second validation case is carried out with two cloud layers: a cumulus fractus layer with a base height at 1000 m a.g.l. and an altocumulus stratiformis layer with a base height of 2300 m a.g.l. Velocity fields at cloud base are computed by tracking image rectangular patterns through successive shots. The height uncertainty is estimated by comparison with a Vaisala CL31 ceilometer located on the site. The uncertainty on the horizontal coordinates and on the velocity field are theoretically quantified by using the experimental uncertainties of the cloud base height and camera orientation. In the first cumulus case, segmentation of the image is performed to identify individuals clouds in the cloud field and determine the horizontal positions of the cloud centers.

Clouds over the summertime Sahara: an evaluation of Met Office retrievals from Meteosat Second Generation using airborne remote sensing

NASA Astrophysics Data System (ADS)

Kealy, John C.; Marenco, Franco; Marsham, John H.; Garcia-Carreras, Luis; Francis, Pete N.; Cooke, Michael C.; Hocking, James

2017-05-01

Novel methods of cloud detection are applied to airborne remote sensing observations from the unique Fennec aircraft dataset, to evaluate the Met Office-derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) satellite. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height (CTH), and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent that is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud-contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. The mean cloud field, derived from the satellite cloud mask acquired during the Fennec flights, shows that areas of high surface albedo and orography are preferred sites for Saharan cloud cover, consistent with published theories. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust. The results of the CTH analysis presented here may also have further-reaching implications for the techniques employed by other satellite applications facilities across the world.

Recent Progress in the Remote Detection of Vapours and Gaseous Pollutants.

ERIC Educational Resources Information Center

Moffat, A. J.; And Others

Work has been continuing on the correlation spectrometry techniques described at previous remote sensing symposiums. Advances in the techniques are described which enable accurate quantitative measurements of diffused atmospheric gases to be made using controlled light sources, accurate quantitative measurements of gas clouds relative to…

Determination of Cd in urine by cloud point extraction-tungsten coil atomic absorption spectrometry.

PubMed

Donati, George L; Pharr, Kathryn E; Calloway, Clifton P; Nóbrega, Joaquim A; Jones, Bradley T

2008-09-15

Cadmium concentrations in human urine are typically at or below the 1 microgL(-1) level, so only a handful of techniques may be appropriate for this application. These include sophisticated methods such as graphite furnace atomic absorption spectrometry and inductively coupled plasma mass spectrometry. While tungsten coil atomic absorption spectrometry is a simpler and less expensive technique, its practical detection limits often prohibit the detection of Cd in normal urine samples. In addition, the nature of the urine matrix often necessitates accurate background correction techniques, which would add expense and complexity to the tungsten coil instrument. This manuscript describes a cloud point extraction method that reduces matrix interference while preconcentrating Cd by a factor of 15. Ammonium pyrrolidinedithiocarbamate and Triton X-114 are used as complexing agent and surfactant, respectively, in the extraction procedure. Triton X-114 forms an extractant coacervate surfactant-rich phase that is denser than water, so the aqueous supernatant is easily removed leaving the metal-containing surfactant layer intact. A 25 microL aliquot of this preconcentrated sample is placed directly onto the tungsten coil for analysis. The cloud point extraction procedure allows for simple background correction based either on the measurement of absorption at a nearby wavelength, or measurement of absorption at a time in the atomization step immediately prior to the onset of the Cd signal. Seven human urine samples are analyzed by this technique and the results are compared to those found by the inductively coupled plasma mass spectrometry analysis of the same samples performed at a different institution. The limit of detection for Cd in urine is 5 ngL(-1) for cloud point extraction tungsten coil atomic absorption spectrometry. The accuracy of the method is determined with a standard reference material (toxic metals in freeze-dried urine) and the determined values agree with the reported levels at the 95% confidence level.

Ultraviolet Satellite Measurements of Volcanic Ash. Chapter 12

NASA Technical Reports Server (NTRS)

Carn, S. A.; Krotkov, N. A.

2016-01-01

Ultraviolet (UV) remote sensing of volcanic ash and other absorbing aerosols from space began with the launch of the first Total Ozone Mapping Spectrometer (TOMS) instrument in 1978. Subsequent UV satellite missions (TOMS, GOME, SCIAMACHY, OMI, GOME-2, OMPS) have extended UV ash measurements to the present, generating a unique multidecadal record. A UV Aerosol Index (UVAI) based on two near-UV wavelengths, equally applicable to multispectral (TOMS, DSCOVR) or hyperspectral (GOME, SCIAMACHY, OMI, GOME-2, OMPS) instruments, has been used to derive a unique absorbing aerosol climatology across multiple UV satellite missions. Advantages of UV ash measurements relative to infrared (IR) techniques include the ability to detect ash at any altitude (assuming no clouds), above clouds, and over bright surfaces, where visible and IR techniques may fail. Disadvantages include the daytime-only restriction and nonspecificity to silicate ash, since UV measurements are sensitive to any UV-absorbing aerosol, including smoke, desert dust, and pollution. However, simultaneous retrieval of sulfur dioxide (SO2) abundance and UVAI provides robust discrimination of volcanic clouds. Although the UVAI is only semiquantitative, it has proved successful at detecting and tracking volcanic ash clouds from many volcanic eruptions since 1978. NASA A-Train measurements since 2006 (eg, CALIOP) have provided much improved constraints on volcanic ash altitude, and also permit identification of aerosol type through sensor synergy. Quantitative UV retrievals of ash optical depth, effective particle size, and ash column mass are possible and require assumptions of ash refractive index, particle size distribution, and ash layer altitude. The lack of extensive ash refractive index data in the UV-visible and the effects of ash particle shape on retrievals introduce significant uncertainty in the retrieved parameters, although limited validation against IR ash retrievals has been successful. In this contribution, we review UV ash detection and retrieval techniques and provide examples of volcanic eruptions detected in the approx. 37 year data record.

Lightning Prediction using Electric Field Measurements Associated with Convective Events at a Tropical Location

NASA Astrophysics Data System (ADS)

Jana, S.; Chakraborty, R.; Maitra, A.

2017-12-01

Nowcasting of lightning activities during intense convective events using a single electric field monitor (EFM) has been carried out at a tropical location, Kolkata (22.65oN, 88.45oE). Before and at the onset of heavy lightning, certain changes of electric field (EF) can be related to high liquid water content (LWC) and low cloud base height (CBH). The present study discusses the utility of EF observation to show a few aspects of convective events. Large convective cloud showed by high LWC and low CBH can be detected from EF variation which could be a precursor of upcoming convective events. Suitable values of EF gradient can be used as an indicator of impending lightning events. An EF variation of 0.195 kV/m/min can predict lightning within 17.5 km radius with a probability of detection (POD) of 91% and false alarm rate (FAR) of 8% with a lead time of 45 min. The total number of predicted lightning strikes is nearly 9 times less than that measured by the lightning detector. This prediction technique can, therefore, give an estimate of cloud to ground (CG) and intra cloud (IC) lighting occurrences within the surrounding area. This prediction technique involving POD, FAR and lead time information shows a better prediction capability compared to the techniques reported earlier. Thus an EFM can be effectively used for prediction of lightning events at a tropical location.

Hyperspectrally-Resolved Surface Emissivity Derived Under Optically Thin Clouds

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Strow, L. Larrabee; Yang, Ping

2010-01-01

Surface spectral emissivity derived from current and future satellites can and will reveal critical information about the Earth s ecosystem and land surface type properties, which can be utilized as a means of long-term monitoring of global environment and climate change. Hyperspectrally-resolved surface emissivities are derived with an algorithm utilizes a combined fast radiative transfer model (RTM) with a molecular RTM and a cloud RTM accounting for both atmospheric absorption and cloud absorption/scattering. Clouds are automatically detected and cloud microphysical parameters are retrieved; and emissivity is retrieved under clear and optically thin cloud conditions. This technique separates surface emissivity from skin temperature by representing the emissivity spectrum with eigenvectors derived from a laboratory measured emissivity database; in other words, using the constraint as a means for the emissivity to vary smoothly across atmospheric absorption lines. Here we present the emissivity derived under optically thin clouds in comparison with that under clear conditions.

Lost in Virtual Reality: Pathfinding Algorithms Detect Rock Fractures and Contacts in Point Clouds

NASA Astrophysics Data System (ADS)

Thiele, S.; Grose, L.; Micklethwaite, S.

2016-12-01

UAV-based photogrammetric and LiDAR techniques provide high resolution 3D point clouds and ortho-rectified photomontages that can capture surface geology in outstanding detail over wide areas. Automated and semi-automated methods are vital to extract full value from these data in practical time periods, though the nuances of geological structures and materials (natural variability in colour and geometry, soft and hard linkage, shadows and multiscale properties) make this a challenging task. We present a novel method for computer assisted trace detection in dense point clouds, using a lowest cost path solver to "follow" fracture traces and lithological contacts between user defined end points. This is achieved by defining a local neighbourhood network where each point in the cloud is linked to its neighbours, and then using a least-cost path algorithm to search this network and estimate the trace of the fracture or contact. A variety of different algorithms can then be applied to calculate the best fit plane, produce a fracture network, or map properties such as roughness, curvature and fracture intensity. Our prototype of this method (Fig. 1) suggests the technique is feasible and remarkably good at following traces under non-optimal conditions such as variable-shadow, partial occlusion and complex fracturing. Furthermore, if a fracture is initially mapped incorrectly, the user can easily provide further guidance by defining intermediate waypoints. Future development will include optimization of the algorithm to perform well on large point clouds and modifications that permit the detection of features such as step-overs. We also plan on implementing this approach in an interactive graphical user environment.

Preliminary Analysis of X-Band and Ka-Band Radar for Use in the Detection of Icing Conditions Aloft

NASA Technical Reports Server (NTRS)

Reehorst, Andrew L.; Koenig, George G.

2004-01-01

NASA and the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) have an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. Radar has been identified as a strong tool for this work. However, since the remote detection of icing conditions with the intent to identify areas of icing hazard is a new and evolving capability, there are no set requirements for radar sensitivity. This work is an initial attempt to quantify, through analysis, the sensitivity requirements for an icing remote sensing radar. The primary radar of interest for cloud measurements is Ka-band, however, since NASA is currently using an X-band unit, this frequency is also examined. Several aspects of radar signal analysis were examined. Cloud reflectivity was calculated for several forms of cloud using two different techniques. The Air Force Geophysical Laboratory (AFGL) cloud models, with different drop spectra represented by a modified gamma distribution, were utilized to examine several categories of cloud formation. Also a fundamental methods approach was used to allow manipulation of the cloud droplet size spectra. And an analytical icing radar simulator was developed to examine the complete radar system response to a configurable multi-layer cloud environment. Also discussed is the NASA vertical pointing X-band radar. The radar and its data system are described, and several summer weather events are reviewed.

A survey of laser lightning rod techniques

NASA Technical Reports Server (NTRS)

Barnes, Arnold A., Jr.; Berthel, Robert O.

1991-01-01

The work done to create a laser lightning rod (LLR) is discussed. Some ongoing research which has the potential for achieving an operational laser lightning rod for use in the protection of missile launch sites, launch vehicles, and other property is discussed. Because of the ease with which a laser beam can be steered into any cloud overhead, an LLR could be used to ascertain if there exists enough charge in the clouds to discharge to the ground as triggered lightning. This leads to the possibility of using LLRs to test clouds prior to launching missiles through the clouds or prior to flying aircraft through the clouds. LLRs could also be used to probe and discharge clouds before or during any hazardous ground operations. Thus, an operational LLR may be able to both detect such sub-critical electrical fields and effectively neutralize them.

Full-Time, Eye-Safe Cloud and Aerosol Lidar Observation at Atmospheric Radiation Measurement Program Sites: Instruments and Data Analysis

NASA Technical Reports Server (NTRS)

Campbell, James R.; Hlavka, Dennis L.; Welton, Ellsworth J.; Flynn, Connor J.; Turner, David D.; Spinhirne, James D.; Scott, V. Stanley, III; Hwang, I. H.; Einaudi, Franco (Technical Monitor)

2001-01-01

Atmospheric radiative forcing, surface radiation budget, and top of the atmosphere radiance interpretation involves a knowledge of the vertical height structure of overlying cloud and aerosol layers. During the last decade, the U.S. Department of Energy through I the Atmospheric Radiation Measurement (ARM) program has constructed four long- term atmospheric observing sites in strategic climate regimes (north central Oklahoma, In Barrow. Alaska, and Nauru and Manus Islands in the tropical western Pacific). Micro Pulse Lidar (MPL) systems provide continuous, autonomous observation of all significant atmospheric cloud and aerosol at each of the central ARM facilities. Systems are compact and transmitted pulses are eye-safe. Eye-safety is achieved by expanding relatively low-powered outgoing Pulse energy through a shared, coaxial transmit/receive telescope. ARM NIPL system specifications, and specific unit optical designs are discussed. Data normalization and calibration techniques are presented. A multiple cloud boundary detection algorithm is also described. These techniques in tandem represent an operational value added processing package used to produce normalized data products for Cloud and aerosol research and the historical ARM data archive.

Cirrus and Polar Stratospheric Cloud Studies using CLAES Data

NASA Technical Reports Server (NTRS)

Mergenthaler, John L.; Douglass, A. (Technical Monitor)

2001-01-01

We've concluded a 3 year (Period of Performance- January 21, 1998 to February 28, 2001) study of cirrus and polar stratospheric clouds using CLAES (Cryogenic Limb Array Etalon Spectrometer) data. We have described the progress of this study in monthly reports, UARS (Upper Atmosphere Research Satellite) science team meetings, American Geophysical Society Meetings, refereed publications and collaborative publications. Work undertaken includes the establishment of CLAES cloud detection criteria, the refinement of CLAES temperature retrieval techniques, compare the findings of CLAES with those of other instruments, and present findings to the larger community. This report describes the progress made in these areas.

Correction of thin cirrus effects in AVIRIS images using the sensitive 1.375-micron cirrus detecting channel

NASA Technical Reports Server (NTRS)

Gao, Bo-Cai; Kaufman, Yorman J.

1995-01-01

Using spectral imaging data acquired with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) from an ER-2 aircraft at 20 km altitude during various field programs, it was found that narrow channels near the center of the strong 1.38-micrometer water vapor band are very effective in detecting think cirrus clouds. Based on this observation from AVIRIS data, Gao and Kaufman proposed to put a channel centered at 1.375 micrometers with a width of 30 nm on the Moderate Resolution Imaging Spectrometer (MODIS) for remote sensing of cirrus clouds from space. The sensitivity of the 1.375-micrometer MODIS channel to detect thin cirrus clouds during the day time is expected to be one to two orders of magnitude better than the current infrared emission techniques. As a result, much larger fraction of the satellite data is expected to be identified as being covered by cirrus clouds, some of them so thin that their obscuration of the surface is very small. In order to make better studies of surface reflectance properties, thin cirrus effects must be removed from satellite images. Therefore, there is a need to study radiative properties of thin cirrus clouds, so that a strategy for correction or removal of the thin cirrus effects, similar to the correction of atmospheric aerosol effect, can be formed. In this extended abstract, we describe an empirical approach for removing/correcting thin cirrus effects in AVIRIS images using channels near 1.375 microns - one step beyond the detection of cirrus clouds using these channels.

Radiative Flux Analysis

DOE Data Explorer

Long, Chuck [NOAA

2008-05-14

The Radiative Flux Analysis is a technique for using surface broadband radiation measurements for detecting periods of clear (i.e. cloudless) skies, and using the detected clear-sky data to fit functions which are then used to produce continuous clear-sky estimates. The clear-sky estimates and measurements are then used in various ways to infer cloud macrophysical properties.

Automated cloud classification using a ground based infra-red camera and texture analysis techniques

NASA Astrophysics Data System (ADS)

Rumi, Emal; Kerr, David; Coupland, Jeremy M.; Sandford, Andrew P.; Brettle, Mike J.

2013-10-01

Clouds play an important role in influencing the dynamics of local and global weather and climate conditions. Continuous monitoring of clouds is vital for weather forecasting and for air-traffic control. Convective clouds such as Towering Cumulus (TCU) and Cumulonimbus clouds (CB) are associated with thunderstorms, turbulence and atmospheric instability. Human observers periodically report the presence of CB and TCU clouds during operational hours at airports and observatories; however such observations are expensive and time limited. Robust, automatic classification of cloud type using infrared ground-based instrumentation offers the advantage of continuous, real-time (24/7) data capture and the representation of cloud structure in the form of a thermal map, which can greatly help to characterise certain cloud formations. The work presented here utilised a ground based infrared (8-14 μm) imaging device mounted on a pan/tilt unit for capturing high spatial resolution sky images. These images were processed to extract 45 separate textural features using statistical and spatial frequency based analytical techniques. These features were used to train a weighted k-nearest neighbour (KNN) classifier in order to determine cloud type. Ground truth data were obtained by inspection of images captured simultaneously from a visible wavelength colour camera at the same installation, with approximately the same field of view as the infrared device. These images were classified by a trained cloud observer. Results from the KNN classifier gave an encouraging success rate. A Probability of Detection (POD) of up to 90% with a Probability of False Alarm (POFA) as low as 16% was achieved.

Low cost digital photogrammetry: From the extraction of point clouds by SFM technique to 3D mathematical modeling

NASA Astrophysics Data System (ADS)

Michele, Mangiameli; Giuseppe, Mussumeci; Salvatore, Zito

2017-07-01

The Structure From Motion (SFM) is a technique applied to a series of photographs of an object that returns a 3D reconstruction made up by points in the space (point clouds). This research aims at comparing the results of the SFM approach with the results of a 3D laser scanning in terms of density and accuracy of the model. The experience was conducted by detecting several architectural elements (walls and portals of historical buildings) both with a 3D laser scanner of the latest generation and an amateur photographic camera. The point clouds acquired by laser scanner and those acquired by the photo camera have been systematically compared. In particular we present the experience carried out on the "Don Diego Pappalardo Palace" site in Pedara (Catania, Sicily).

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

PubMed

Elshazly, Hatem; Souilmi, Yassine; Tonellato, Peter J; Wall, Dennis P; Abouelhoda, Mohamed

2017-01-20

Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Developing and Evaluating RGB Composite MODIS Imagery for Applications in National Weather Service Forecast Offices

NASA Technical Reports Server (NTRS)

Oswald, Hayden; Molthan, Andrew L.

2011-01-01

Satellite remote sensing has gained widespread use in the field of operational meteorology. Although raw satellite imagery is useful, several techniques exist which can convey multiple types of data in a more efficient way. One of these techniques is multispectral compositing. The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed two multispectral satellite imagery products which utilize data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA's Terra and Aqua satellites, based upon products currently generated and used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The nighttime microphysics product allows users to identify clouds occurring at different altitudes, but emphasizes fog and low cloud detection. This product improves upon current spectral difference and single channel infrared techniques. Each of the current products has its own set of advantages for nocturnal fog detection, but each also has limiting drawbacks which can hamper the analysis process. The multispectral product combines each current product with a third channel difference. Since the final image is enhanced with color, it simplifies the fog identification process. Analysis has shown that the nighttime microphysics imagery product represents a substantial improvement to conventional fog detection techniques, as well as provides a preview of future satellite capabilities to forecasters.

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

NASA Astrophysics Data System (ADS)

Li, Y. H.; Shinohara, T.; Satoh, T.; Tachibana, K.

2016-06-01

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

Cloud cover estimation optical package: New facility, algorithms and techniques

NASA Astrophysics Data System (ADS)

Krinitskiy, Mikhail

2017-02-01

Short- and long-wave radiation is an important component of surface heat budget over sea and land. For estimating them accurate observations of the cloud cover are needed. While massively observed visually, for building accurate parameterizations cloud cover needs also to be quantified using precise instrumental measurements. Major disadvantages of the most of existing cloud-cameras are associated with their complicated design and inaccuracy of post-processing algorithms which typically result in the uncertainties of 20% to 30% in the camera-based estimates of cloud cover. The accuracy of these types of algorithm in terms of true scoring compared to human-observed values is typically less than 10%. We developed new generation package for cloud cover estimating, which provides much more accurate results and also allows for measuring additional characteristics. New algorithm, namely SAIL GrIx, based on routine approach, also developed for this package. It uses the synthetic controlling index ("grayness rate index") which allows to suppress the background sunburn effect. This makes it possible to increase the reliability of the detection of the optically thin clouds. The accuracy of this algorithm in terms of true scoring became 30%. One more approach, namely SAIL GrIx ML, we have used to increase the cloud cover estimating accuracy is the algorithm that uses machine learning technique along with some other signal processing techniques. Sun disk condition appears to be a strong feature in this kind of models. Artificial Neural Networks type of model demonstrates the best quality. This model accuracy in terms of true scoring increases up to 95,5%. Application of a new algorithm lets us to modify the design of the optical sensing package and to avoid the use of the solar trackers. This made the design of the cloud camera much more compact. New cloud-camera has already been tested in several missions across Atlantic and Indian oceans on board of IORAS research vessels.

Low Clouds and Fog Characterization over Iberian Peninsula using Meteosat Second Generation Images

NASA Astrophysics Data System (ADS)

Sánchez, Beatriz; Maqueda, Gregorio

2014-05-01

Fog is defined as a collection of suspended water droplets or ice crystals in the air near the Earth's surface that lead to a reduction of horizontal visibility below 1 km (National Oceanic and Atmospheric Administration, 1995). Fog is a stratiform cloud with similar radiative characteristics, for this reason the difference between fog and low stratus clouds is of little importance for remote sensing applications. Fog and low clouds are important atmospheric phenomena, mainly because of their impact on traffic safety and air quality, acting as an obstruction to traffic at land, sea and in the air. The purpose of this work is to develop the method of fog/low clouds detection and analysis on nighttime using Meteosat Second Generation data. This study is focused on the characterization of these atmospheric phenomena in different study cases over the Iberian Peninsula with distinct orography. Firstly, fog/low clouds detection is implemented as a composition of three infrared channels 12.0, 10.8 and 3.9 µm from SEVIRI radiometer on board European geostationary satellite Meteosat (Meteosat-9). The algorithm of detection makes use of a combination of these channels and their differences by creating RGB composites images. On this way, it displays the spatial coverage and location of fog entities. Secondly, this technique allows separating pixels which are indicated as fog/low clouds from clear pixels, assessing the properties of individual pixels using appropriated thresholds of brightness temperature. Thus, it achieves a full analysis of the extent and distribution of fog and its evolution over time. The results of this study have been checked by using ground-based point measurements available as METAR data. Despite the flaws in this sort of inter-comparison approach, the outcome produces to accurate fog/low clouds detection. This work encompasses the way to obtain spatial information from this atmospheric phenomenon by means of satellite imagery.

Arctic PBL Cloud Height and Motion Retrievals from MISR and MINX

NASA Technical Reports Server (NTRS)

Wu, Dong L.

2012-01-01

How Arctic clouds respond and feedback to sea ice loss is key to understanding of the rapid climate change seen in the polar region. As more open water becomes available in the Arctic Ocean, cold air outbreaks (aka. off-ice flow from polar lows) produce a vast sheet of roll clouds in the planetary boundary layer (PBl). The cold air temperature and wind velocity are the critical parameters to determine and understand the PBl structure formed under these roll clouds. It has been challenging for nadir visible/IR sensors to detect Arctic clouds due to lack of contrast between clouds and snowy/icy surfaces. In addition) PBl temperature inversion creates a further problem for IR sensors to relate cloud top temperature to cloud top height. Here we explore a new method with the Multiangle Imaging Spectro-Radiometer (MISR) instrument to measure cloud height and motion over the Arctic Ocean. Employing a stereoscopic-technique, MISR is able to measure cloud top height accurately and distinguish between clouds and snowy/icy surfaces with the measured height. We will use the MISR INteractive eXplorer (MINX) to quantify roll cloud dynamics during cold-air outbreak events and characterize PBl structures over water and over sea ice.

Microseismic techniques for avoiding induced seismicity during fluid injection

DOE PAGES

Matzel, Eric; White, Joshua; Templeton, Dennise; ...

2014-01-01

The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterization phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.

DISCRIMINATING BETWEEN CLOUDY, HAZY, AND CLEAR SKY EXOPLANETS USING REFRACTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Misra, Amit K.; Meadows, Victoria S.

2014-11-01

We propose a method to distinguish between cloudy, hazy, and clear sky (free of clouds and hazes) exoplanet atmospheres that could be applicable to upcoming large aperture space- and ground-based telescopes such as the James Webb Space Telescope (JWST) and the European Extremely Large Telescope (E-ELT). These facilities will be powerful tools for characterizing transiting exoplanets, but only after a considerable amount of telescope time is devoted to a single planet. A technique that could provide a relatively rapid means of identifying haze-free targets (which may be more valuable targets for characterization) could potentially increase the science return for thesemore » telescopes. Our proposed method utilizes broadband observations of refracted light in the out-of-transit spectrum. Light refracted through an exoplanet atmosphere can lead to an increase of flux prior to ingress and subsequent to egress. Because this light is transmitted at pressures greater than those for typical cloud and haze layers, the detection of refracted light could indicate a cloud- or haze-free atmosphere. A detection of refracted light could be accomplished in <10 hr for Jovian exoplanets with JWST and <5 hr for super-Earths/mini-Neptunes with E-ELT. We find that this technique is most effective for planets with equilibrium temperatures between 200 and 500 K, which may include potentially habitable planets. A detection of refracted light for a potentially habitable planet would strongly suggest the planet was free of a global cloud or haze layer, and therefore a promising candidate for follow-up observations.« less

Automatic detection of zebra crossings from mobile LiDAR data

NASA Astrophysics Data System (ADS)

Riveiro, B.; González-Jorge, H.; Martínez-Sánchez, J.; Díaz-Vilariño, L.; Arias, P.

2015-07-01

An algorithm for the automatic detection of zebra crossings from mobile LiDAR data is developed and tested to be applied for road management purposes. The algorithm consists of several subsequent processes starting with road segmentation by performing a curvature analysis for each laser cycle. Then, intensity images are created from the point cloud using rasterization techniques, in order to detect zebra crossing using the Standard Hough Transform and logical constrains. To optimize the results, image processing algorithms are applied to the intensity images from the point cloud. These algorithms include binarization to separate the painting area from the rest of the pavement, median filtering to avoid noisy points, and mathematical morphology to fill the gaps between the pixels in the border of white marks. Once the road marking is detected, its position is calculated. This information is valuable for inventorying purposes of road managers that use Geographic Information Systems. The performance of the algorithm has been evaluated over several mobile LiDAR strips accounting for a total of 30 zebra crossings. That test showed a completeness of 83%. Non-detected marks mainly come from painting deterioration of the zebra crossing or by occlusions in the point cloud produced by other vehicles on the road.

Atmospheric Polarization Imaging with Variable Aerosols, Clouds, and Surface Albedo

DTIC Science & Technology

2013-07-01

but partly supported by AFOSR polarization funds); 6. Mr. Gavin Lommatsch – undergraduate student developing NIR polarimetry ; 7. Ms. Elizabeth...grant: 1. J. S. Tyo, D. B. Chenault, J. A. Shaw, D. H. Goldstein, “Techniques in Imaging Polarimetry ,” Chapter 18 in D. H. Goldstein, Polarized Light...A. Barta, J. Gal, B. Suhai, and O. Haiman, “Ground-based full-sky imaging polarimetry of rapidly skies and its use for polarimetric cloud detection

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images

NASA Astrophysics Data System (ADS)

Lück, W.; van Niekerk, A.

2016-05-01

Image compositing is a multi-objective optimization process. Its goal is to produce a seamless cloud and artefact-free artificial image. This is achieved by aggregating image observations and by replacing poor and cloudy data with good observations from imagery acquired within the timeframe of interest. This compositing process aims to minimise the visual artefacts which could result from different radiometric properties, caused by atmospheric conditions, phenologic patterns and land cover changes. It has the following requirements: (1) image compositing must be cloud free, which requires the detection of clouds and shadows, and (2) the image composite must be seamless, minimizing artefacts and visible across inter image seams. This study proposes a new rule-based compositing technique (RBC) that combines the strengths of several existing methods. A quantitative and qualitative evaluation is made of the RBC technique by comparing it to the maximum NDVI (MaxNDVI), minimum red (MinRed) and maximum ratio (MaxRatio) compositing techniques. A total of 174 Landsat TM and ETM+ images, covering three study sites and three different timeframes for each site, are used in the evaluation. A new set of quantitative/qualitative evaluation techniques for compositing quality measurement was developed and showed that the RBC technique outperformed all other techniques, with MaxRatio, MaxNDVI, and MinRed techniques in order of performance from best to worst.

Lidar detection algorithm for time and range anomalies.

PubMed

Ben-David, Avishai; Davidson, Charles E; Vanderbeek, Richard G

2007-10-10

A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t(1) to t(2)" is addressed, and for range anomaly where the question "is a target present at time t within ranges R(1) and R(2)" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO(2) lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.

Improvements in AVHRR Daytime Cloud Detection Over the ARM NSA Site

NASA Technical Reports Server (NTRS)

Chakrapani, V.; Spangenberg, D. A.; Doelling, D. R.; Minnis, P.; Trepte, Q. Z.; Arduini, R. F.

2001-01-01

Clouds play an important role in the radiation budget over Arctic and Antarctic. Because of limited surface observing capabilities, it is necessary to detect clouds over large areas using satellite imagery. At low and mid-latitudes, satellite-observed visible (VIS; 0.65 micrometers) and infrared (IR; 11 micrometers) radiance data are used to derive cloud fraction, temperature, and optical depth. However, the extreme variability in the VIS surface albedo makes the detection of clouds from satellite a difficult process in polar regions. The IR data often show that the surface is nearly the same temperature or even colder than clouds, further complicating cloud detection. Also, the boundary layer can have large areas of haze, thin fog, or diamond dust that are not seen in standard satellite imagery. Other spectral radiances measured by satellite imagers provide additional information that can be used to more accurately discriminate clouds from snow and ice. Most techniques currently use a fixed reflectance or temperature threshold to decide between clouds and clear snow. Using a subjective approach, Minnis et al. (2001) found that the clear snow radiance signatures vary as a function of viewing and illumination conditions as well as snow condition. To routinely process satellite imagery over polar regions with an automated algorithm, it is necessary to account for this angular variability and the change in the background reflectance as snow melts, vegetation grows over land, and melt ponds form on pack ice. This paper documents the initial satellite-based cloud product over the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site at Barrow for use by the modeling community. Cloud amount and height are determined subjectively using an adaptation of the methodology of Minnis et al. (2001) and the radiation fields arc determined following the methods of Doelling et al. (2001) as applied to data taken during the Surface Heat and Energy Budget of the Arctic (SHEBA). The procedures and data produced in this empirically based analysis will also facilitate the development of the automated algorithm for future processing of satellite data over the ARM NSA domain. Results are presented for May, June, and July 1998. ARM surface data are use to partially validate the results taken directly over the ARM site.

Cloud Properties and Radiative Heating Rates for TWP

DOE Data Explorer

Comstock, Jennifer

2013-11-07

A cloud properties and radiative heating rates dataset is presented where cloud properties retrieved using lidar and radar observations are input into a radiative transfer model to compute radiative fluxes and heating rates at three ARM sites located in the Tropical Western Pacific (TWP) region. The cloud properties retrieval is a conditional retrieval that applies various retrieval techniques depending on the available data, that is if lidar, radar or both instruments detect cloud. This Combined Remote Sensor Retrieval Algorithm (CombRet) produces vertical profiles of liquid or ice water content (LWC or IWC), droplet effective radius (re), ice crystal generalized effective size (Dge), cloud phase, and cloud boundaries. The algorithm was compared with 3 other independent algorithms to help estimate the uncertainty in the cloud properties, fluxes, and heating rates (Comstock et al. 2013). The dataset is provided at 2 min temporal and 90 m vertical resolution. The current dataset is applied to time periods when the MMCR (Millimeter Cloud Radar) version of the ARSCL (Active Remotely-Sensed Cloud Locations) Value Added Product (VAP) is available. The MERGESONDE VAP is utilized where temperature and humidity profiles are required. Future additions to this dataset will utilize the new KAZR instrument and its associated VAPs.

Thin Cloud Detection Method by Linear Combination Model of Cloud Image

NASA Astrophysics Data System (ADS)

Liu, L.; Li, J.; Wang, Y.; Xiao, Y.; Zhang, W.; Zhang, S.

2018-04-01

The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

Search for water and life's building blocks in the Universe: An Introduction

NASA Astrophysics Data System (ADS)

Kwok, Sun

Water and organics are commonly believed to be the essential ingredients for life on Earth. The development of infrared and submillimeter observational techniques has resulted in the detection of water in circumstellar envelopes, interstellar clouds, comets, asteroids, planetary satellites and the Sun. Complex organics have also been found in stellar ejecta, diffuse and molecular clouds, meteorites, interplanetary dust particles, comets and planetary satellites. In this Focus Meeting, we will discuss the origin, distribution, and detection of water and other life's building blocks both inside and outside of the Solar System. The possibility of extraterrestrial organics and water on the origin of life on Earth will also be discussed.

Machine learning based cloud mask algorithm driven by radiative transfer modeling

NASA Astrophysics Data System (ADS)

Chen, N.; Li, W.; Tanikawa, T.; Hori, M.; Shimada, R.; Stamnes, K. H.

2017-12-01

Cloud detection is a critically important first step required to derive many satellite data products. Traditional threshold based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and have difficulty over snow/ice covered areas. With the advance of computational power and machine learning techniques, we have developed a new algorithm based on a neural network classifier driven by extensive radiative transfer modeling. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over mid-latitude snow covered areas. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors.

Cloud radiative properties and aerosol - cloud interaction

NASA Astrophysics Data System (ADS)

Viviana Vladutescu, Daniela; Gross, Barry; Li, Clement; Han, Zaw

2015-04-01

The presented research discusses different techniques for improvement of cloud properties measurements and analysis. The need for these measurements and analysis arises from the high errors noticed in existing methods that are currently used in retrieving cloud properties and implicitly cloud radiative forcing. The properties investigated are cloud fraction (cf) and cloud optical thickness (COT) measured with a suite of collocated remote sensing instruments. The novel approach makes use of a ground based "poor man's camera" to detect cloud and sky radiation in red, green, and blue with a high spatial resolution of 30 mm at 1km. The surface-based high resolution photography provides a new and interesting view of clouds. As the cloud fraction cannot be uniquely defined or measured, it depends on threshold and resolution. However as resolution decreases, cloud fraction tends to increase if the threshold is below the mean, and vice versa. Additionally cloud fractal dimension also depends on threshold. Therefore these findings raise concerns over the ability to characterize clouds by cloud fraction or fractal dimension. Our analysis indicate that Principal Component analysis may lead to a robust means of quantifying cloud contribution to radiance. The cloud images are analyzed in conjunction with a collocated CIMEL sky radiometer, Microwave Radiometer and LIDAR to determine homogeneity and heterogeneity. Additionally, MFRSR measurements are used to determine the cloud radiative properties as a validation tool to the results obtained from the other instruments and methods. The cloud properties to be further studied are aerosol- cloud interaction, cloud particle radii, and vertical homogeneity.

Background Characterization Techniques For Pattern Recognition Applications

NASA Astrophysics Data System (ADS)

Noah, Meg A.; Noah, Paul V.; Schroeder, John W.; Kessler, Bernard V.; Chernick, Julian A.

1989-08-01

The Department of Defense has a requirement to investigate technologies for the detection of air and ground vehicles in a clutter environment. The use of autonomous systems using infrared, visible, and millimeter wave detectors has the potential to meet DOD's needs. In general, however, the hard-ware technology (large detector arrays with high sensitivity) has outpaced the development of processing techniques and software. In a complex background scene the "problem" is as much one of clutter rejection as it is target detection. The work described in this paper has investigated a new, and innovative, methodology for background clutter characterization, target detection and target identification. The approach uses multivariate statistical analysis to evaluate a set of image metrics applied to infrared cloud imagery and terrain clutter scenes. The techniques are applied to two distinct problems: the characterization of atmospheric water vapor cloud scenes for the Navy's Infrared Search and Track (IRST) applications to support the Infrared Modeling Measurement and Analysis Program (IRAMMP); and the detection of ground vehicles for the Army's Autonomous Homing Munitions (AHM) problems. This work was sponsored under two separate Small Business Innovative Research (SBIR) programs by the Naval Surface Warfare Center (NSWC), White Oak MD, and the Army Material Systems Analysis Activity at Aberdeen Proving Ground MD. The software described in this paper will be available from the respective contract technical representatives.

Pseudorandom Noise Code-Based Technique for Thin Cloud Discrimination with CO2 and O2 Absorption Measurements

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Prasad, Narasimha S.; Flood, Michael A.

2011-01-01

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a pseudo noise (PN) code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths.

Estimation of Cloud Fraction Profile in Shallow Convection Using a Scanning Cloud Radar

DOE PAGES

Oue, Mariko; Kollias, Pavlos; North, Kirk W.; ...

2016-10-18

Large spatial heterogeneities in shallow convection result in uncertainties in estimations of domain-averaged cloud fraction profiles (CFP). This issue is addressed using large eddy simulations of shallow convection over land coupled with a radar simulator. Results indicate that zenith profiling observations are inadequate to provide reliable CFP estimates. Use of Scanning Cloud Radar (SCR), performing a sequence of cross-wind horizon-to-horizon scans, is not straightforward due to the strong dependence of radar sensitivity to target distance. An objective method for estimating domain-averaged CFP is proposed that uses observed statistics of SCR hydrometeor detection with height to estimate optimum sampling regions. Thismore » method shows good agreement with the model CFP. Results indicate that CFP estimates require more than 35 min of SCR scans to converge on the model domain average. Lastly, the proposed technique is expected to improve our ability to compare model output with cloud radar observations in shallow cumulus cloud conditions.« less

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

PubMed Central

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed “cloud computing”) has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows. PMID:23248640

Now and next-generation sequencing techniques: future of sequence analysis using cloud computing.

PubMed

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed "cloud computing") has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.

Cloud Detection by Fusing Multi-Scale Convolutional Features

NASA Astrophysics Data System (ADS)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

Refinements to HIRS CO2 Slicing Algorithm with Results Compared to CALIOP and MODIS

NASA Astrophysics Data System (ADS)

Frey, R.; Menzel, P.

2012-12-01

This poster reports on the refinement of a cloud top property algorithm using High-resolution Infrared Radiation Sounder (HIRS) measurements. The HIRS sensor has been flown on fifteen satellites from TIROS-N through NOAA-19 and MetOp-A forming a continuous 30 year cloud data record. Cloud Top Pressure and effective emissivity (cloud fraction multiplied by cloud emissivity) are derived using the 15 μm spectral bands in the CO2 absorption band, implementing the CO2 slicing technique which is strong for high semi-transparent clouds but weak for low clouds with little thermal contrast from clear skies. We report on algorithm adjustments suggested from MODIS cloud record validations and the inclusion of collocated AVHRR cloud fraction data from the PATMOS-x algorithm. Reprocessing results for 2008 are shown using NOAA-18 HIRS and collocated CALIOP data for validation, as well as comparisons to MODIS monthly mean values. Adjustments to the cloud algorithm include (a) using CO2 slicing for all ice and mixed phase clouds and infrared window determinations for all water clouds, (b) determining the cloud top pressure from the most opaque CO2 spectral band pair seeing the cloud, (c) reducing the cloud detection threshold for the CO2 slicing algorithm to include conditions of smaller radiance differences that are often due to thin ice clouds, and (d) identifying stratospheric clouds when an opaque band is warmer than a less opaque band.

CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

NASA Technical Reports Server (NTRS)

Nowottnick, E. P.; Yorks, J. E.; Selmer, P. A.; Palm, S. P.; Hlavka, D. L.; Pauly, R. M.; Ozog, S.; McGill, M. J.; Da Silva, A.

2017-01-01

The Cloud Aerosol Transport System (CATS) lidar has been operating onboard the International Space Station (ISS) since February 2015 and provides vertical observations of clouds and aerosols using total attenuated backscatter and depolarization measurements. From February March 2015, CATS operated in Mode 1, providing backscatter and depolarization measurements at 532 and 1064 nm. CATS began operation in Mode 2 in March 2015, providing backscatter and depolarization measurements at 1064 nm and has continued to operate to the present in this mode. CATS level 2 products are derived from these measurements, including feature detection, cloud aerosol discrimination, cloud and aerosol typing, and optical properties of cloud and aerosol layers. Here, we present changes to our level 2 algorithms, which were aimed at reducing several biases in our version 1 level 2 data products. These changes will be incorporated into our upcoming version 2 level 2 data release in summer 2017. Additionally, owing to the near real time (NRT) data downlinking capabilities of the ISS, CATS provides expedited NRT data products within 6 hours of observation time. This capability provides a unique opportunity for supporting field campaigns and for developing data assimilation techniques to improve simulated cloud and aerosol vertical distributions in models. We additionally present preliminary work toward assimilating CATS observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) global atmospheric model and data assimilation system.

Observing ice particle growth along fall streaks in mixed-phase clouds using spectral polarimetric radar data

NASA Astrophysics Data System (ADS)

Pfitzenmaier, Lukas; Unal, Christine M. H.; Dufournet, Yann; Russchenberg, Herman W. J.

2018-06-01

The growth of ice crystals in presence of supercooled liquid droplets represents the most important process for precipitation formation in the mid-latitudes. However, such mixed-phase interaction processes remain relatively unknown, as capturing the complexity in cloud dynamics and microphysical variabilities turns to be a real observational challenge. Ground-based radar systems equipped with fully polarimetric and Doppler capabilities in high temporal and spatial resolutions such as the S-band transportable atmospheric radar (TARA) are best suited to observe mixed-phase growth processes. In this paper, measurements are taken with the TARA radar during the ACCEPT campaign (analysis of the composition of clouds with extended polarization techniques). Besides the common radar observables, the 3-D wind field is also retrieved due to TARA unique three beam configuration. The novelty of this paper is to combine all these observations with a particle evolution detection algorithm based on a new fall streak retrieval technique in order to study ice particle growth within complex precipitating mixed-phased cloud systems. In the presented cases, three different growth processes of ice crystals, plate-like crystals, and needles are detected and related to the presence of supercooled liquid water. Moreover, TARA observed signatures are assessed with co-located measurements obtained from a cloud radar and radiosondes. This paper shows that it is possible to observe ice particle growth processes within complex systems taking advantage of adequate technology and state of the art retrieval algorithms. A significant improvement is made towards a conclusive interpretation of ice particle growth processes and their contribution to rain production using fall streak rearranged radar data.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Measurement of the line-of-sight velocity of high-altitude barium clouds A technique

NASA Technical Reports Server (NTRS)

Mende, S. B.; Harris, S. E.

1982-01-01

It is demonstrated that for maximizing the scientific output of future ionospheric and magnetospheric ion cloud release experiments a new type of instrument is required which will measure the line-of-sight velocity of the ion cloud by the Doppler technique. A simple instrument was constructed using a 5-cm diam solid Fabry-Perot etalon coupled to a low-light-level integrating TV camera. It was demonstrated that the system has both the sensitivity and spectral resolution for detection of ion clouds and measurement of their line-of-sight Doppler velocity. The tests consisted of (1) a field experiment using a rocket barium cloud release to check sensitivity, and (2) laboratory experiments to show the spectral resolving capabilities of the system. The instrument was found to be operational if the source was brighter than approximately 1 kR, and it had a wavelength resolution much better than 0.2 A, which corresponds to approximately 12 km/sec or in the case of barium ion an acceleration potential of 100 V. The instrument is rugged and, therefore, simple to use in field experiments or on flight instruments. The sensitivity limit of the instrument can be increased by increasing the size of the etalon.

Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record

NASA Astrophysics Data System (ADS)

Karlsson, Karl-Göran; Håkansson, Nina

2018-02-01

The sensitivity in detecting thin clouds of the cloud screening method being used in the CM SAF cloud, albedo and surface radiation data set from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. The sensitivity, including its global variation, has been studied based on collocations of Advanced Very High Resolution Radiometer (AVHRR) and CALIOP measurements over a 10-year period (2006-2015). The cloud detection sensitivity has been defined as the minimum cloud optical thickness for which 50 % of clouds could be detected, with the global average sensitivity estimated to be 0.225. After using this value to reduce the CALIOP cloud mask (i.e. clouds with optical thickness below this threshold were interpreted as cloud-free cases), cloudiness results were found to be basically unbiased over most of the globe except over the polar regions where a considerable underestimation of cloudiness could be seen during the polar winter. The overall probability of detecting clouds in the polar winter could be as low as 50 % over the highest and coldest parts of Greenland and Antarctica, showing that a large fraction of optically thick clouds also remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud's geographical position. Best results were achieved over oceanic surfaces at mid- to high latitudes where at least 50 % of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over the Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 for the parts with the highest altitudes over Greenland and Antarctica. It is suggested to quantify the detection performance of other CDRs in terms of a sensitivity threshold of cloud optical thickness, which can be estimated using active lidar observations. Validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterization of various cloud CDRs from passive imagery.

The Earth System Science Pathfinder VOLCAM Volcanic Hazard Mission

NASA Technical Reports Server (NTRS)

Krueger, Arlin J.

1999-01-01

The VOLCAM mission is planned for research on volcanic eruptions and as a demonstration of a satellite system for measuring the location and density of volcanic eruption clouds for use in mitigating hazards to aircraft by the operational air traffic control systems. A requirement for 15 minute time resolution is met by flight as payloads of opportunity on geostationary satellites. Volcanic sulfur dioxide and ash are detected using techniques that have been developed from polar orbiting TOMS (UV) and AVHRR (IR) data. Seven band UV and three band IR filter wheel cameras are designed for continuous observation of the full disk of the earth with moderate (10 - 20 km) ground resolution. This resolution can be achieved with small, low cost instruments but is adequate for discrimination of ash and sulfur dioxide in the volcanic clouds from meteorological clouds and ozone. The false alarm rate is small through use of sulfur dioxide as a unique tracer of volcanic clouds. The UV band wavelengths are optimized to detect very small sulfur dioxide amounts that are present in pre-eruptive outgassing of volcanoes. The system is also capable of tracking dust and smoke clouds, and will be used to infer winds at tropopause level from the correlation of total ozone with potential vorticity.

Cloud Screening and Quality Control Algorithm for Star Photometer Data: Assessment with Lidar Measurements and with All-sky Images

NASA Technical Reports Server (NTRS)

Ramirez, Daniel Perez; Lyamani, H.; Olmo, F. J.; Whiteman, D. N.; Navas-Guzman, F.; Alados-Arboledas, L.

2012-01-01

This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.

A Comparison of Several Techniques to Assign Heights to Cloud Tracers.

NASA Astrophysics Data System (ADS)

Nieman, Steven J.; Schmetz, Johannes; Menzel, W. Paul

1993-09-01

Satellite-derived cloud-motion vector (CMV) production has been troubled by inaccurate height assignment of cloud tracers, especially in thin semitransparent clouds. This paper presents the results of an intercomparison of current operational height assignment techniques. Currently, heights are assigned by one of three techniques when the appropriate spectral radiance measurements are available. The infrared window (IRW) technique compares measured brightness temperatures to forecast temperature profiles and thus infers opaque cloud levels. In semitransparent or small subpixel clouds, the carbon dioxide (CO2) technique uses the ratio of radiances from different layers of the atmosphere to infer the correct cloud height. In the water vapor (H2O) technique, radiances influenced by upper-tropospheric moisture and IRW radiances are measured for several pixels viewing different cloud amounts, and their linear relationship is used to extrapolate the correct cloud height. The results presented in this paper suggest that the H2O technique is a viable alternative to the CO2 technique for inferring the heights of semitransparent cloud elements. This is important since future National Environmental Satellite, Data, and Information Service (NESDIS) operations will have to rely on H20-derived cloud-height assignments in the wind field determinations with the next operational geostationary satellite. On a given day, the heights from the two approaches compare to within 60 110 hPa rms; drier atmospheric conditions tend to reduce the effectiveness of the H2O technique. By inference one can conclude that the present height algorithms used operationally at NESDIS (with the C02 technique) and at the European Satellite Operations Center (ESOC) (with their version of the H20 technique) are providing similar results. Sample wind fields produced with the ESOC and NESDIS algorithms using Meteosat-4 data show good agreement.

An Automated System to Quantify Convectively induced Aircraft encounters with Turbulence over Europe and North Atlantic

NASA Astrophysics Data System (ADS)

Meneguz, Elena; Turp, Debi; Wells, Helen

2015-04-01

It is well known that encounters with moderate or severe turbulence can lead to passenger injuries and incur high costs for airlines from compensation and litigation. As one of two World Area Forecast Centres (WAFCs), the Met Office has responsibility for forecasting en-route weather hazards worldwide for aviation above a height of 10,000 ft. Observations from commercial aircraft provide a basis for gaining a better understanding of turbulence and for improving turbulence forecasts through verification. However there is currently a lack of information regarding the possible cause of the observed turbulence, or whether the turbulence occurred within cloud. Such information would be invaluable for the development of forecasting techniques for particular types of turbulence and for forecast verification. Of all the possible sources of turbulence, convective activity is believed to be a major cause of turbulence. Its relative importance over the Europe and North Atlantic area has not been yet quantified in a systematic way: in this study, a new approach is developed to automate identification of turbulent encounters in the proximity of convective clouds. Observations of convection are provided from two independent sources: a surface based lightning network and satellite imagery. Lightning observations are taken from the Met Office Arrival Time Detections network (ATDnet). ATDnet has been designed to identify cloud-to-ground flashes over Europe but also detects (a smaller fraction of) strikes over the North Atlantic. Meteosat Second Generation (MSG) satellite products are used to identify convective clouds by applying a brightness temperature filtering technique. The morphological features of cold cloud tops are also investigated. The system is run for all in situ turbulence reports received from airlines for a total of 12 months during summer 2013 and 2014 for the domain of interest. Results of this preliminary short term climatological study show significant intra-seasonal variability and an average of 15% of all aircraft encounters with turbulence are found in the proximity of convective clouds.

An assessment of thin cloud detection by applying bidirectional reflectance distribution function model-based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea

NASA Astrophysics Data System (ADS)

Kim, Hye-Won; Yeom, Jong-Min; Shin, Daegeun; Choi, Sungwon; Han, Kyung-Soo; Roujean, Jean-Louis

2017-08-01

In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model-based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle-dependent geostationary sensor geometry. For quantitative validation, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).

New methods to detect particle velocity and mass flux in arc-heated ablation/erosion facilities

NASA Technical Reports Server (NTRS)

Brayton, D. B.; Bomar, B. W.; Seibel, B. L.; Elrod, P. D.

1980-01-01

Arc-heated flow facilities with injected particles are used to simulate the erosive and ablative/erosive environments encountered by spacecraft re-entry through fog, clouds, thermo-nuclear explosions, etc. Two newly developed particle diagnostic techniques used to calibrate these facilities are discussed. One technique measures particle velocity and is based on the detection of thermal radiation and/or chemiluminescence from the hot seed particles in a model ablation/erosion facility. The second technique measures a local particle rate, which is proportional to local particle mass flux, in a dust erosion facility by photodetecting and counting the interruptions of a focused laser beam by individual particles.

Cloud detection method for Chinese moderate high resolution satellite imagery (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zhong, Bo; Chen, Wuhan; Wu, Shanlong; Liu, Qinhuo

2016-10-01

Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors don't have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called OCM (Object oriented Cloud and cloud-shadow Matching method) is presented in this paper. It firstly modified the Automatic Cloud Cover Assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold setting produces different cloud map. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Secondly, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Thirdly, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. OCM method was tested using almost 200 HJ-1/CCD images across China and the overall accuracy of cloud detection is close to 90%.

Application of Ground-Penetrating Radar for Detecting Internal Anomalies in Tree Trunks with Irregular Contours.

PubMed

Li, Weilin; Wen, Jian; Xiao, Zhongliang; Xu, Shengxia

2018-02-22

To assess the health conditions of tree trunks, it is necessary to estimate the layers and anomalies of their internal structure. The main objective of this paper is to investigate the internal part of tree trunks considering their irregular contour. In this respect, we used ground penetrating radar (GPR) for non-invasive detection of defects and deteriorations in living trees trunks. The Hilbert transform algorithm and the reflection amplitudes were used to estimate the relative dielectric constant. The point cloud data technique was applied as well to extract the irregular contours of trunks. The feasibility and accuracy of the methods were examined through numerical simulations, laboratory and field measurements. The results demonstrated that the applied methodology allowed for accurate characterizations of the internal inhomogeneity. Furthermore, the point cloud technique resolved the trunk well by providing high-precision coordinate information. This study also demonstrated that cross-section tomography provided images with high resolution and accuracy. These integrated techniques thus proved to be promising for observing tree trunks and other cylindrical objects. The applied approaches offer a great promise for future 3D reconstruction of tomographic images with radar wave.

Constraining Methane Abundance and Cloud Properties from the Reflected Light Spectra of Directly Imaged Exoplanets

NASA Astrophysics Data System (ADS)

Lupu, R.; Marley, M. S.; Lewis, N. K.

2015-12-01

We have assembled an atmospheric retrieval package for the reflected light spectra of gas- and ice- giants in order to inform the design and estimate the scientific return of future space-based coronagraph instruments. Such instruments will have a working bandpass of ~0.4-1 μm and a resolving power R~70, and will enable the characterization of tens of exoplanets in the Solar neighborhood. The targets will be chosen form known RV giants, with estimated effective temperatures of ~100-600 K and masses between 0.3 and 20 MJupiter. In this regime, both methane and clouds will have the largest effects on the observed spectra. Our retrieval code is the first to include cloud properties in the core set of parameters, along with methane abundance and surface gravity. We consider three possible cloud structure scenarios, with 0, 1 or 2 cloud layers, respectively. The best-fit parameters for a given model are determined using a Monte Carlo Markov Chain ensemble sampler, and the most favored cloud structure is chosen by calculating the Bayes factors between different models. We present the performance of our retrieval technique applied to a set of representative model spectra, covering a SNR range form 5 to 20 and including possible noise correlations over a 25 or 100 nanometer scale. Further, we apply the technique to more realistic cases, namely simulated observations of Jupiter, Saturn, Uranus, and the gas-giant HD99492c. In each case, we determine the confidence levels associated with the methane and cloud detections, as a function of SNR and noise properties.

Combining In-situ Measurements, Passive Satellite Imagery, and Active Radar Retrievals for the Detection of High Ice Water Content

NASA Astrophysics Data System (ADS)

Yost, C. R.; Minnis, P.; Bedka, K. M.; Nguyen, L.; Palikonda, R.; Spangenberg, D.; Strapp, J. W.; Delanoë, J.; Protat, A.

2016-12-01

At least one hundred jet engine power loss events since the 1990s have been attributed to the phenomenon known as ice crystal icing (ICI). Ingestion of high concentrations of ice particles into aircraft engines is thought to cause these events, but it is clear that the use of current on-board weather radar systems alone is insufficient for detecting conditions that might cause ICI. Passive radiometers in geostationary orbit are valuable for monitoring systems that produce high ice water content (HIWC) and will play an important role in nowcasting, but are incapable of making vertically resolved measurements of ice particle concentration, i.e., ice water content (IWC). Combined radar, lidar, and in-situ measurements are essential for developing a skilled satellite-based HIWC nowcasting technique. The High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) field campaigns in Darwin, Australia, and Cayenne, French Guiana, have produced a valuable dataset of in-situ total water content (TWC) measurements with which to study conditions that produce HIWC. The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) was used to derive cloud physical and optical properties such cloud top height, temperature, optical depth, and ice water path from multi-spectral satellite imagery acquired throughout the HAIC-HIWC campaigns. These cloud properties were collocated with the in-situ TWC measurements in order to characterize cloud properties in the vicinity of HIWC. Additionally, a database of satellite-derived overshooting cloud top (OT) detections was used to identify TWC measurements in close proximity to convective cores likely producing large concentrations of ice crystals. Certain cloud properties show some sensitivity to increasing TWC and a multivariate probabilistic indicator of HIWC was developed from these datasets. This paper describes the algorithm development and demonstrates the HIWC indicator with imagery from the HAIC-HIWC campaigns. Vertically resolved IWC retrievals from active sensors such as the Cloud Profiling Radar (CPR) on CloudSat and the Doppler Radar System Airborne (RASTA) provide IWC profiles with which to validate and potentially enhance the satellite-based HIWC indicator.

Comparison of Cloud and Aerosol Detection between CERES Edition 3 Cloud Mask and CALIPSO Version 2 Data Products

NASA Astrophysics Data System (ADS)

Trepte, Qing; Minnis, Patrick; Sun-Mack, Sunny; Trepte, Charles

Clouds and aerosol play important roles in the global climate system. Accurately detecting their presence, altitude, and properties using satellite radiance measurements is a crucial first step in determining their influence on surface and top-of-atmosphere radiative fluxes. This paper presents a comparison analysis of a new version of the Clouds and Earth's Radiant Energy System (CERES) Edition 3 cloud detection algorithms using Aqua MODIS data with the recently released Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Version 2 Vertical Feature Mask (VFM). Improvements in CERES Edition 3 cloud mask include dust detection, thin cirrus tests, enhanced low cloud detection at night, and a smoother transition from mid-latitude to polar regions. For the CALIPSO Version 2 data set, changes to the lidar calibration can result in significant improvements to its identification of optically thick aerosol layers. The Aqua and CALIPSO satellites, part of the A-train satellite constellation, provide a unique opportunity for validating passive sensor cloud and aerosol detection using an active sensor. In this paper, individual comparison cases will be discussed for different types of clouds and aerosols over various surfaces, for daytime and nighttime conditions, and for regions ranging from the tropics to the poles. Examples will include an assessment of the CERES detection algorithm for optically thin cirrus, marine stratus, and polar night clouds as well as its ability to characterize Saharan dust plumes off the African coast. With the CALIPSO lidar's unique ability to probe the vertical structure of clouds and aerosol layers, it provides an excellent validation data set for cloud detection algorithms, especially for polar nighttime clouds.

Statistical analysis of lightning electric field measured under Malaysian condition

NASA Astrophysics Data System (ADS)

Salimi, Behnam; Mehranzamir, Kamyar; Abdul-Malek, Zulkurnain

2014-02-01

Lightning is an electrical discharge during thunderstorms that can be either within clouds (Inter-Cloud), or between clouds and ground (Cloud-Ground). The Lightning characteristics and their statistical information are the foundation for the design of lightning protection system as well as for the calculation of lightning radiated fields. Nowadays, there are various techniques to detect lightning signals and to determine various parameters produced by a lightning flash. Each technique provides its own claimed performances. In this paper, the characteristics of captured broadband electric fields generated by cloud-to-ground lightning discharges in South of Malaysia are analyzed. A total of 130 cloud-to-ground lightning flashes from 3 separate thunderstorm events (each event lasts for about 4-5 hours) were examined. Statistical analyses of the following signal parameters were presented: preliminary breakdown pulse train time duration, time interval between preliminary breakdowns and return stroke, multiplicity of stroke, and percentages of single stroke only. The BIL model is also introduced to characterize the lightning signature patterns. Observations on the statistical analyses show that about 79% of lightning signals fit well with the BIL model. The maximum and minimum of preliminary breakdown time duration of the observed lightning signals are 84 ms and 560 us, respectively. The findings of the statistical results show that 7.6% of the flashes were single stroke flashes, and the maximum number of strokes recorded was 14 multiple strokes per flash. A preliminary breakdown signature in more than 95% of the flashes can be identified.

Pseudorandom Noise Code-Based Technique for Cloud and Aerosol Discrimination Applications

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Prasad, Narasimha S.; Flood, Michael A.; Harrison, Fenton Wallace

2011-01-01

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a PN code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths. Keywords: ASCENDS, CO2 sensing, O2 sensing, PN codes, CW lidar

Detection of defects in formed sheet metal using medial axis transformation

NASA Astrophysics Data System (ADS)

Murmu, Naresh C.; Velgan, Roman

2003-05-01

In the metal forming processes, the sheet metals are often prone to various defects such as thinning, dents, wrinkles etc. In the present manufacturing environments with ever increasing demand of higher quality, detecting the defects of formed sheet metal using an effective and objective inspection system is the foremost norm to remain competitive in market. The defect detection using optical techniques aspire to satisfy its needs to be non-contact and fast. However, the main difficulties to achieve this goal remain essentially on the development of efficient evaluation technique and accurate interpretation of extracted data. The defect like thinning is detected by evaluating the deviations of the thickness in the formed sheet metal against its nominal value. The present evaluation procedure for determination of thickness applied on the measurements data is not without deficiency. To improve this procedure, a new evaluation approach based on medial axis transformation is proposed here. The formed sheet metals are digitized using fringe projection systems in different orientations, and afterwards registered into one coordinate frame. The medial axis transformation (MAT) is applied on the point clouds, generating the point clouds of MAT. This data is further processed and medial surface is determined. The thinning defect is detected by evaluating local wall thickness and other defects like wrinkles are determined using the shape recognition on the medial surface. The applied algorithm is simple, fast and robust.

Impact of Resolution on Simulation of Closed Mesoscale Cellular Convection Identified by Dynamically Guided Watershed Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martini, Matus N.; Gustafson, William I.; Yang, Qing

2014-11-18

Organized mesoscale cellular convection (MCC) is a common feature of marine stratocumulus that forms in response to a balance between mesoscale dynamics and smaller scale processes such as cloud radiative cooling and microphysics. We use the Weather Research and Forecasting model with chemistry (WRF-Chem) and fully coupled cloud-aerosol interactions to simulate marine low clouds during the VOCALS-REx campaign over the southeast Pacific. A suite of experiments with 3- and 9-km grid spacing indicates resolution-dependent behavior. The simulations with finer grid spacing have smaller liquid water paths and cloud fractions, while cloud tops are higher. The observed diurnal cycle is reasonablymore » well simulated. To isolate organized MCC characteristics we develop a new automated method, which uses a variation of the watershed segmentation technique that combines the detection of cloud boundaries with a test for coincident vertical velocity characteristics. This ensures that the detected cloud fields are dynamically consistent for closed MCC, the most common MCC type over the VOCALS-REx region. We demonstrate that the 3-km simulation is able to reproduce the scaling between horizontal cell size and boundary layer height seen in satellite observations. However, the 9-km simulation is unable to resolve smaller circulations corresponding to shallower boundary layers, instead producing invariant MCC horizontal scale for all simulated boundary layers depths. The results imply that climate models with grid spacing of roughly 3 km or smaller may be needed to properly simulate the MCC structure in the marine stratocumulus regions.« less

Satellite Observations of Volcanic Clouds from the Eruption of Redoubt Volcano, Alaska, 2009

NASA Astrophysics Data System (ADS)

Dean, K. G.; Ekstrand, A. L.; Webley, P.; Dehn, J.

2009-12-01

Redoubt Volcano began erupting on 23 March 2009 (UTC) and consisted of 19 events over a 14 day period. The volcano is located on the Alaska Peninsula, 175 km southwest of Anchorage, Alaska. The previous eruption was in 1989/1990 and seriously disrupted air traffic in the region, including the near catastrophic engine failure of a passenger airliner. Plumes and ash clouds from the recent eruption were observed on a variety of satellite data (AVHRR, MODIS and GOES). The eruption produced volcanic clouds up to 19 km which are some of the highest detected in recent times in the North Pacific region. The ash clouds primarily drifted north and east of the volcano, had a weak ash signal in the split window data and resulted in light ash falls in the Cook Inlet basin and northward into Alaska’s Interior. Volcanic cloud heights were measured using ground-based radar, and plume temperature and wind shear methods but each of the techniques resulted in significant variations in the estimates. Even though radar showed the greatest heights, satellite data and wind shears suggest that the largest concentrations of ash may be at lower altitudes in some cases. Sulfur dioxide clouds were also observed on satellite data (OMI, AIRS and Calipso) and they primarily drifted to the east and were detected at several locations across North America, thousands of kilometers from the volcano. Here, we show time series data collected by the Alaska Volcano Observatory, illustrating the different eruptive events and ash clouds that developed over the subsequent days.

Cavitation erosion - scale effect and model investigations

NASA Astrophysics Data System (ADS)

Geiger, F.; Rutschmann, P.

2015-12-01

The experimental works presented in here contribute to the clarification of erosive effects of hydrodynamic cavitation. Comprehensive cavitation erosion test series were conducted for transient cloud cavitation in the shear layer of prismatic bodies. The erosion pattern and erosion rates were determined with a mineral based volume loss technique and with a metal based pit count system competitively. The results clarified the underlying scale effects and revealed a strong non-linear material dependency, which indicated significantly different damage processes for both material types. Furthermore, the size and dynamics of the cavitation clouds have been assessed by optical detection. The fluctuations of the cloud sizes showed a maximum value for those cavitation numbers related to maximum erosive aggressiveness. The finding suggests the suitability of a model approach which relates the erosion process to cavitation cloud dynamics. An enhanced experimental setup is projected to further clarify these issues.

Comparison of global cloud liquid water path derived from microwave measurements with CERES-MODIS

NASA Astrophysics Data System (ADS)

Yi, Y.; Minnis, P.; Huang, J.; Lin, B.; Ayers, K.; Sun-Mack, S.; Fan, A.

Cloud liquid water path LWP is a crucial parameter for climate studies due to the link that it provides between the atmospheric hydrological and radiative budgets Satellite-based visible infrared techniques such as the Visible Infrared Solar Split-Window Technique VISST can retrieve LWP for water clouds assumes single-layer over a variety of surfaces If the water clouds are overlapped by ice clouds the LWP of the underlying clouds can not be retrieved by such techniques However microwave techniques may be used to retrieve the LWP underneath ice clouds due to the microwave s insensitivity to cloud ice particles LWP is typically retrieved from satellite-observed microwave radiances only over ocean due to variations of land surface temperature and emissivity Recently Deeter and Vivekanandan 2006 developed a new technique for retrieving LWP over land In order to overcome the sensitivity to land surface temperature and emissivity their technique is based on a parameterization of microwave polarization-difference signals In this study a similar regression-based technique for retrieving LWP over land and ocean using Advanced Microwave Scanning Radiometer - EOS AMSR-E measurements is developed Furthermore the microwave surface emissivities are also derived using clear-sky fields of view based on the Clouds and Earth s Radiant Energy System Moderate-resolution Imaging Spectroradiometer CERES-MODIS cloud mask These emissivities are used in an alternate form of the technique The results are evaluated using independent measurements such

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solaimani, Mohiuddin; Iftekhar, Mohammed; Khan, Latifur

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. Asmore » a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.« less

DEEPLY EMBEDDED PROTOSTELLAR POPULATION IN THE 20 km s{sup −1} CLOUD OF THE CENTRAL MOLECULAR ZONE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Xing; Gu, Qiusheng; Zhang, Qizhou

2015-12-01

We report the discovery of a population of deeply embedded protostellar candidates in the 20 km s{sup −1} cloud, one of the massive molecular clouds in the Central Molecular Zone (CMZ) of the Milky Way, using interferometric submillimeter continuum and H{sub 2}O maser observations. The submillimeter continuum emission shows five 1 pc scale clumps, each of which further fragments into several 0.1 pc scale cores. We identify 17 dense cores, among which 12 are gravitationally bound. Among the 18 H{sub 2}O masers detected, 13 coincide with the cores and probably trace outflows emanating from the protostars. There are also 5more » gravitationally bound dense cores without H{sub 2}O maser detection. In total, the 13 masers and 5 cores may represent 18 protostars with spectral types later than B1 or potentially growing more massive stars at earlier evolutionary stages, given the non-detection in the centimeter radio continuum. In combination with previous studies of CH{sub 3}OH masers, we conclude that the star formation in this cloud is at an early evolutionary phase, before the presence of any significant ionizing or heating sources. Our findings indicate that star formation in this cloud may be triggered by a tidal compression as it approaches pericenter, similar to the case of G0.253+0.016 but with a higher star formation rate, and demonstrate that high angular resolution, high-sensitivity maser, and submillimeter observations are promising techniques to unveil deeply embedded star formation in the CMZ.« less

Comparative evaluation of polarimetric and bi-spectral cloud microphysics retrievals: Retrieval closure experiments and comparisons based on idealized and LES case studies

NASA Astrophysics Data System (ADS)

Miller, D. J.; Zhang, Z.; Ackerman, A. S.; Platnick, S. E.; Cornet, C.

2016-12-01

A remote sensing cloud retrieval simulator, created by coupling an LES cloud model with vector radiative transfer (RT) models is the ideal framework for assessing cloud remote sensing techniques. This simulator serves as a tool for understanding bi-spectral and polarimetric retrievals by comparing them directly to LES cloud properties (retrieval closure comparison) and for comparing the retrieval techniques to one another. Our simulator utilizes the DHARMA LES [Ackerman et al., 2004] with cloud properties based on marine boundary layer (MBL) clouds observed during the DYCOMS-II and ATEX field campaigns. The cloud reflectances are produced by the vectorized RT models based on polarized doubling adding and monte carlo techniques (PDA, MCPOL). Retrievals are performed utilizing techniques as similar as possible to those implemented on their corresponding well known instruments; polarimetric retrievals are based on techniques implemented for polarimeters (POLDER, AirMSPI, and RSP) and bi-spectral retrievals are performed using the Nakajima-King LUT method utilized on a number of spectral instruments (MODIS and VIIRS). Retrieval comparisons focus on cloud droplet effective radius (re), effective variance (ve), and cloud optical thickness (τ). This work explores the sensitivities of these two retrieval techniques to various observation limitations, such as spatial resolution/cloud inhomogeneity, impact of 3D radiative effects, and angular resolution requirements. With future remote sensing missions like NASA's Aerosols/Clouds/Ecosystems (ACE) planning to feature advanced polarimetric instruments it is important to understand how these retrieval techniques compare to one another. The cloud retrieval simulator we've developed allows us to probe these important questions in a realistically relevant test bed.

Absorbing Aerosols Above Cloud: Detection, Quantitative Retrieval, and Radiative Forcing from Satellite-based Passive Sensors

NASA Astrophysics Data System (ADS)

Jethva, H.; Torres, O.; Remer, L. A.; Bhartia, P. K.

2012-12-01

Light absorbing particles such as carbonaceous aerosols generated from biomass burning activities and windblown dust particles can exert a net warming effect on climate; the strength of which depends on the absorption capacity of the particles and brightness of the underlying reflecting background. When advected over low-level bright clouds, these aerosols absorb the cloud reflected radiation from ultra-violet (UV) to shortwave-IR (SWIR) and makes cloud scene darker-a phenomenon commonly known as "cloud darkening". The apparent "darkening" effect can be seen by eyes in satellite images as well as quantitatively in the spectral reflectance measurements made by space borne sensors over regions where light absorbing carbonaceous and dust aerosols overlay low-level cloud decks. Theoretical radiative transfer simulations support the observational evidence, and further reveal that the strength of the cloud darkening and its spectral signature (or color ratio) between measurements at two wavelengths are a bi-function of aerosol and cloud optical thickness (AOT and COT); both are measures of the total amount of light extinction caused by aerosols and cloud, respectively. Here, we developed a retrieval technique, named as the "color ratio method" that uses the satellite measurements at two channels, one at shorter wavelength in the visible and one at longer wavelength in the shortwave-IR for the simultaneous retrieval of AOT and COT. The present technique requires assumptions on the aerosol single-scattering albedo and aerosol-cloud separation which are supplemented by the Aerosol Robotic Network (AERONET) and space borne CALIOP lidar measurements. The retrieval technique has been tested making use of the near-UV and visible reflectance observations made by the Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) for distinct above-cloud smoke and dust aerosol events observed seasonally over the southeast and tropical Atlantic Ocean, respectively. This study constitutes the first attempt to use non-polarized and non-lidar reflectance observations-both of them shown to have above-cloud aerosols retrieval capability, to retrieve above-cloud AOT by a passive non-polarized sensor. The uncertainty analysis suggests that the present method should retrieve above-cloud AOT within -10% to 50% which mainly arises due to uncertainty associated with the single-scattering albedo assumption. Although, currently tested by making use of OMI and MODIS measurements, the present color ratio method can be equally applied to the other satellite measurements that carry similar or near-by channels in VIS region of the spectrum such as MISR and NPP/VIIRS. The capability of quantifying the above-cloud aerosol load will facilitate several aspects of cloud-aerosol interaction research such as estimation of the direct radiative forcing of aerosols above clouds; the sign of which can be opposite (warming) to cloud-free aerosol forcing (cooling), aerosol transport, indirect effects of aerosols on clouds, and hydrological cycle.

Analysis of the VIIRS cloud mask, comparison with the NAVOCEANO cloud mask, and how they complement each other

NASA Astrophysics Data System (ADS)

Cayula, Jean-François P.; May, Douglas A.; McKenzie, Bruce D.

2014-05-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (VCM) Intermediate Product (IP) has been developed for use with Suomi National Polar-orbiting Partnership (NPP) VIIRS Environmental Data Record (EDR) products. In particular, the VIIRS Sea Surface Temperature (SST) EDR relies on VCM to identify cloud contaminated observations. Unfortunately, VCM does not appear to perform as well as cloud detection algorithms for SST. This may be due to similar but different goals of the two algorithms. VCM is concerned with detecting clouds while SST is interested in identifying clear observations. The result is that in undetermined cases VCM defaults to "clear," while the SST cloud detection defaults to "cloud." This problem is further compounded because classic SST cloud detection often flags as "cloud" all types of corrupted data, thus making a comparison with VCM difficult. The Naval Oceanographic Office (NAVOCEANO), which operationally produces a VIIRS SST product, relies on cloud detection from the NAVOCEANO Cloud Mask (NCM), adapted from cloud detection schemes designed for SST processing. To analyze VCM, the NAVOCEANO SST process was modified to attach the VCM flags to all SST retrievals. Global statistics are computed for both day and night data. The cases where NCM and/or VCM tag data as cloud-contaminated or clear can then be investigated. By analyzing the VCM individual test flags in conjunction with the status of NCM, areas where VCM can complement NCM are identified.

Thunderstorm monitoring and lightning warning, operational applications of the Safir system

NASA Technical Reports Server (NTRS)

Richard, Philippe

1991-01-01

During the past years a new range of studies have been opened by the application of electromagnetic localization techniques to the field of thunderstorm remote sensing. VHF localization techniques were used in particular for the analysis of lightning discharges and gave access to time resolved 3-D images of lightning discharges within thunderclouds. Detection and localization techniques developed have been applied to the design of the SAFIR system. This development's main objective was the design of an operational system capable of assessing and warning in real time for lightning hazards and potential thunderstorm hazards. The SAFIR system main detection technique is the long range interferometric localization of thunderstorm electromagnetic activity; the system performs the localization of intracloud and cloud to ground lightning discharges and the analysis of the characteristics of the activity.

Overview of MPLNET Version 3 Cloud Detection

NASA Technical Reports Server (NTRS)

Lewis, Jasper R.; Campbell, James; Welton, Ellsworth J.; Stewart, Sebastian A.; Haftings, Phillip

2016-01-01

The National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.

Day-time identification of summer hailstorm cells from MSG data

NASA Astrophysics Data System (ADS)

Merino, A.; López, L.; Sánchez, J. L.; García-Ortega, E.; Cattani, E.; Levizzani, V.

2013-10-01

Identifying deep convection is of paramount importance, as it may be associated with extreme weather that has significant impact on the environment, property and the population. A new method, the Hail Detection Tool (HDT), is described for identifying hail-bearing storms using multi-spectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the Convective Mask (CM) algorithm devised for detection of deep convection, and the second a Hail Detection algorithm (HD) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HD are based on logistic regression models trained with multi-spectral MSG data-sets comprised of summer convective events in the middle Ebro Valley between 2006-2010, and detected by the RGB visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HD are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients." Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall Probability of Detection (POD) was 76.9% and False Alarm Ratio 16.7%.

GOES Cloud Detection at the Global Hydrology and Climate Center

NASA Technical Reports Server (NTRS)

Laws, Kevin; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2002-01-01

The bi-spectral threshold (BTH) for cloud detection and height assignment is now operational at NASA's Global Hydrology and Climate Center (GHCC). This new approach is similar in principle to the bi-spectral spatial coherence (BSC) method with improvements made to produce a more robust cloud-filtering algorithm for nighttime cloud detection and subsequent 24-hour operational cloud top pressure assignment. The method capitalizes on cloud and surface emissivity differences from the GOES 3.9 and 10.7-micrometer channels to distinguish cloudy from clear pixels. Separate threshold values are determined for day and nighttime detection, and applied to a 20-day minimum composite difference image to better filter background effects and enhance differences in cloud properties. A cloud top pressure is assigned to each cloudy pixel by referencing the 10.7-micrometer channel temperature to a thermodynamic profile from a locally -run regional forecast model. This paper and supplemental poster will present an objective validation of nighttime cloud detection by the BTH approach in comparison with previous methods. The cloud top pressure will be evaluated by comparing to the NESDIS operational CO2 slicing approach.

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

NASA Technical Reports Server (NTRS)

Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

2012-01-01

With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

Volcanic ash cloud detection from space: a preliminary comparison between RST approach and water vapour corrected BTD procedure

NASA Astrophysics Data System (ADS)

Piscini, Alessandro; Marchese, Francesco; Merucci, Luca; Pergola, Nicola; Corradini, Stefano; Tramutoli, Valerio

2010-05-01

Volcanic eruptions can inject large amounts (Tg) of gas and particles into the troposphere and, sometimes, into the stratosphere. Besides the main gases (H2O, CO2 , SO2 and HCl), volcanic clouds contain a mix of silicate ash particles in the size range 0.1μm to mm or larger. Interest in the ash presence detection is high in particular because it represents a serious hazard for air traffic. Particles with dimension of several millimeters can damage the aircraft structure (windows, wings, ailerons), while particles less than 10μm may be extremely dangerous for the jet engines and are undetectable by the pilots during night or in low visibility conditions. Satellite data are useful for measuring volcanic clouds because of the large vertical range of these emissions and their likely large horizontal spread. Moreover, since volcanoes are globally distributed and inherently dangerous, satellite measurements offer a practical and safe platform from which to make observations. Two different techniques used to detect volcanic clouds from satellite data are considered here for a preliminary comparison, with possible implications on quantitative retrievals of plume parameters. In particular, the Robust Satellite Techniques (RST) approach and a water vapour corrected version of the Brightness Temperature Difference (BTD) procedure, will be compared. The RST approach is based on the multi-temporal analysis of historical, long-term satellite records, devoted to a former characterization 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. The BTD method is based on the difference between the brightness temperature measured in two channels centered around 11 and 12 mm. To take into account the atmospheric water vapour differential absorption in the 11-12 μm spectral range that tends to reduce (and in some cases completely mask) the BTD signal, a water vapor correction procedure, based on measured or synthetic atmospheric profiles, has been applied. Results independently achieved by both methods during recent Mt. Etna eruptions are presented, compared and discussed also in terms of further implications for quantitative retrievals of plume parameters.

Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

NASA Technical Reports Server (NTRS)

Chiriaco, M.; Chepfer, H.; Haeffelin, M.; Minnis, P.; Noel, V.; Platnick, S.; McGill, M.; Baumgardner, D.; Dubuisson, P.; Pelon, J.;

A Cloud Mask for AIRS

NASA Technical Reports Server (NTRS)

Brubaker, N.; Jedlovec, G. J.

2004-01-01

With the preliminary release of AIRS Level 1 and 2 data to the scientific community, there is a growing need for an accurate AIRS cloud mask for data assimilation studies and in producing products derived from cloud free radiances. Current cloud information provided with the AIRS data are limited or based on simplified threshold tests. A multispectral cloud detection approach has been developed for AIRS that utilizes the hyper-spectral capabilities to detect clouds based on specific cloud signatures across the short wave and long wave infrared window regions. This new AIRS cloud mask has been validated against the existing AIRS Level 2 cloud product and cloud information derived from MODIS. Preliminary results for both day and night applications over the continental U.S. are encouraging. Details of the cloud detection approach and validation results will be presented at the conference.

Marine Layer Clouds off the California Coast

NASA Image and Video Library

2017-12-08

NASA image acquired September 27, 2012 On September 27, 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite captured this nighttime view of low-lying marine layer clouds along the coast of California. The image was captured by the VIIRS “day-night band,” which detects light in a range of wavelengths from green to near-infrared and uses filtering techniques to observe signals such as gas flares, auroras, wildfires, city lights, and reflected moonlight. An irregularly-shaped patch of high clouds hovers off the coast of California, and moonlight caused the high clouds to cast distinct shadows on the marine layer clouds below. VIIRS acquired the image when the Moon was in its waxing gibbous phase, meaning it was more than half-lit, but less than full. Low clouds pose serious hazards for air and ship traffic, but satellites have had difficulty detecting them in the past. To illustrate this, the second image shows the same scene in thermal infrared, the band that meteorologists generally use to monitor clouds at night. Only high clouds are visible; the low clouds do not show up at all because they are roughly the same temperature as the ground. NASA Earth Observatory image by Jesse Allen and Robert Simmon, using VIIRS Day-Night Band data from the Suomi National Polar-orbiting Partnership. Suomi NPP is the result of a partnership between NASA, the National Oceanic and Atmospheric Administration, and the Department of Defense. Caption by Adam Voiland. Instrument: Suomi NPP - VIIRS Credit: NASA Earth Observatory Click here to view all of the Earth at Night 2012 images Click here to read more about this image NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

NASA Technical Reports Server (NTRS)

Wind, Galina (Gala); Platnick, Steven; Riedi, Jerome

2011-01-01

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Heterodyne detection of CO2 emission lines and wind velocities in the atmosphere of Venus

NASA Technical Reports Server (NTRS)

Betz, A. L.; Johnson, M. A.; Mclaren, R. A.; Sutton, E. C.

1975-01-01

Strong 10 micrometer line emission from (c-12)(o-16)2 in the upper atmosphere of Venus was detected by heterodyne techniques. Observations of the absolute Doppler shift of the emission features indicate mean zonal wind velocities less than 10 m/sec in the upper atmosphere near the equator. No evidence was found of the 100 m/sec wind velocity implied by the apparent 4-day rotation period of ultraviolet cloud features.

Integrating Flexible Sensor and Virtual Self-Organizing DC Grid Model With Cloud Computing for Blood Leakage Detection During Hemodialysis.

PubMed

Huang, Ping-Tzan; Jong, Tai-Lang; Li, Chien-Ming; Chen, Wei-Ling; Lin, Chia-Hung

2017-08-01

Blood leakage and blood loss are serious complications during hemodialysis. From the hemodialysis survey reports, these life-threatening events occur to attract nephrology nurses and patients themselves. When the venous needle and blood line are disconnected, it takes only a few minutes for an adult patient to lose over 40% of his / her blood, which is a sufficient amount of blood loss to cause the patient to die. Therefore, we propose integrating a flexible sensor and self-organizing algorithm to design a cloud computing-based warning device for blood leakage detection. The flexible sensor is fabricated via a screen-printing technique using metallic materials on a soft substrate in an array configuration. The self-organizing algorithm constructs a virtual direct current grid-based alarm unit in an embedded system. This warning device is employed to identify blood leakage levels via a wireless network and cloud computing. It has been validated experimentally, and the experimental results suggest specifications for its commercial designs. The proposed model can also be implemented in an embedded system.

Ribosomal DNA status inferred from DNA cloud assays and mass spectrometry identification of agarose-squeezed proteins interacting with chromatin (ASPIC-MS).

PubMed

Krol, Kamil; Jendrysek, Justyna; Debski, Janusz; Skoneczny, Marek; Kurlandzka, Anna; Kaminska, Joanna; Dadlez, Michal; Skoneczna, Adrianna

2017-04-11

Ribosomal RNA-encoding genes (rDNA) are the most abundant genes in eukaryotic genomes. To meet the high demand for rRNA, rDNA genes are present in multiple tandem repeats clustered on a single or several chromosomes and are vastly transcribed. To facilitate intensive transcription and prevent rDNA destabilization, the rDNA-encoding portion of the chromosome is confined in the nucleolus. However, the rDNA region is susceptible to recombination and DNA damage, accumulating mutations, rearrangements and atypical DNA structures. Various sophisticated techniques have been applied to detect these abnormalities. Here, we present a simple method for the evaluation of the activity and integrity of an rDNA region called a "DNA cloud assay". We verified the efficacy of this method using yeast mutants lacking genes important for nucleolus function and maintenance (RAD52, SGS1, RRM3, PIF1, FOB1 and RPA12). The DNA cloud assay permits the evaluation of nucleolus status and is compatible with downstream analyses, such as the chromosome comet assay to identify DNA structures present in the cloud and mass spectrometry of agarose squeezed proteins (ASPIC-MS) to detect nucleolar DNA-bound proteins, including Las17, the homolog of human Wiskott-Aldrich Syndrome Protein (WASP).

Ribosomal DNA status inferred from DNA cloud assays and mass spectrometry identification of agarose-squeezed proteins interacting with chromatin (ASPIC-MS)

PubMed Central

Krol, Kamil; Jendrysek, Justyna; Debski, Janusz; Skoneczny, Marek; Kurlandzka, Anna; Kaminska, Joanna; Dadlez, Michal; Skoneczna, Adrianna

2017-01-01

Ribosomal RNA-encoding genes (rDNA) are the most abundant genes in eukaryotic genomes. To meet the high demand for rRNA, rDNA genes are present in multiple tandem repeats clustered on a single or several chromosomes and are vastly transcribed. To facilitate intensive transcription and prevent rDNA destabilization, the rDNA-encoding portion of the chromosome is confined in the nucleolus. However, the rDNA region is susceptible to recombination and DNA damage, accumulating mutations, rearrangements and atypical DNA structures. Various sophisticated techniques have been applied to detect these abnormalities. Here, we present a simple method for the evaluation of the activity and integrity of an rDNA region called a “DNA cloud assay”. We verified the efficacy of this method using yeast mutants lacking genes important for nucleolus function and maintenance (RAD52, SGS1, RRM3, PIF1, FOB1 and RPA12). The DNA cloud assay permits the evaluation of nucleolus status and is compatible with downstream analyses, such as the chromosome comet assay to identify DNA structures present in the cloud and mass spectrometry of agarose squeezed proteins (ASPIC-MS) to detect nucleolar DNA-bound proteins, including Las17, the homolog of human Wiskott-Aldrich Syndrome Protein (WASP). PMID:28212567

A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds

PubMed Central

Dorninger, Peter; Pfeifer, Norbert

2008-01-01

Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects. PMID:27873931

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

NASA Astrophysics Data System (ADS)

Bayram, Eda; Vural, Elif; Alatan, Aydin

2017-10-01

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification.

PubMed

Bao, Riyue; Hernandez, Kyle; Huang, Lei; Kang, Wenjun; Bartom, Elizabeth; Onel, Kenan; Volchenboum, Samuel; Andrade, Jorge

2015-01-01

Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud.

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

NASA Astrophysics Data System (ADS)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Interstellar C2, CH, and CN in translucent molecular clouds

NASA Technical Reports Server (NTRS)

Black, John H.; Van Dishoeck, Ewine F.

1989-01-01

Optical absorption-line techniques have been applied to the study of a number of translucent molecular clouds in which the total column densities are large enough that substantial molecular abundances can be maintained. Results are presented for a survey of absorption lines of interstellar C2, CH, and CN. Detections of CN through the A 2Pi-X 2Sigma(+) (1,O) and (2,O) bands of the red system are reported and compared with observations of the violet system for one line of sight. The population distributions in C2 provide diagnostic information on temperature and density. The measured column densities of the three species can be used to test details of the theory of molecule formation in clouds where photoprocesses still play a significant role. The C2 and CH column densities are strongly correlated with each other and probably also with the H2 column density. In contrast, the CN column densities are found to vary greatly from cloud to cloud. The observations are discussed with reference to detailed theoretical models.

Daytime identification of summer hailstorm cells from MSG data

NASA Astrophysics Data System (ADS)

Merino, A.; López, L.; Sánchez, J. L.; García-Ortega, E.; Cattani, E.; Levizzani, V.

2014-04-01

Identifying deep convection is of paramount importance, as it may be associated with extreme weather phenomena that have significant impact on the environment, property and populations. A new method, the hail detection tool (HDT), is described for identifying hail-bearing storms using multispectral Meteosat Second Generation (MSG) data. HDT was conceived as a two-phase method, in which the first step is the convective mask (CM) algorithm devised for detection of deep convection, and the second a hail mask algorithm (HM) for the identification of hail-bearing clouds among cumulonimbus systems detected by CM. Both CM and HM are based on logistic regression models trained with multispectral MSG data sets comprised of summer convective events in the middle Ebro Valley (Spain) between 2006 and 2010, and detected by the RGB (red-green-blue) visualization technique (CM) or C-band weather radar system of the University of León. By means of the logistic regression approach, the probability of identifying a cumulonimbus event with CM or a hail event with HM are computed by exploiting a proper selection of MSG wavelengths or their combination. A number of cloud physical properties (liquid water path, optical thickness and effective cloud drop radius) were used to physically interpret results of statistical models from a meteorological perspective, using a method based on these "ingredients". Finally, the HDT was applied to a new validation sample consisting of events during summer 2011. The overall probability of detection was 76.9 % and the false alarm ratio 16.7 %.

HUBBLE SPOTS NORTHERN HEMISPHERIC CLOUDS ON URANUS

NASA Technical Reports Server (NTRS)

2002-01-01

Using visible light, astronomers for the first time this century have detected clouds in the northern hemisphere of Uranus. The newest images, taken July 31 and Aug. 1, 1997 with NASA Hubble Space Telescope's Wide Field and Planetary Camera 2, show banded structure and multiple clouds. Using these images, Dr. Heidi Hammel (Massachusetts Institute of Technology) and colleagues Wes Lockwood (Lowell Observatory) and Kathy Rages (NASA Ames Research Center) plan to measure the wind speeds in the northern hemisphere for the first time. Uranus is sometimes called the 'sideways' planet, because its rotation axis is tipped more than 90 degrees from the planet's orbit around the Sun. The 'year' on Uranus lasts 84 Earth years, which creates extremely long seasons - winter in the northern hemisphere has lasted for nearly 20 years. Uranus has also been called bland and boring, because no clouds have been detectable in ground-based images of the planet. Even to the cameras of the Voyager spacecraft in 1986, Uranus presented a nearly uniform blank disk, and discrete clouds were detectable only in the southern hemisphere. Voyager flew over the planet's cloud tops near the dead of northern winter (when the northern hemisphere was completely shrouded in darkness). Spring has finally come to the northern hemisphere of Uranus. The newest images, both the visible-wavelength ones described here and those taken a few days earlier with the Near Infrared and Multi-Object Spectrometer (NICMOS) by Erich Karkoschka (University of Arizona), show a planet with banded structure and detectable clouds. Two images are shown here. The 'aqua' image (on the left) is taken at 5,470 Angstroms, which is near the human eye's peak response to wavelength. Color has been added to the image to show what a person on a spacecraft near Uranus might see. Little structure is evident at this wavelength, though with image-processing techniques, a small cloud can be seen near the planet's northern limb (rightmost edge). The 'red' image (on the right) is taken at 6,190 Angstroms, and is sensitive to absorption by methane molecules in the planet's atmosphere. The banded structure of Uranus is evident, and the small cloud near the northern limb is now visible. Scientists are expecting that the discrete clouds and banded structure may become even more pronounced as Uranus continues in its slow pace around the Sun. 'Some parts of Uranus haven't seen the Sun in decades,' says Dr. Hammel, 'and historical records suggest that we may see the development of more banded structure and patchy clouds as the planet's year progresses.' Some scientists have speculated that the winds of Uranus are not symmetric around the planet's equator, but no clouds were visible to test those theories. The new data will provide the opportunity to measure the northern winds. Hammel and colleagues expect to have results soon. Credits: Heidi Hammel (Massachusetts Institute of Technology), and NASA.

First Look at the Upper Tropospheric Ozone Mixing Ratio from OMI Estimated using the Cloud Slicing Technique

NASA Technical Reports Server (NTRS)

Bhartia, Pawan K.; Ziemke, Jerry; Chandra, Sushil; Joiner, Joanna; Vassilkov, Alexandra; Taylor, Steven; Yang, Kai; Ahn, Chang-Woo

2004-01-01

The Cloud Slicing technique has emerged as a powerful tool for the study of ozone in the upper troposphere. In this technique one looks at the variation with cloud height of the above-cloud column ozone derived from the backscattered ultraviolet instruments, such as TOMS, to determine the ozone mixing ratio. For this technique to work properly one needs an instrument with relatively good horizontal resolution with very good signal to noise in measuring above-cloud column ozone. In addition, one needs the (radiatively) effective cloud pressure rather than the cloud-top pressure, for the ultraviolet photons received by a satellite instrument are scattered from inside the cloud rather than from the top. For this study we use data from the OMI sensor, which was recently launched on the EOS Aura satellite. OMI is a W-Visible backscattering instrument with a nadir pixel size of 13 x 24 km. The effective cloud pressure is derived from a new algorithm based on Rotational Raman Scattering and O2-O2, absorption in the 340-400 nm band of OMI.

Comparison of Cloud Detection Using the CERES-MODIS Ed4 and LaRC AVHRR Cloud Masks and CALIPSO Vertical Feature Mask

NASA Astrophysics Data System (ADS)

Trepte, Q. Z.; Minnis, P.; Palikonda, R.; Bedka, K. M.; Sun-Mack, S.

2011-12-01

Accurate detection of cloud amount and distribution using satellite observations is crucial in determining cloud radiative forcing and earth energy budget. The CERES-MODIS (CM) Edition 4 cloud mask is a global cloud detection algorithm for application to Terra and Aqua MODIS data with the aid of other ancillary data sets. It is used operationally for the NASA's Cloud and Earth's Radiant Energy System (CERES) project. The LaRC AVHRR cloud mask, which uses only five spectral channels, is based on a subset of the CM cloud mask which employs twelve MODIS channels. The LaRC mask is applied to AVHRR data for the NOAA Climate Data Record Program. Comparisons among the CM Ed4, and LaRC AVHRR cloud masks and the CALIPSO Vertical Feature Mask (VFM) constitute a powerful means for validating and improving cloud detection globally. They also help us understand the strengths and limitations of the various cloud retrievals which use either active and passive satellite sensors. In this paper, individual comparisons will be presented for different types of clouds over various surfaces, including daytime and nighttime, and polar and non-polar regions. Additionally, the statistics of the global, regional, and zonal cloud occurrence and amount from the CERES Ed4, AVHRR cloud masks and CALIPSO VFM will be discussed.

Extraction of convective cloud parameters from Doppler Weather Radar MAX(Z) product using Image Processing Technique

NASA Astrophysics Data System (ADS)

Arunachalam, M. S.; Puli, Anil; Anuradha, B.

2016-07-01

In the present work continuous extraction of convective cloud optical information and reflectivity (MAX(Z) in dBZ) using online retrieval technique for time series data production from Doppler Weather Radar (DWR) located at Indian Meteorological Department, Chennai has been developed in MATLAB. Reflectivity measurements for different locations within the DWR range of 250 Km radii of circular disc area can be retrieved using this technique. It gives both time series reflectivity of point location and also Range Time Intensity (RTI) maps of reflectivity for the corresponding location. The Graphical User Interface (GUI) developed for the cloud reflectivity is user friendly; it also provides the convective cloud optical information such as cloud base height (CBH), cloud top height (CTH) and cloud optical depth (COD). This technique is also applicable for retrieving other DWR products such as Plan Position Indicator (Z, in dBZ), Plan Position Indicator (Z, in dBZ)-Close Range, Volume Velocity Processing (V, in knots), Plan Position Indicator (V, in m/s), Surface Rainfall Intensity (SRI, mm/hr), Precipitation Accumulation (PAC) 24 hrs at 0300UTC. Keywords: Reflectivity, cloud top height, cloud base, cloud optical depth

3D modeling of building indoor spaces and closed doors from imagery and point clouds.

PubMed

Díaz-Vilariño, Lucía; Khoshelham, Kourosh; Martínez-Sánchez, Joaquín; Arias, Pedro

2015-02-03

3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.

Ground-based Nighttime Cloud Detection Using a Commercial Digital Camera: Observations at Manila Observatory (14.64N, 121.07E)

NASA Astrophysics Data System (ADS)

Gacal, G. F. B.; Tan, F.; Antioquia, C. T.; Lagrosas, N.

2014-12-01

Cloud detection during nighttime poses a real problem to researchers because of a lack of optimum sensors that can specifically detect clouds during this time of the day. Hence, lidars and satellites are currently some of the instruments that are being utilized to determine cloud presence in the atmosphere. These clouds play a significant role in the night weather system for the reason that they serve as barriers of thermal radiation from the Earth and thereby reflecting this radiation back to the Earth. This effectively lowers the rate of decreasing temperature in the atmosphere at night. The objective of this study is to detect cloud occurrences at nighttime for the purpose of studying patterns of cloud occurrence and the effects of clouds on local weather. In this study, a commercial camera (Canon Powershot A2300) is operated continuously to capture nighttime clouds. The camera is situated inside a weather-proof box with a glass cover and is placed on the rooftop of the Manila Observatory building to gather pictures of the sky every 5min to observe cloud dynamics and evolution in the atmosphere. To detect pixels with clouds, the pictures are converted from its native JPEG to grayscale format. The pixels are then screened for clouds by looking at the values of pixels with and without clouds. In grayscale format, pixels with clouds have greater pixel values than pixels without clouds. Based on the observations, 0.34 of the maximum pixel value is enough to discern pixels with clouds from pixels without clouds. Figs. 1a & 1b are sample unprocessed pictures of cloudless night (May 22-23, 2014) and cloudy skies (May 23-24, 2014), respectively. Figs.1c and 1d show percentage of occurrence of nighttime clouds on May 22-23 and May 23-24, 2014, respectively. The cloud occurrence in a pixel is defined as the ratio of the number times when the pixel has clouds to the total number of observations. Fig. 1c shows less than 50% cloud occurrence while Fig. 1d shows cloud occurrence more than what is shown in Fig. 1c. These graphs show the capability of the camera to detect and measure the cloud occurrence at nighttime. Continuous collection of nighttime pictures is currently implemented. In regions where there is a dearth of scientific data, the measured nighttime cloud occurrence will serve as a baseline for future cloud studies in this part of the world.

HST Hot-Jupiter Transmission Spectral Survey: Clear Skies for Cool Saturn WASP-39b

NASA Astrophysics Data System (ADS)

Fischer, Patrick D.; Knutson, Heather A.; Sing, David K.; Henry, Gregory W.; Williamson, Michael W.; Fortney, Jonathan J.; Burrows, Adam S.; Kataria, Tiffany; Nikolov, Nikolay; Showman, Adam P.; Ballester, Gilda E.; Désert, Jean-Michel; Aigrain, Suzanne; Deming, Drake; Lecavelier des Etangs, Alain; Vidal-Madjar, Alfred

2016-08-01

We present the Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) optical transmission spectroscopy of the cool Saturn-mass exoplanet WASP-39b from 0.29-1.025 μm, along with complementary transit observations from Spitzer IRAC at 3.6 and 4.5 μm. The low density and large atmospheric pressure scale height of WASP-39b make it particularly amenable to atmospheric characterization using this technique. We detect a Rayleigh scattering slope as well as sodium and potassium absorption features; this is the first exoplanet in which both alkali features are clearly detected with the extended wings predicted by cloud-free atmosphere models. The full transmission spectrum is well matched by a clear H2-dominated atmosphere, or one containing a weak contribution from haze, in good agreement with the preliminary reduction of these data presented in Sing et al. WASP-39b is predicted to have a pressure-temperature profile comparable to that of HD 189733b and WASP-6b, making it one of the coolest transiting gas giants observed in our HST STIS survey. Despite this similarity, WASP-39b appears to be largely cloud-free, while the transmission spectra of HD 189733b and WASP-6b both indicate the presence of high altitude clouds or hazes. These observations further emphasize the surprising diversity of cloudy and cloud-free gas giant planets in short-period orbits and the corresponding challenges associated with developing predictive cloud models for these atmospheres.

The dynamic surface tension of atmospheric aerosol surfactants reveals new aspects of cloud activation.

PubMed

Nozière, Barbara; Baduel, Christine; Jaffrezo, Jean-Luc

2014-02-25

The activation of aerosol particles into cloud droplets in the Earth's atmosphere is both a key process for the climate budget and a main source of uncertainty. Its investigation is facing major experimental challenges, as no technique can measure the main driving parameters, the Raoult's term and surface tension, σ, for sub-micron atmospheric particles. In addition, the surfactant fraction of atmospheric aerosols could not be isolated until recently. Here we present the first dynamic investigation of the total surfactant fraction of atmospheric aerosols, evidencing adsorption barriers that limit their gradient (partitioning) in particles and should enhance their cloud-forming efficiency compared with current models. The results also show that the equilibration time of surfactants in sub-micron atmospheric particles should be beyond the detection of most on-line instruments. Such instrumental and theoretical shortcomings would be consistent with atmospheric and laboratory observations and could have limited the understanding of cloud activation until now.

SERS-active Au/SiO2 clouds in powder for rapid ex vivo breast adenocarcinoma diagnosis

PubMed Central

Cepeda-Pérez, Elisa; López-Luke, Tzarara; Salas, Pedro; Plascencia-Villa, Germán; Ponce, Arturo; Vivero-Escoto, Juan; José-Yacamán, Miguel; de la Rosa, Elder

2016-01-01

In the present work, we report a dry-based application technique of Au/SiO2 clouds in powder for rapid ex vivo adenocarcinoma diagnosis through surface-enhanced Raman scattering (SERS); using low laser power and an integration time of one second. Several characteristic Raman peaks frequently used for the diagnosis of breast adenocarcinoma in the range of the amide III are successfully enhanced by breading the tissue with Au/SiO2 powder. The SERS activity of these Au/SiO2 powders is attributed to their rapid rehydration upon contact with the wet tissues, which promotes the formation of gold nanoparticle aggregates. The propensity of the Au/SiO2 cloud structures to adsorb biomolecules in the vicinity of the gold nanoparticle clusters promotes the necessary conditions for SERS detection. In addition, electron microscopy, together with elemental analysis, have been used to confirm the structure of the new Au/SiO2 cloud material and to investigate its distribution in breast tissues. PMID:27375955

Detection of Ice Polar Stratospheric Clouds from Assimilation of Atmospheric Infrared Sounder Data

NASA Technical Reports Server (NTRS)

Stajner, Ivanka; Benson, Craig; Liu, Hui-Chun; Pawson, Steven; Chang, Ping; Riishojgaard, Lars Peter

2006-01-01

A novel technique is presented for detection of ice polar stratospheric clouds (PSCs) that form at extremely low temperatures in the lower polar stratosphere during winter. Temperature is a major factor in determining abundance of PSCs, which in turn provide surfaces for heterogeneous chemical reactions leading to ozone loss and radiative cooling. The technique infers the presence of ice PSCs using radiances from the Atmospheric Infrared Sounder (AIRS) in the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. Brightness temperatures are computed from short-term GEOS-5 forecasts for several hundred AIRS channels, using a radiation transfer module. The differences between collocated AIRS observations and these computed values are the observed-minus-forecast (O-F) residuals in the assimilation system. Because the radiation model assumes clear-sky conditions, we hypothesize that these O-F residuals contain quantitative information about PSCs. This is confirmed using sparse data from the Polar Ozone and Aerosol Measurement (POAM) III occultation instrument. The analysis focuses on 0-F residuals for the 6.79pm AIRS moisture channel. At coincident locations, when POAM III detects ice clouds, the AIRS O-F residuals for this channel are lower than -2K. When no ice PSCs are evident in POAM III data, the AIRS 0-F residuals are larger. Given this relationship, the high spatial density of AIRS data is used to construct maps of regions where 0-F residuals are lower than -2K, as a proxy for ice PSCs. The spatial scales and spatio-temporal variations of these PSCs in the Antarctic and Arctic are discussed on the basis of these maps.

Spatially Varying Spectrally Thresholds for MODIS Cloud Detection

NASA Technical Reports Server (NTRS)

Haines, S. L.; Jedlovec, G. J.; Lafontaine, F.

2004-01-01

The EOS science team has developed an elaborate global MODIS cloud detection procedure, and the resulting MODIS product (MOD35) is used in the retrieval process of several geophysical parameters to mask out clouds. While the global application of the cloud detection approach appears quite robust, the product has some shortcomings on the regional scale, often over determining clouds in a variety of settings, particularly at night. This over-determination of clouds can cause a reduction in the spatial coverage of MODIS derived clear-sky products. To minimize this problem, a new regional cloud detection method for use with MODIS data has been developed at NASA's Global Hydrology and Climate Center (GHCC). The approach is similar to that used by the GHCC for GOES data over the continental United States. Several spatially varying thresholds are applied to MODIS spectral data to produce a set of tests for detecting clouds. The thresholds are valid for each MODIS orbital pass, and are derived from 20-day composites of GOES channels with similar wavelengths to MODIS. This paper and accompanying poster will introduce the GHCC MODIS cloud mask, provide some examples, and present some preliminary validation.

Reassessing the effect of cloud type on Earth's energy balance

NASA Astrophysics Data System (ADS)

Hang, A.; L'Ecuyer, T.

2017-12-01

Cloud feedbacks depend critically on the characteristics of the clouds that change, their location and their environment. As a result, accurately predicting the impact of clouds on future climate requires a better understanding of individual cloud types and their spatial and temporal variability. This work revisits the problem of documenting the effects of distinct cloud regimes on Earth's radiation budget distinguishing cloud types according to their signatures in spaceborne active observations. Using CloudSat's multi-sensor radiative fluxes product that leverages high-resolution vertical cloud information from CloudSat, CALIPSO, and MODIS observations to provide the most accurate estimates of vertically-resolved radiative fluxes available to date, we estimate the global annual mean net cloud radiative effect at the top of the atmosphere to be -17.1 W m-2 (-44.2 W m-2 in the shortwave and 27.1 W m-2 in the longwave), slightly weaker than previous estimates from passive sensor observations. Multi-layered cloud systems, that are often misclassified using passive techniques but are ubiquitous in both hemispheres, contribute about -6.2 W m-2 of the net cooling effect, particularly at ITCZ and higher latitudes. Another unique aspect of this work is the ability of CloudSat and CALIPSO to detect cloud boundary information providing an improved capability to accurately discern the impact of cloud-type variations on surface radiation balance, a critical factor in modulating the disposition of excess energy in the climate system. The global annual net cloud radiative effect at the surface is estimated to be -24.8 W m-2 (-51.1 W m-2 in the shortwave and 26.3 W m-2 in the longwave), dominated by shortwave heating in multi-layered and stratocumulus clouds. Corresponding estimates of the effects of clouds on atmospheric heating suggest that clouds redistribute heat from poles to equator enhancing the general circulation.

Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

NASA Technical Reports Server (NTRS)

Wind, Galina; Platnick, Steven; King, Michael D.; Hubanks, Paul A,; Pavolonis, Michael J.; Heidinger, Andrew K.; Yang, Ping; Baum, Bryan A.

2009-01-01

Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Earth Observing System EOS Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that presents difficulties for retrieving cloud effective radius using single layer plane-parallel cloud models. The algorithm uses the MODIS 0.94 micron water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94 micron methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases

Arctic polar stratospheric cloud measurements by means of a four wavelength depolarization lidar

NASA Technical Reports Server (NTRS)

Stefanutti, L.; Castagnoli, F.; Delguasta, M.; Flesia, C.; Godin, S.; Kolenda, J.; Kneipp, H.; Kyro, Esko; Matthey, R.; Morandi, M.

1994-01-01

A four wavelength depolarization backscattering lidar has been operated during the European Arctic Stratospheric Ozone Experiment (EASOE) in Sodankyl, in the Finnish Arctic. The lidar performed measurements during the months of December 1991, January, February and March 1992. The Finnish Meteorological Institute during the same period launched regularly three Radiosondes per day, and three Ozone sondes per week. Both Mt. Pinatubo aerosols and Polar Stratospheric Clouds were measured. The use of four wavelengths, respectively at 355 nm, 532 nm , 750 nm, and 850 nm permits an inversion of the lidar data to determine aerosol particle size. The depolarization technique permits the identification of Polar Stratospheric Clouds. Frequent correlation between Ozone minima and peaks in the Mt. Pinatubo aerosol maxima were detected. Measurements were carried out both within and outside the Polar Vortex.

Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States

USGS Publications Warehouse

Jin, Suming; Homer, Collin G.; Yang, Limin; Xian, George; Fry, Joyce; Danielson, Patrick; Townsend, Philip A.

2013-01-01

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

Evolution and Advances in Satellite Analysis of Volcanoes

NASA Astrophysics Data System (ADS)

Dean, K. G.; Dehn, J.; Webley, P.; Bailey, J.

2008-12-01

Over the past 20 years satellite data used for monitoring and analysis of volcanic eruptions has evolved in terms of timeliness, access, distribution, resolution and understanding of volcanic processes. Initially satellite data was used for retrospective analysis but has evolved to proactive monitoring systems. Timely acquisition of data and the capability to distribute large data files paralleled advances in computer technology and was a critical component for near real-time monitoring. The sharing of these data and resulting discussions has improved our understanding of eruption processes and, even more importantly, their impact on society. To illustrate this evolution, critical scientific discoveries will be highlighted, including detection of airborne ash and sulfur dioxide, cloud-height estimates, prediction of ash cloud movement, and detection of thermal anomalies as precursor-signals to eruptions. AVO has been a leader in implementing many of these advances into an operational setting such as, automated eruption detection, database analysis systems, and remotely accessible web-based analysis systems. Finally, limitations resulting from trade-offs between resolution and how they impact some weakness in detection techniques and hazard assessments will be presented.

Evaluation of Malware Target Recognition Deployed in a Cloud-Based Fileserver Environment

DTIC Science & Technology

2012-03-01

many of these detection techniques could be evaded with simple obfuscation. Kolter and Maloof extend Schultz’s research in [KM04] and [KM06]. Their...69 [KM04] Jeremy Z. Kolter and Marcus A. Maloof. Learning to detect malicious executables in the wild. In Proceedings of the tenth ACM SIGKDD...international conference on Knowledge discovery and data mining, KDD ’04, pages 470–478, New York, NY, USA, 2004. ACM. [KM06] J.Z. Kolter and M.A. Maloof

HERBIG-HARO OBJECTS IN THE LUPUS I AND III MOLECULAR CLOUDS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Hongchi; Henning, Thomas

2009-10-15

We performed a deep search for Herbig-Haro (HH) objects toward the Lupus I and III clouds, covering a sky area of {approx} 1 and {approx} 0.5 deg{sup 2}, respectively. In total, 11 new HH objects, HH 981--991, are discovered. The HH objects both in Lupus I and in Lupus III tend to be concentrated in small areas. The HH objects detected in Lupus I are located in a region of radius 0.26 pc near the young star Sz 68. The abundance of HH objects shows that this region of the cloud is active in on-going star formation. HH objects inmore » the Lup III cloud are concentrated in the central part of the cloud around the Herbig Ae/Be stars HR 5999 and 6000. HH 981 and 982 in Lupus I are probably driven by the young brown dwarf SSTc2d J154457.9-342340 which has a mass of 50 M{sub J} . HH 990 and 991 in Lup III align well with the HH 600 jet emanating from the low-mass star Par-Lup3-4, and are probably excited by this low-mass star of spectral type M5. High proper motions for HH 228 W, E, and E2 are measured, which confirms that they are excited by the young star Th 28. In contrast, HH 78 exhibits no measurable proper motion in the time span of 18 years, indicating that HH 78 is unlikely part of the HH 228 flow. The HH objects in Lup I and III are generally weak in terms of brightness and dimension in comparison to HH objects we detected with the same technique in the R CrA and Cha I clouds. Through a comparison with the survey results from the Spitzer c2d program, we find that our optical survey is more sensitive, in terms of detection rate, than the Spitzer IRAC survey to high-velocity outflows in the Lup I and III clouds.« less

Street curb recognition in 3d point cloud data using morphological operations

NASA Astrophysics Data System (ADS)

Rodríguez-Cuenca, Borja; Concepción Alonso-Rodríguez, María; García-Cortés, Silverio; Ordóñez, Celestino

2015-04-01

Accurate and automatic detection of cartographic-entities saves a great deal of time and money when creating and updating cartographic databases. The current trend in remote sensing feature extraction is to develop methods that are as automatic as possible. The aim is to develop algorithms that can obtain accurate results with the least possible human intervention in the process. Non-manual curb detection is an important issue in road maintenance, 3D urban modeling, and autonomous navigation fields. This paper is focused on the semi-automatic recognition of curbs and street boundaries using a 3D point cloud registered by a mobile laser scanner (MLS) system. This work is divided into four steps. First, a coordinate system transformation is carried out, moving from a global coordinate system to a local one. After that and in order to simplify the calculations involved in the procedure, a rasterization based on the projection of the measured point cloud on the XY plane was carried out, passing from the 3D original data to a 2D image. To determine the location of curbs in the image, different image processing techniques such as thresholding and morphological operations were applied. Finally, the upper and lower edges of curbs are detected by an unsupervised classification algorithm on the curvature and roughness of the points that represent curbs. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. This method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. That point cloud comprises more than 6,000,000 points and covers a 400-meter street. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. That point cloud comprises 8,000,000 points and represents a 160-meter street. The proposed method provides success rates in curb recognition of over 85% in both datasets.

Detecting Super-Thin Clouds With Polarized Light

NASA Technical Reports Server (NTRS)

Sun, Wenbo; Videen, Gorden; Mishchenko, Michael I.

2014-01-01

We report a novel method for detecting cloud particles in the atmosphere. Solar radiation backscattered from clouds is studied with both satellite data and a radiative transfer model. A distinct feature is found in the angle of linear polarization of solar radiation that is backscattered from clouds. The dominant backscattered electric field from the clear-sky Earth-atmosphere system is nearly parallel to the Earth surface. However, when clouds are present, this electric field can rotate significantly away from the parallel direction. Model results demonstrate that this polarization feature can be used to detect super-thin cirrus clouds having an optical depth of only 0.06 and super-thin liquid water clouds having an optical depth of only 0.01. Such clouds are too thin to be sensed using any current passive satellite instruments.

Detecting Super-Thin Clouds with Polarized Sunlight

NASA Technical Reports Server (NTRS)

Sun, Wenbo; Videen, Gorden; Mishchenko, Michael I.

2014-01-01

We report a novel method for detecting cloud particles in the atmosphere. Solar radiation backscattered from clouds is studied with both satellite data and a radiative transfer model. A distinct feature is found in the angle of linear polarization of solar radiation that is backscattered from clouds. The dominant backscattered electric field from the clear-sky Earth-atmosphere system is nearly parallel to the Earth surface. However, when clouds are present, this electric field can rotate significantly away from the parallel direction. Model results demonstrate that this polarization feature can be used to detect super-thin cirrus clouds having an optical depth of only 0.06 and super-thin liquid water clouds having an optical depth of only 0.01. Such clouds are too thin to be sensed using any current passive satellite instruments.

Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

NASA Astrophysics Data System (ADS)

Janugani, S.; Jayaram, V.; Cabrera, S. D.; Rosiles, J. G.; Gill, T. E.; Rivera Rivera, N.

2009-05-01

In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.

Applicability Analysis of Cloth Simulation Filtering Algorithm for Mobile LIDAR Point Cloud

NASA Astrophysics Data System (ADS)

Cai, S.; Zhang, W.; Qi, J.; Wan, P.; Shao, J.; Shen, A.

2018-04-01

Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

Molecular clouds without detectable CO

NASA Technical Reports Server (NTRS)

Blitz, Leo; Bazell, David; Desert, F. Xavier

1990-01-01

The clouds identified by Desert, Bazell, and Boulanger (DBB clouds) in their search for high-latitude molecular clouds were observed in the CO (J = 1-0) line, but only 13 percent of the sample was detected. The remaining 87 percent are diffuse molecular clouds with CO abundances of about 10 to the -6th, a typical value for diffuse clouds. This hypothesis is shown to be consistent with Copernicus data. The DBB clouds are shown to ben an essentially complete catalog of diffuse molecular clouds in the solar vicinity. The total molecular surface density in the vicinity of the sun is then only about 20 percent greater than the 1.3 solar masses/sq pc determined by Dame et al. (1987). Analysis of the CO detections indicates that there is a sharp threshold in extinction of 0.25 mag before CO is detectable and is derived from the IRAS I(100) micron threshold of 4 MJy/sr. This threshold is presumably where the CO abundance exhibits a sharp increase

Molecular clouds without detectable CO

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blitz, L.; Bazell, D.; Desert, F.X.

1990-03-01

The clouds identified by Desert, Bazell, and Boulanger (DBB clouds) in their search for high-latitude molecular clouds were observed in the CO (J = 1-0) line, but only 13 percent of the sample was detected. The remaining 87 percent are diffuse molecular clouds with CO abundances of about 10 to the -6th, a typical value for diffuse clouds. This hypothesis is shown to be consistent with Copernicus data. The DBB clouds are shown to be an essentially complete catalog of diffuse molecular clouds in the solar vicinity. The total molecular surface density in the vicinity of the sun is thenmore » only about 20 percent greater than the 1.3 solar masses/sq pc determined by Dame et al. (1987). Analysis of the CO detections indicates that there is a sharp threshold in extinction of 0.25 mag before CO is detectable and is derived from the IRAS I(100) micron threshold of 4 MJy/sr. This threshold is presumably where the CO abundance exhibits a sharp increase 18 refs.« less

The Dependence of Cloud Property Trend Detection on Absolute Calibration Accuracy of Passive Satellite Sensors

NASA Astrophysics Data System (ADS)

Shea, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Zelinka, M. D.

2016-12-01

Detecting trends in climate variables on global, decadal scales requires highly accurate, stable measurements and retrieval algorithms. Trend uncertainty depends on its magnitude, natural variability, and instrument and retrieval algorithm accuracy and stability. We applied a climate accuracy framework to quantify the impact of absolute calibration on cloud property trend uncertainty. The cloud properties studied were cloud fraction, effective temperature, optical thickness, and effective radius retrieved using the Clouds and the Earth's Radiant Energy System (CERES) Cloud Property Retrieval System, which uses Moderate-resolution Imaging Spectroradiometer measurements (MODIS). Modeling experiments from the fifth phase of the Climate Model Intercomparison Project (CMIP5) agree that net cloud feedback is likely positive but disagree regarding its magnitude, mainly due to uncertainty in shortwave cloud feedback. With the climate accuracy framework we determined the time to detect trends for instruments with various calibration accuracies. We estimated a relationship between cloud property trend uncertainty, cloud feedback, and Equilibrium Climate Sensitivity and also between effective radius trend uncertainty and aerosol indirect effect trends. The direct relationship between instrument accuracy requirements and climate model output provides the level of instrument absolute accuracy needed to reduce climate model projection uncertainty. Different cloud types have varied radiative impacts on the climate system depending on several attributes, such as their thermodynamic phase, altitude, and optical thickness. Therefore, we also conducted these studies by cloud types for a clearer understanding of instrument accuracy requirements needed to detect changes in their cloud properties. Combining this information with the radiative impact of different cloud types helps to prioritize among requirements for future satellite sensors and understanding the climate detection capabilities of existing sensors.

Comparison of cloud optical depth and cloud mask applying BRDF model-based background surface reflectance

NASA Astrophysics Data System (ADS)

Kim, H. W.; Yeom, J. M.; Woo, S. H.

2017-12-01

Over the thin cloud region, satellite can simultaneously detect the reflectance from thin clouds and land surface. Since the mixed reflectance is not the exact cloud information, the background surface reflectance should be eliminated to accurately distinguish thin cloud such as cirrus. In the previous research, Kim et al (2017) was developed the cloud masking algorithm using the Geostationary Ocean Color Imager (GOCI), which is one of significant instruments for Communication, Ocean, and Meteorology Satellite (COMS). Although GOCI has 8 spectral channels including visible and near infra-red spectral ranges, the cloud masking has quantitatively reasonable result when comparing with MODIS cloud mask (Collection 6 MYD35). Especially, we noticed that this cloud masking algorithm is more specialized in thin cloud detections through the validation with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. Because this cloud masking method was concentrated on eliminating background surface effects from the top-of-atmosphere (TOA) reflectance. Applying the difference between TOA reflectance and the bi-directional reflectance distribution function (BRDF) model-based background surface reflectance, cloud areas both thick cloud and thin cloud can be discriminated without infra-red channels which were mostly used for detecting clouds. Moreover, when the cloud mask result was utilized as the input data when simulating BRDF model and the optimized BRDF model-based surface reflectance was used for the optimized cloud masking, the probability of detection (POD) has higher value than POD of the original cloud mask. In this study, we examine the correlation between cloud optical depth (COD) and its cloud mask result. Cloud optical depths mostly depend on the cloud thickness, the characteristic of contents, and the size of cloud contents. COD ranges from less than 0.1 for thin clouds to over 1000 for the huge cumulus due to scattering by droplets. With the cloud optical depth of CALIPSO, the cloud masking result can be more improved since we can figure out how deep cloud is. To validate the cloud mask and the correlation result, the atmospheric retrieval will be computed to compare the difference between TOA reflectance and the simulated surface reflectance.

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

DOE PAGES

Ahn, M. H.; Han, D.; Won, H. Y.; ...

2015-02-03

For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperaturemore » and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.« less

Comparison of the MODIS Multilayer Cloud Detection and Thermodynamic Phase Products with CALIPSO and CloudSat

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Gala; Holz, Robert E.; Ackerman, Steven A.; Nagle, Fred W.

2008-01-01

CALIPSO and CloudSat, launched in June 2006, provide global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the "Collection 5" stream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 h resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, and CloudSat radar measurements, we investigate the global performance of the thermodynamic phase and multilayer cloud detection algorithms.

MR-based detection of individual histotripsy bubble clouds formed in tissues and phantoms.

PubMed

Allen, Steven P; Hernandez-Garcia, Luis; Cain, Charles A; Hall, Timothy L

2016-11-01

To demonstrate that MR sequences can detect individual histotripsy bubble clouds formed inside intact tissues. A line-scan and an EPI sequence were sensitized to histotripsy by inserting a bipolar gradient whose lobes bracketed the lifespan of a histotripsy bubble cloud. Using a 7 Tesla, small-bore scanner, these sequences monitored histotripsy clouds formed in an agar phantom and in vitro porcine liver and brain. The bipolar gradients were adjusted to apply phase with k-space frequencies of 10, 300 or 400 cm -1 . Acoustic pressure amplitude was also varied. Cavitation was simultaneously monitored using a passive cavitation detection system. Each image captured local signal loss specific to an individual bubble cloud. In the agar phantom, this signal loss appeared only when the transducer output exceeded the cavitation threshold pressure. In tissues, bubble clouds were immediately detected when the gradients created phase with k-space frequencies of 300 and 400 cm -1 . When the gradients created phase with a k-space frequency of 10 cm -1 , individual bubble clouds were not detectable until many acoustic pulses had been applied to the tissue. Cavitation-sensitive MR-sequences can detect single histotripsy bubble clouds formed in biologic tissue. Detection is influenced by the sensitizing gradients and treatment history. Magn Reson Med 76:1486-1493, 2016. © 2015 International Society for Magnetic Resonance in Medicine. © 2015 International Society for Magnetic Resonance in Medicine.

Automated detection of cloud and cloud-shadow in single-date Landsat imagery using neural networks and spatial post-processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hughes, Michael J.; Hayes, Daniel J

2014-01-01

Use of Landsat data to answer ecological questions is contingent on the effective removal of cloud and cloud shadow from satellite images. We develop a novel algorithm to identify and classify clouds and cloud shadow, \\textsc{sparcs}: Spacial Procedures for Automated Removal of Cloud and Shadow. The method uses neural networks to determine cloud, cloud-shadow, water, snow/ice, and clear-sky membership of each pixel in a Landsat scene, and then applies a set of procedures to enforce spatial rules. In a comparison to FMask, a high-quality cloud and cloud-shadow classification algorithm currently available, \\textsc{sparcs} performs favorably, with similar omission errors for cloudsmore » (0.8% and 0.9%, respectively), substantially lower omission error for cloud-shadow (8.3% and 1.1%), and fewer errors of commission (7.8% and 5.0%). Additionally, textsc{sparcs} provides a measure of uncertainty in its classification that can be exploited by other processes that use the cloud and cloud-shadow detection. To illustrate this, we present an application that constructs obstruction-free composites of images acquired on different dates in support of algorithms detecting vegetation change.« less

Probing non polar interstellar molecules through their protonated form: Detection of protonated cyanogen (NCCNH+)★

PubMed Central

Agúndez, M.; Cernicharo, J.; de Vicente, P.; Marcelino, N.; Roueff, E.; Fuente, A.; Gerin, M.; Guélin, M.; Albo, C.; Barcia, A.; Barbas, L.; Bolaño, R.; Colomer, F.; Diez, M. C.; Gallego, J. D.; Gómez-González, J.; López-Fernández, I.; López-Fernández, J. A.; López-Pérez, J. A.; Malo, I.; Serna, J. M.; Tercero, F.

2015-01-01

Cyanogen (NCCN) is the simplest member of the series of dicyanopolyynes. It has been hypothesized that this family of molecules can be important constituents of interstellar and circumstellar media, although the lack of a permanent electric dipole moment prevents its detection through radioastronomical techniques. Here we present the first solid evidence of the presence of cyanogen in interstellar clouds through the detection of its protonated form toward the cold dark clouds TMC-1 and L483. Protonated cyanogen (NCCNH+) has been identified through the J = 5 – 4 and J = 10 – 9 rotational transitions using the 40m radiotelescope of Yebes and the IRAM 30m telescope. We derive beam averaged column densities for NCCNH+ of (8.6 ± 4.4) × 1010 cm−2 in TMC-1 and (3.9 ± 1.8) × 1010 cm−2 in L483, which translate to fairly low fractional abundances relative to H2, in the range (1-10) × 10−12. The chemistry of protonated molecules in dark clouds is discussed, and it is found that, in general terms, the abundance ratio between the protonated and non protonated forms of a molecule increases with increasing proton affinity. Our chemical model predicts an abundance ratio NCCNH+/NCCN of ~ 10−4, which implies that the abundance of cyanogen in dark clouds could be as high as (1-10) × 10−8 relative to H2, i.e., comparable to that of other abundant nitriles such as HCN, HNC, and HC3N. PMID:26543239

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm

PubMed Central

Abdulhamid, Shafi’i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. PMID:27384239

Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm.

PubMed

Abdulhamid, Shafi'i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid

2016-01-01

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.

Eye-Safe Lidar System for Pesticide Spray Drift Measurement

PubMed Central

Gregorio, Eduard; Rocadenbosch, Francesc; Sanz, Ricardo; Rosell-Polo, Joan R.

2015-01-01

Spray drift is one of the main sources of pesticide contamination. For this reason, an accurate understanding of this phenomenon is necessary in order to limit its effects. Nowadays, spray drift is usually studied by using in situ collectors which only allow time-integrated sampling of specific points of the pesticide clouds. Previous research has demonstrated that the light detection and ranging (lidar) technique can be an alternative for spray drift monitoring. This technique enables remote measurement of pesticide clouds with high temporal and distance resolution. Despite these advantages, the fact that no lidar instrument suitable for such an application is presently available has appreciably limited its practical use. This work presents the first eye-safe lidar system specifically designed for the monitoring of pesticide clouds. Parameter design of this system is carried out via signal-to-noise ratio simulations. The instrument is based on a 3-mJ pulse-energy erbium-doped glass laser, an 80-mm diameter telescope, an APD optoelectronic receiver and optomechanically adjustable components. In first test measurements, the lidar system has been able to measure a topographic target located over 2 km away. The instrument has also been used in spray drift studies, demonstrating its capability to monitor the temporal and distance evolution of several pesticide clouds emitted by air-assisted sprayers at distances between 50 and 100 m. PMID:25658395

Site-resolved imaging of a bosonic Mott insulator using ytterbium atoms

NASA Astrophysics Data System (ADS)

Miranda, Martin; Inoue, Ryotaro; Tambo, Naoki; Kozuma, Mikio

2017-10-01

We demonstrate site-resolved imaging of a strongly correlated quantum system without relying on laser cooling techniques during fluorescence imaging. We observe the formation of Mott shells in the insulating regime and realize thermometry in an atomic cloud. This work proves the feasibility of the noncooled approach and opens the door to extending the detection technology to new atomic species.

A cloud masking algorithm for EARLINET lidar systems

NASA Astrophysics Data System (ADS)

Binietoglou, Ioannis; Baars, Holger; D'Amico, Giuseppe; Nicolae, Doina

2015-04-01

Cloud masking is an important first step in any aerosol lidar processing chain as most data processing algorithms can only be applied on cloud free observations. Up to now, the selection of a cloud-free time interval for data processing is typically performed manually, and this is one of the outstanding problems for automatic processing of lidar data in networks such as EARLINET. In this contribution we present initial developments of a cloud masking algorithm that permits the selection of the appropriate time intervals for lidar data processing based on uncalibrated lidar signals. The algorithm is based on a signal normalization procedure using the range of observed values of lidar returns, designed to work with different lidar systems with minimal user input. This normalization procedure can be applied to measurement periods of only few hours, even if no suitable cloud-free interval exists, and thus can be used even when only a short period of lidar measurements is available. Clouds are detected based on a combination of criteria including the magnitude of the normalized lidar signal and time-space edge detection performed using the Sobel operator. In this way the algorithm avoids misclassification of strong aerosol layers as clouds. Cloud detection is performed using the highest available time and vertical resolution of the lidar signals, allowing the effective detection of low-level clouds (e.g. cumulus humilis). Special attention is given to suppress false cloud detection due to signal noise that can affect the algorithm's performance, especially during day-time. In this contribution we present the details of algorithm, the effect of lidar characteristics (space-time resolution, available wavelengths, signal-to-noise ratio) to detection performance, and highlight the current strengths and limitations of the algorithm using lidar scenes from different lidar systems in different locations across Europe.

An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

2003-01-01

An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

Enhancing a Simple MODIS Cloud Mask Algorithm for the Landsat Data Continuity Mission

NASA Technical Reports Server (NTRS)

Wilson, Michael J.; Oreopoulos, Lazarous

2011-01-01

The presence of clouds in images acquired by the Landsat series of satellites is usually an undesirable, but generally unavoidable fact. With the emphasis of the program being on land imaging, the suspended liquid/ice particles of which clouds are made of fully or partially obscure the desired observational target. Knowing the amount and location of clouds in a Landsat scene is therefore valuable information for scene selection, for making clear-sky composites from multiple scenes, and for scheduling future acquisitions. The two instruments in the upcoming Landsat Data Continuity Mission (LDCM) will include new channels that will enhance our ability to detect high clouds which are often also thin in the sense that a large fraction of solar radiation can pass through them. This work studies the potential impact of these new channels on enhancing LDCM's cloud detection capabilities compared to previous Landsat missions. We revisit a previously published scheme for cloud detection and add new tests to capture more of the thin clouds that are harder to detect with the more limited arsenal channels. Since there are no Landsat data yet that include the new LDCM channels, we resort to data from another instrument, MODIS, which has these bands, as well as the other bands of LDCM, to test the capabilities of our new algorithm. By comparing our revised scheme's performance against the performance of the official MODIS cloud detection scheme, we conclude that the new scheme performs better than the earlier scheme which was not very good at thin cloud detection.

ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification

PubMed Central

Huang, Lei; Kang, Wenjun; Bartom, Elizabeth; Onel, Kenan; Volchenboum, Samuel; Andrade, Jorge

2015-01-01

Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud. PMID:26271043

SUCCESS Evidence for Cirrus Cloud Ice Nucleation Mechanisms

NASA Technical Reports Server (NTRS)

Jensen, Eric; Gore, Warren J. Y. (Technical Monitor)

1997-01-01

During the SUCCESS mission, several measurements were made which should improve our understanding of ice nucleation processes in cirrus clouds. Temperature and water vapor concentration were made with a variety of instruments on the NASA DC-8. These observations should provide accurate upper tropospheric humidities. In particular, we will evaluate what humidities are required for ice nucleation. Preliminary results suggest that substantial supersaturations frequently exist in the upper troposphere. The leading-edge region of wave-clouds (where ice nucleation occurs) was sampled extensively at temperatures near -40 and -60C. These observations should give precise information about conditions required for ice nucleation. In addition, we will relate the observed aerosol composition and size distributions to the ice formation observed to evaluate the role of soot or mineral particles on ice nucleation. As an alternative technique for determining what particles act as ice nuclei, numerous samples of aerosols inside ice crystals were taken. In some cases, large numbers of aerosols were detected in each crystal, indicating that efficient scavenging occurred. Analysis of aerosols in ice crystals when only one particle per crystal was detected should help with the ice nucleation issue. Direct measurements of the ice nucleating activity of ambient aerosols drawn into airborne cloud chambers were also made. Finally, measurements of aerosols and ice crystals in contrails should indicate whether aircraft exhaust soot particles are effective ice nuclei.

Satellite-derived vertical profiles of temperature and dew point for mesoscale weather forecast

NASA Astrophysics Data System (ADS)

Masselink, Thomas; Schluessel, P.

1995-12-01

Weather forecast-models need spatially high resolutioned vertical profiles of temperature and dewpoint for their initialisation. These profiles can be supplied by a combination of data from the Tiros-N Operational Vertical Sounder (TOVS) and the imaging Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbiting sate!- lites. In cloudy cases the profiles derived from TOVS data only are of insufficient accuracy. The stanthrd deviations from radiosonde ascents or numerical weather analyses likely exceed 2 K in temperature and 5Kin dewpoint profiles. It will be shown that additional cloud information as retrieved from AVHIRR allows a significant improvement in theaccuracy of vertical profiles. The International TOVS Processing Package (ITPP) is coupled to an algorithm package called AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) where parameters like cloud fraction and cloud-top temperature are determined with higher accuracy than obtained from TOVS retrieval alone. Furthermore, a split-window technique is applied to the cloud-free AVHRR imagery in order to derive more accurate surface temperatures than can be obtained from the pure TOVS retrieval. First results of the impact of AVHRR cloud detection on the quality of the profiles are presented. The temperature and humidity profiles of different retrieval approaches are validated against analyses of the European Centre for Medium-Range Weatherforecasts.

Design and operation of the pellet charge exchange diagnostic for measurement of energetic confined alphas and tritons on TFTR

DOE Office of Scientific and Technical Information (OSTI.GOV)

Medley, S.S.; Duong, H.H.; Fisher, R.K.

1996-05-01

Radially-resolved energy and density distributions of the energetic confined alpha particles in D-T experiments on TFTR are being measured by active neutral particle analysis using low-Z impurity pellet injection. When injected into a high temperature plasma, an impurity pellet (e.g. Lithium or Boron) rapidly ablates forming an elongated cloud which is aligned with the magnetic field and moves with the pellet. This ablation cloud provides a dense target with which the alpha particles produced in D-T fusion reactions can charge exchange. A small fraction of the alpha particles incident on the pellet ablation cloud will be converted to helium neutralsmore » whose energy is essentially unchanged by the charge transfer process. By measuring the resultant helium neutrals escaping from the plasma using a mass and energy resolving charge exchange analyzer, this technique offers a direct measurement of the energy distribution of the incident high-energy alpha particles. Other energetic ion species can be detected as well, such as tritons generated in D-D plasmas and H or He{sup 3} RF-driven minority ion tails. The diagnostic technique and its application on TFTR are described in detail.« less

Atmospheric electrical detection of organized convection.

PubMed

Markson, R

1975-06-20

Relatively simple atmospheric electrical instrumentation carried on a small aircraft constitutes a flexible and sensitive system for detecting organized convection. Data can be obtained close to the sea surface, and low-velocity flight enhances the spatial resolution. With a slow-flying airplane or powered glider, it may be possible to trace the circulation of individual convection cells and to investigate the trajectory of air which forms cumulus clouds, one of the major unsolved problems in tropical meteorology. Since space charge near the ocean surface was found on some days to be organized on a horizontal scale equivalent to the cumulus cloud scale, this suggests that some of the air which forms maritime cumulus clouds may come from within a few meters of the ocean and that atmospheric electrical instrumentation may have the potential for tracing air from the sea surface to the clouds. Although the atmospheric electrical instrumentation technique described here cannot be used for direct measurement of air velocity, it may be possible to develop model that can be used to calculate air velocities from electric field data. Even though with the technique described here it is not possible to make direct measurements of wind velocity, airborne electric field records can provide useful information about convection by delineating patterns in the wind field and structural features of thermals (rising bodies of relatively warm air) and by making possible the remote detection of thermals (29). Future plans include attempting to trace interfaces between adjacent roll vortices from the sea surface through the depth of the mixed layer (i) by flying the aircraft parallel to the wind so as to nullify the horizontal electric field (measured between wing-tip probes) while ascending and descending along the interface between adjacent roll vortices and (ii) by measuring vertical and horizontal potential gradient variations at different flight levels (30). The sensitivity of atmospheric electrical instrumentation to the top of the mixed layer and structure within it can be used to explore another important problem in boundary layer convection-why convective cloud cover and oceanic rainfall are greater at night than during the day(31). Workers in atmospheric electricity have long recognized that their domain is strongly controlled by turbulence in the lower atmosphere, and many have believed that the most effective use of atmospheric electrical techniques to assist meteorological research would be in studying exchange processes. Reiter [see (8)] effectively extended atmospheric electrical studies of boundary layer phenomena through a height range by mounting instruments on cable cars traveling between the valley floor and mountain tops in the Alps. The airborne measurements described here extend this approach. Relating the electrical structure of the atmosphere to its dynamic structure poses an interesting problem which may contribute to our understanding of the atmosphere.

Raman Lidar Measurements of Water Vapor and Cirrus Clouds During The Passage of Hurricane Bonnie

NASA Technical Reports Server (NTRS)

Whiteman, D. N.; Evans, K. D.; Demoz, B.; Starr, D OC.; Eloranta, E. W.; Tobin, D.; Feltz, W.; Jedlovec, G. J.; Gutman, S. I.; Schwemmer, G. K.;

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

NASA Technical Reports Server (NTRS)

Smith, William L.; Ebert, Elizabeth

1990-01-01

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Algorithm for Automated Detection of Edges of Clouds

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Merceret, Francis J.

2006-01-01

An algorithm processes cloud-physics data gathered in situ by an aircraft, along with reflectivity data gathered by ground-based radar, to determine whether the aircraft is inside or outside a cloud at a given time. A cloud edge is deemed to be detected when the in/out state changes, subject to a hysteresis constraint. Such determinations are important in continuing research on relationships among lightning, electric charges in clouds, and decay of electric fields with distance from cloud edges.

Improvements to GOES Twilight Cloud Detection over the ARM SGP

NASA Technical Reports Server (NTRS)

Yost, c. R.; Trepte, Q.; Khaiyer, M. M.; Palikonda, R.; Nguyen, L.

2007-01-01

The current ARM satellite cloud products derived from Geostationary Operational Environmental Satellite (GOES) data provide continuous coverage of many cloud properties over the ARM Southern Great Plains domain. However, discontinuities occur during daylight near the terminator, a time period referred to here as twilight. This poster presentation will demonstrate the improvements in cloud detection provided by the improved cloud mask algorithm as well as validation of retrieved cloud properties using surface observations from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) site.

Development of a cloud point extraction and spectrophotometry-based microplate method for the determination of nitrite in human urine and blood.

PubMed

Zhao, Jiao; Lu, Yunhui; Fan, Chongyang; Wang, Jun; Yang, Yaling

2015-02-05

A novel and simple method for the sensitive determination of trace amounts of nitrite in human urine and blood has been developed by combination of cloud point extraction (CPE) and microplate assay. The method is based on the Griess reaction and the reaction product is extracted into nonionic surfactant Triton-X114 using CPE technique. In this study, decolorization treatment of urine and blood was applied to overcome the interference of matrix and enhance the sensitivity of nitrite detection. Multi-sample can be simultaneously detected thanks to a 96-well microplate technique. The effects of different operating parameters such as type of decolorizing agent, concentration of surfactant (Triton X-114), addition of (NH4)2SO4, extraction temperature and time, interfering elements were studied and optimum conditions were obtained. Under the optimum conditions, a linear calibration graph was obtained in the range of 10-400 ng mL(-1) of nitrite with limit of detection (LOD) of 2.5 ng mL(-1). The relative standard deviation (RSD) for determination of 100 ng mL(-1) of nitrite was 2.80%. The proposed method was successfully applied for the determination of nitrite in the urine and blood samples with recoveries of 92.6-101.2%. Copyright © 2014 Elsevier B.V. All rights reserved.

HST HOT-JUPITER TRANSMISSION SPECTRAL SURVEY: CLEAR SKIES FOR COOL SATURN WASP-39b

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fischer, Patrick D.; Knutson, Heather A.; Sing, David K.

We present the Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) optical transmission spectroscopy of the cool Saturn-mass exoplanet WASP-39b from 0.29-1.025 μ m, along with complementary transit observations from Spitzer IRAC at 3.6 and 4.5 μ m. The low density and large atmospheric pressure scale height of WASP-39b make it particularly amenable to atmospheric characterization using this technique. We detect a Rayleigh scattering slope as well as sodium and potassium absorption features; this is the first exoplanet in which both alkali features are clearly detected with the extended wings predicted by cloud-free atmosphere models. The full transmission spectrummore » is well matched by a clear H{sub 2}-dominated atmosphere, or one containing a weak contribution from haze, in good agreement with the preliminary reduction of these data presented in Sing et al. WASP-39b is predicted to have a pressure-temperature profile comparable to that of HD 189733b and WASP-6b, making it one of the coolest transiting gas giants observed in our HST STIS survey. Despite this similarity, WASP-39b appears to be largely cloud-free, while the transmission spectra of HD 189733b and WASP-6b both indicate the presence of high altitude clouds or hazes. These observations further emphasize the surprising diversity of cloudy and cloud-free gas giant planets in short-period orbits and the corresponding challenges associated with developing predictive cloud models for these atmospheres.« less

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

NASA Technical Reports Server (NTRS)

Joiner, J.; Vasilkov, A. P.; Bhartia, P. K.; Wind, G.; Platnick, S.; Menzel, W. P.

2010-01-01

The detection of mUltiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

A comparison between high-altitude horizon photography and direct sampling of El Chichon cloud particles

NASA Technical Reports Server (NTRS)

Coletti, A.; Hofmann, D. J.; Rosen, J. M.

1986-01-01

Perturbations to the visible radiation by the El Chichon aerosol layers in the stratosphere observed on May 18, 1982 in Laredo, Texas using in situ, time-lapsed photography are analyzed. The densitometric data are compared with optical counter data. Good correlation is detected for the scattered light intensities of the sky estimated with the two techniques. It is observed that the optical thickness of the stratosphere from 18.8 km to the top of the atmosphere = 0.18 and the residual optical thickness at 27 km = 0.0007. The relationship between the isodensity contours and the height of the observations, cloud cover, specific vertical aerosol distribution, and earth curvature is examined.

Single-shot imaging of trapped Fermi gas

NASA Astrophysics Data System (ADS)

Gajda, Mariusz; Mostowski, Jan; Sowiński, Tomasz; Załuska-Kotur, Magdalena

2016-07-01

Recently developed techniques allow for simultaneous measurements of the positions of all ultra-cold atoms in a trap with high resolution. Each such single-shot experiment detects one element of the quantum ensemble formed by the cloud of atoms. Repeated single-shot measurements can be used to determine all correlations between particle positions as opposed to standard measurements that determine particle density or two-particle correlations only. In this paper we discuss the possible outcomes of such single-shot measurements in the case of cloud of ultra-cold noninteracting Fermi atoms. We show that the Pauli exclusion principle alone leads to correlations between particle positions that originate from unexpected spatial structures formed by the atoms.

Progress in Near Real-Time Volcanic Cloud Observations Using Satellite UV Instruments

NASA Astrophysics Data System (ADS)

Krotkov, N. A.; Yang, K.; Vicente, G.; Hughes, E. J.; Carn, S. A.; Krueger, A. J.

2011-12-01

Volcanic clouds from explosive eruptions can wreak havoc in many parts of the world, as exemplified by the 2010 eruption at the Eyjafjöll volcano in Iceland, which caused widespread disruption to air traffic and resulted in economic impacts across the globe. A suite of satellite-based systems offer the most effective means to monitor active volcanoes and to track the movement of volcanic clouds globally, providing critical information for aviation hazard mitigation. Satellite UV sensors, as part of this suite, have a long history of making unique near-real time (NRT) measurements of sulfur dioxide (SO2) and ash (aerosol Index) in volcanic clouds to supplement operational volcanic ash monitoring. Recently a NASA application project has shown that the use of near real-time (NRT,i.e., not older than 3 h) Aura/OMI satellite data produces a marked improvement in volcanic cloud detection using SO2 combined with Aerosol Index (AI) as a marker for ash. An operational online NRT OMI AI and SO2 image and data product distribution system was developed in collaboration with the NOAA Office of Satellite Data Processing and Distribution. Automated volcanic eruption alarms, and the production of volcanic cloud subsets for multiple regions are provided through the NOAA website. The data provide valuable information in support of the U.S. Federal Aviation Administration goal of a safe and efficient National Air Space. In this presentation, we will highlight the advantages of UV techniques and describe the advances in volcanic SO2 plume height estimation and enhanced volcanic ash detection using hyper-spectral UV measurements, illustrated with Aura/OMI observations of recent eruptions. We will share our plan to provide near-real-time volcanic cloud monitoring service using the Ozone Mapping and Profiler Suite (OMPS) on the Joint Polar Satellite System (JPSS).

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

NASA Technical Reports Server (NTRS)

Bedka, Kristopher M.; Dworak, Richard; Brunner, Jason; Feltz, Wayne

2012-01-01

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

A multispectral study of an extratropical cyclone with Nimbus 3 medium resolution infrared radiometer data

NASA Technical Reports Server (NTRS)

Holub, R.; Shenk, W. E.

1973-01-01

Four registered channels (0.2 to 4, 6.5 to 7, 10 to 11, and 20 to 23 microns) of the Nimbus 3 Medium Resolution Infrared Radiometer (MRIR) were used to study 24-hr changes in the structure of an extratropical cyclone during a 6-day period in May 1969. Use of a stereographic-horizon map projection insured that the storm was mapped with a single perspective throughout the series and allowed the convenient preparation of 24-hr difference maps of the infrared radiation fields. Single-channel and multispectral analysis techniques were employed to establish the positions and vertical slopes of jetstreams, large cloud systems, and major features of middle and upper tropospheric circulation. Use of these techniques plus the difference maps and continuity of observation allowed the early detection of secondary cyclones developing within the circulation of the primary cyclone. An automated, multispectral cloud-type identification technique was developed, and comparisons that were made with conventional ship reports and with high-resolution visual data from the image dissector camera system showed good agreement.

A Fourier approach to cloud motion estimation

NASA Technical Reports Server (NTRS)

Arking, A.; Lo, R. C.; Rosenfield, A.

1977-01-01

A Fourier technique is described for estimating cloud motion from pairs of pictures using the phase of the cross spectral density. The method allows motion estimates to be made for individual spatial frequencies, which are related to cloud pattern dimensions. Results obtained are presented and compared with the results of a Fourier domain cross correlation scheme. Using both artificial and real cloud data show that the technique is relatively sensitive to the presence of mixtures of motions, changes in cloud shape, and edge effects.

Automated, per pixel Cloud Detection from High-Resolution VNIR Data

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

Cloud-based MOTIFSIM: Detecting Similarity in Large DNA Motif Data Sets.

PubMed

Tran, Ngoc Tam L; Huang, Chun-Hsi

2017-05-01

We developed the cloud-based MOTIFSIM on Amazon Web Services (AWS) cloud. The tool is an extended version from our web-based tool version 2.0, which was developed based on a novel algorithm for detecting similarity in multiple DNA motif data sets. This cloud-based version further allows researchers to exploit the computing resources available from AWS to detect similarity in multiple large-scale DNA motif data sets resulting from the next-generation sequencing technology. The tool is highly scalable with expandable AWS.

Daytime Cloud Property Retrievals Over the Arctic from Multispectral MODIS Data

NASA Technical Reports Server (NTRS)

Spangenberg, Douglas A.; Trepte, Qing; Minnis, Patrick; Uttal, Taneil

2004-01-01

Improving climate model predictions over Earth's polar regions requires a complete understanding of polar clouds properties. Passive satellite remote sensing techniques can be used to retrieve macro and microphysical properties of polar cloud systems. However, over the Arctic, there is minimal contrast between clouds and the background snow surface observed in satellite data, especially for visible wavelengths. This makes it difficult to identify clouds and retrieve their properties from space. Variable snow and ice cover, temperature inversions, and the predominance of mixed-phase clouds further complicate cloud property identification. For this study, the operational Clouds and the Earth s Radiant Energy System (CERES) cloud mask is first used to discriminate clouds from the background surface in Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data. A solar-infrared infrared nearinfrared technique (SINT) first used by Platnick et al. (2001) is used here to retrieve cloud properties over snow and ice covered regions.

Pattern recognition of satellite cloud imagery for improved weather prediction

NASA Technical Reports Server (NTRS)

Gautier, Catherine; Somerville, Richard C. J.; Volfson, Leonid B.

1986-01-01

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products.

Comparing Two Independent Satellite-Based Algorithms for Detecting and Tracking Ash Clouds by Using SEVIRI Sensor.

PubMed

Falconieri, Alfredo; Cooke, Michael C; Filizzola, Carolina; Marchese, Francesco; Pergola, Nicola; Tramutoli, Valerio

2018-01-27

The Eyjafjallajökull (Iceland) volcanic eruption of April-May 2010 caused unprecedented air-traffic disruption in Northern Europe, revealing some important weaknesses of current operational ash-monitoring and forecasting systems and encouraging the improvement of methods and procedures for supporting the activities of Volcanic Ash Advisory Centers (VAACs) better. In this work, we compare two established satellite-based algorithms for ash detection, namely RST ASH and the operational London VAAC method, both exploiting sensor data of the spinning enhanced visible and infrared imager (SEVIRI). We analyze similarities and differences in the identification of ash clouds during the different phases of the Eyjafjallajökull eruption. The work reveals, in some cases, a certain complementary behavior of the two techniques, whose combination might improve the identification of ash-affected areas in specific conditions. This is indicated by the quantitative comparison of the merged SEVIRI ash product, achieved integrating outputs of the RST ASH and London VAAC methods, with independent atmospheric infrared sounder (AIRS) DDA (dust-detection algorithm) observations.

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data.

PubMed

Jun, Goo; Wing, Mary Kate; Abecasis, Gonçalo R; Kang, Hyun Min

2015-06-01

The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies. © 2015 Jun et al.; Published by Cold Spring Harbor Laboratory Press.

Comparing Two Independent Satellite-Based Algorithms for Detecting and Tracking Ash Clouds by Using SEVIRI Sensor

PubMed Central

Cooke, Michael C.; Filizzola, Carolina

2018-01-01

The Eyjafjallajökull (Iceland) volcanic eruption of April–May 2010 caused unprecedented air-traffic disruption in Northern Europe, revealing some important weaknesses of current operational ash-monitoring and forecasting systems and encouraging the improvement of methods and procedures for supporting the activities of Volcanic Ash Advisory Centers (VAACs) better. In this work, we compare two established satellite-based algorithms for ash detection, namely RSTASH and the operational London VAAC method, both exploiting sensor data of the spinning enhanced visible and infrared imager (SEVIRI). We analyze similarities and differences in the identification of ash clouds during the different phases of the Eyjafjallajökull eruption. The work reveals, in some cases, a certain complementary behavior of the two techniques, whose combination might improve the identification of ash-affected areas in specific conditions. This is indicated by the quantitative comparison of the merged SEVIRI ash product, achieved integrating outputs of the RSTASH and London VAAC methods, with independent atmospheric infrared sounder (AIRS) DDA (dust-detection algorithm) observations. PMID:29382058

Invisible Cirrus Clouds

NASA Technical Reports Server (NTRS)

2002-01-01

The Moderate-resolution Imaging Spectroradiometer's (MODIS') cloud detection capability is so sensitive that it can detect clouds that would be indistinguishable to the human eye. This pair of images highlights MODIS' ability to detect what scientists call 'sub-visible cirrus.' The image on top shows the scene using data collected in the visible part of the electromagnetic spectrum-the part our eyes can see. Clouds are apparent in the center and lower right of the image, while the rest of the image appears to be relatively clear. However, data collected at 1.38um (lower image) show that a thick layer of previously undetected cirrus clouds obscures the entire scene. These kinds of cirrus are called 'sub-visible' because they can't be detected using only visible light. MODIS' 1.38um channel detects electromagnetic radiation in the infrared region of the spectrum. These images were made from data collected on April 4, 2000. Image courtesy Mark Gray, MODIS Atmosphere Team

Fluorescent pseudomonads isolated from Hebridean cloud and rain water produce biosurfactants but do not cause ice nucleation

NASA Astrophysics Data System (ADS)

Ahern, H. E.; Walsh, K. A.; Hill, T. C. J.; Moffett, B. F.

2007-02-01

Microorganisms were discovered in clouds over 100 years ago but information on bacterial community structure and function is limited. Clouds may not only be a niche within which bacteria could thrive but they might also influence dynamic processes using ice nucleating and cloud condensing abilities. Cloud and rain samples were collected from two mountains in the Outer Hebrides, NW Scotland, UK. Community composition was determined using a combination of amplified 16S ribosomal DNA restriction analysis and sequencing. 256 clones yielded 100 operational taxonomic units (OTUs) of which half were related to bacteria from terrestrial psychrophilic environments. Cloud samples were dominated by a mixture of fluorescent Pseudomonas spp., some of which have been reported to be ice nucleators. It was therefore possible that these bacteria were using the ice nucleation (IN) gene to trigger the Bergeron-Findeisen process of raindrop formation as a mechanism for dispersal. In this study the IN gene was not detected in any of the isolates using both polymerase chain reaction (PCR) and differential scanning calorimetry (DSC). Instead 55% of the total isolates from both cloud and rain samples displayed significant biosurfactant activity when analyzed using the drop-collapse technique. All isolates were characterised as fluorescent pseudomonads. Surfactants have been found to be very important in lowering atmospheric critical supersaturations required for the activation of aerosols into cloud condensation nuclei (CCN). It is also known that surfactants influence cloud droplet size and increase cloud lifetime and albedo. Some bacteria are known to act as CCN and so it is conceivable that these fluorescent pseudomonads are using surfactants to facilitate their activation from aerosols into CCN. This would allow water scavenging,~countering desiccation, and assist in their widespread dispersal.

Ice-nucleation negative fluorescent pseudomonads isolated from Hebridean cloud and rain water produce biosurfactants

NASA Astrophysics Data System (ADS)

Ahern, H. E.; Walsh, K. A.; Hill, T. C. J.; Moffett, B. F.

2006-10-01

Microorganisms were discovered in clouds over 100 years ago but information on bacterial community structure and function is limited. Clouds may not only be a niche within which bacteria could thrive but they might also influence dynamic processes using ice nucleating and cloud condensing abilities. Cloud and rain samples were collected from two mountains in the Outer Hebrides, NW Scotland, UK. Community composition was determined using a combination of amplified 16S ribosomal DNA restriction analysis and sequencing. 256 clones yielded 100 operational taxonomic units (OTUs) of which half were related to bacteria from terrestrial psychrophilic environments. Cloud samples were dominated by a mixture of fluorescent Pseudomonas spp., some of which have been reported to be ice nucleators. It was therefore possible that these bacteria were using the ice nucleation (IN) gene to trigger the Bergeron-Findeisen process of raindrop formation as a mechanism for dispersal. In this study the IN gene was not detected in any of the isolates using both polymerase chain reaction (PCR) and differential scanning calorimetry (DSC). Instead 55% of the total isolates from both cloud and rain samples displayed significant biosurfactant activity when analyzed using the drop-collapse technique. All were characterised as fluorescent pseudomonads. Surfactants have been found to be very important in lowering atmospheric critical supersaturations required for the activation of aerosols into cloud condensation nuclei (CCN). It is also known that surfactants influence cloud droplet size and increase cloud lifetime and albedo. Some bacteria are known to act as CCN and so it is conceivable that these fluorescent pseudomonads are using surfactants to facilitate their activation from aerosols into CCN. This would allow water scavenging, countering desiccation, and assist in their widespread dispersal.

Cloud and aerosol studies using combined CPL and MAS data

NASA Astrophysics Data System (ADS)

Vaughan, Mark A.; Rodier, Sharon; Hu, Yongxiang; McGill, Matthew J.; Holz, Robert E.

2004-11-01

Current uncertainties in the role of aerosols and clouds in the Earth's climate system limit our abilities to model the climate system and predict climate change. These limitations are due primarily to difficulties of adequately measuring aerosols and clouds on a global scale. The A-train satellites (Aqua, CALIPSO, CloudSat, PARASOL, and Aura) will provide an unprecedented opportunity to address these uncertainties. The various active and passive sensors of the A-train will use a variety of measurement techniques to provide comprehensive observations of the multi-dimensional properties of clouds and aerosols. However, to fully achieve the potential of this ensemble requires a robust data analysis framework to optimally and efficiently map these individual measurements into a comprehensive set of cloud and aerosol physical properties. In this work we introduce the Multi-Instrument Data Analysis and Synthesis (MIDAS) project, whose goal is to develop a suite of physically sound and computationally efficient algorithms that will combine active and passive remote sensing data in order to produce improved assessments of aerosol and cloud radiative and microphysical properties. These algorithms include (a) the development of an intelligent feature detection algorithm that combines inputs from both active and passive sensors, and (b) identifying recognizable multi-instrument signatures related to aerosol and cloud type derived from clusters of image pixels and the associated vertical profile information. Classification of these signatures will lead to the automated identification of aerosol and cloud types. Testing of these new algorithms is done using currently existing and readily available active and passive measurements from the Cloud Physics Lidar and the MODIS Airborne Simulator, which simulate, respectively, the CALIPSO and MODIS A-train instruments.

A Personal Storm Warning Service

NASA Technical Reports Server (NTRS)

1994-01-01

Although lightning detection systems operated by government agencies, utilities and other businesses provide storm warnings, this information often does not reach the public until some time after the observations have been made. A low-cost personal lightning detector offers a significant safety advantage to private flyers, boaters, golfers and others. Developed by Airborne Research Associates, the detectors originated in Space Shuttle tests of an optical lightning detection technique. The commercial device is pointed toward a cloud to detect invisible intracloud lightning by sensing subtle changes in light presence. The majority of the sales have been to golf courses. Additional products and more advanced applications are in progress.

Investigation of Advanced Radar Techniques for Atmospheric Hazard Detection with Airborne Weather Radar

NASA Technical Reports Server (NTRS)

Pazmany, Andrew L.

2014-01-01

In 2013 ProSensing Inc. conducted a study to investigate the hazard detection potential of aircraft weather radars with new measurement capabilities, such as multi-frequency, polarimetric and radiometric modes. Various radar designs and features were evaluated for sensitivity, measurement range and for detecting and quantifying atmospheric hazards in wide range of weather conditions. Projected size, weight, power consumption and cost of the various designs were also considered. Various cloud and precipitation conditions were modeled and used to conduct an analytic evaluation of the design options. This report provides an overview of the study and summarizes the conclusions and recommendations.

Detecting high-frequency gravitational waves with optically levitated sensors.

PubMed

Arvanitaki, Asimina; Geraci, Andrew A

2013-02-15

We propose a tunable resonant sensor to detect gravitational waves in the frequency range of 50-300 kHz using optically trapped and cooled dielectric microspheres or microdisks. The technique we describe can exceed the sensitivity of laser-based gravitational wave observatories in this frequency range, using an instrument of only a few percent of their size. Such a device extends the search volume for gravitational wave sources above 100 kHz by 1 to 3 orders of magnitude, and could detect monochromatic gravitational radiation from the annihilation of QCD axions in the cloud they form around stellar mass black holes within our galaxy due to the superradiance effect.

4-D cloud properties from passive satellite data and applications to resolve the flight icing threat to aircraft

NASA Astrophysics Data System (ADS)

Smith, William L., Jr.

The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. This thesis develops new techniques for interpreting cloud products derived from satellite data to infer the flight icing threat to aircraft in a wide range of cloud conditions. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water cloud systems, ice and liquid water content profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. Retrievals of the SLW content embedded within overlapping clouds are mapped to the icing threat using guidance from an airfoil modeling study. Compared to PIREPS, the satellite icing detection and intensity accuracies are found to be about 90% and 70%, respectively. Mean differences between the imager IWC retrievals with those from CloudSat and Calipso are less than 30%. This level of closure in the cloud water budget can only be achieved by correcting for errors in the imager retrievals due to the simplifying but poor assumption that deep optically thick clouds are single-phase and vertically homogeneous. When applied to geostationary satellite data, the profiling method provides a real-time characterization of clouds in 4-D. This research should improve the utility of satellite imager data for quantitatively diagnosing and predicting clouds and their effects in weather and climate applications.

Tiny, Dusty, Galactic HI Clouds: The GALFA-HI Compact Cloud Catalog

NASA Astrophysics Data System (ADS)

Saul, Destry R.; Putman, M. E.; Peek, J. G.

2013-01-01

The recently published GALFA-HI Compact Cloud Catalog contains 2000 nearby neutral hydrogen clouds under 20' in angular size detected with a machine-vision algorithm in the Galactic Arecibo L-Band Feed Array HI survey (GALFA-HI). At a distance of 1kpc, the compact clouds would typically be 1 solar mass and 1pc in size. We observe that nearly all of the compact clouds that are classified as high velocity (> 90 km/s) are near previously-identified high velocity complexes. We separate the compact clouds into populations based on velocity, linewidth, and position. We have begun to search for evidence of dust in these clouds using IRIS and have detections in several populations.

DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

NASA Astrophysics Data System (ADS)

Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

2017-12-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Ground and satellite-based remote sensing of mineral dust using AERI spectra and MODIS thermal infrared window brightness temperatures

NASA Astrophysics Data System (ADS)

Hansell, Richard Allen, Jr.

The radiative effects of dust aerosol on our climate system have yet to be fully understood and remain a topic of contemporary research. To investigate these effects, detection/retrieval methods for dust events over major dust outbreak and transport areas have been developed using satellite and ground-based approaches. To this end, both the shortwave and longwave surface radiative forcing of dust aerosol were investigated. The ground-based remote sensing approach uses the Atmospheric Emitted Radiance Interferometer brightness temperature spectra to detect mineral dust events and to retrieve their properties. Taking advantage of the high spectral resolution of the AERI instrument, absorptive differences in prescribed thermal IR window sub-band channels were exploited to differentiate dust from cirrus clouds. AERI data collected during the UAE2 at Al-Ain UAE was employed for dust retrieval. Assuming a specified dust composition model a priori and using the light scattering programs of T-matrix and the finite difference time domain methods for oblate spheroids and hexagonal plates, respectively, dust optical depths have been retrieved and compared to those inferred from a collocated and coincident AERONET sun-photometer dataset. The retrieved optical depths were then used to determine the dust longwave surface forcing during the UAE2. Likewise, dust shortwave surface forcing is investigated employing a differential technique from previous field studies. The satellite-based approach uses MODIS thermal infrared brightness temperature window data for the simultaneous detection/separation of mineral dust and cirrus clouds. Based on the spectral variability of dust emissivity at the 3.75, 8.6, 11 and 12 mum wavelengths, the D*-parameter, BTD-slope and BTD3-11 tests are combined to identify dust and cirrus. MODIS data for the three dust-laden scenes have been analyzed to demonstrate the effectiveness of this detection/separation method. Detected daytime dust and cloud coverage for the Persian Gulf case compare reasonably well to those from the "Deep Blue" algorithm developed at NASA-GSFC. The nighttime dust/cloud detection for the cases surrounding Cape Verde and Niger, West Africa has been validated by comparing to coincident and collocated ground-based micro-pulse lidar measurements.

Optical Algorithm for Cloud Shadow Detection Over Water

DTIC Science & Technology

2013-02-01

REPORT DATE (DD-MM-YYYY) 05-02-2013 2. REPORT TYPE Journal Article 3. DATES COVERED (From ■ To) 4. TITLE AND SUBTITLE Optical Algorithm for Cloud...particularly over humid tropical regions. Throughout the year, about two-thirds of the Earth’s surface is always covered by clouds [1]. The problem...V. Khlopenkov and A. P. Trishchenko, "SPARC: New cloud, snow , cloud shadow detection scheme for historical I-km AVHHR data over Canada," / Atmos

Improvements to the CERES Cloud Detection Algorithm using Himawari 8 Data and Validation using CALIPSO and CATS Lidar Observations

NASA Astrophysics Data System (ADS)

Trepte, Q.; Minnis, P.; Palikonda, R.; Yost, C. R.; Rodier, S. D.; Trepte, C. R.; McGill, M. J.

2016-12-01

Geostationary satellites provide continuous cloud and meteorological observations important for weather forecasting and for understanding climate processes. The Himawari-8 satellite represents a new generation of measurement capabilities with significantly improved resolution and enhanced spectral information. The satellite was launched in October 2014 by the Japanese Meteorological Agency and is centered at 140° E to provide coverage over eastern Asia and the western Pacific region. A cloud detection algorithm was developed as part of the CERES Cloud Mask algorithm using the Advanced Himawari Imager (AHI), a 16 channel multi-spectral imager. The algorithm was originally designed for use with Meteosat Second Generation (MSG) data and has been adapted for Himawari-8 AHI measurements. This paper will describe the improvements in the Himawari cloud mask including daytime ocean low cloud and aerosol discrimination, nighttime thin cirrus detection, and Australian desert and coastal cloud detection. The statistics from matched CERES Himawari cloud mask results with CALIPSO lidar data and with new observations from the CATS lidar will also be presented. A feature of the CATS instrument on board the International Space Station is that it gives information at different solar viewing times to examine the diurnal variation of clouds and this provides an ability to evaluate the performance of the cloud mask for different sun angles.

A holistic image segmentation framework for cloud detection and extraction

NASA Astrophysics Data System (ADS)

Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yufeng; Ling, Haibin; Chen, Genshe

2013-05-01

Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The problem of image segmentation is considered as a "clustering game". Within this context, the notion of a cluster is equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step, we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our cloud detection framework on a video clip provides supportive results.

Using Word Clouds to Develop Proactive Learners

ERIC Educational Resources Information Center

Miley, Frances; Read, Andrew

2011-01-01

This article examines student responses to a technique for summarizing electronically available information based on word frequency. Students used this technique to create word clouds, using those word clouds to enhance personal and small group study. This is a qualitative study. Small focus groups were used to obtain student feedback. Feedback…

Four dimensional observations of clouds from geosynchronous orbit using stereo display and measurement techniques on an interactive information processing system

NASA Technical Reports Server (NTRS)

Hasler, A. F.; Desjardins, M.; Shenk, W. E.

1979-01-01

Simultaneous Geosynchronous Operational Environmental Satellite (GOES) 1 km resolution visible image pairs can provide quantitative three dimensional measurements of clouds. These data have great potential for severe storms research and as a basic parameter measurement source for other areas of meteorology (e.g. climate). These stereo cloud height measurements are not subject to the errors and ambiguities caused by unknown cloud emissivity and temperature profiles that are associated with infrared techniques. This effort describes the display and measurement of stereo data using digital processing techniques.

Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands

NASA Technical Reports Server (NTRS)

Kuze, Akihiko; Chance, Kelly V.

1994-01-01

Cloud height and cloud coverage detection are important for total ozone retrieval using ultraviolet and visible scattered light. Use of the O2 A and B bands, around 761 and 687 nm, by a satellite-borne instrument of moderately high spectral resolution viewing in the nadir makes it possible to detect cloud top height and related parameters, including fractional coverage. The measured values of a satellite-borne spectrometer are convolutions of the instrument slit function and the atmospheric transmittance between cloud top and satellite. Studies here determine the optical depth between a satellite orbit and the Earth or cloud top height to high accuracy using FASCODE 3. Cloud top height and a cloud coverage parameter are determined by least squares fitting to calculated radiance ratios in the oxygen bands. A grid search method is used to search the parameter space of cloud top height and the coverage parameter to minimize an appropriate sum of squares of deviations. For this search, nonlinearity of the atmospheric transmittance (i.e., leverage based on varying amounts of saturation in the absorption spectrum) is important for distinguishing between cloud top height and fractional coverage. Using the above-mentioned method, an operational cloud detection algorithm which uses minimal computation time can be implemented.

Standoff detection: classification of biological aerosols using laser induced fluorescence (LIF) technique

NASA Astrophysics Data System (ADS)

Hausmann, Anita; Duschek, Frank; Fischbach, Thomas; Pargmann, Carsten; Aleksejev, Valeri; Poryvkina, Larisa; Sobolev, Innokenti; Babichenko, Sergey; Handke, Jürgen

2014-05-01

The challenges of detecting hazardous biological materials are manifold: Such material has to be discriminated from other substances in various natural surroundings. The detection sensitivity should be extremely high. As living material may reproduce itself, already one single bacterium may represent a high risk. Of course, identification should be quite fast with a low false alarm rate. Up to now, there is no single technique to solve this problem. Point sensors may collect material and identify it, but the problems of fast identification and especially of appropriate positioning of local collectors are sophisticated. On the other hand, laser based standoff detection may instantaneously provide the information of some accidental spillage of material by detecting the generated thin cloud. LIF technique may classify but hardly identify the substance. A solution can be the use of LIF technique in a first step to collect primary data and - if necessary- followed by utilizing these data for an optimized positioning of point sensors. We perform studies on an open air laser test range at distances between 20 and 135 m applying LIF technique to detect and classify aerosols. In order to employ LIF capability, we use a laser source emitting two wavelengths alternatively, 280 and 355 nm, respectively. Moreover, the time dependence of fluorescence spectra is recorded by a gated intensified CCD camera. Signal processing is performed by dedicated software for spectral pattern recognition. The direct comparison of all results leads to a basic classification of the various compounds.

The Cloud Detection and UV Monitoring Experiment (CLUE)

NASA Technical Reports Server (NTRS)

Barbier, L.; Loh, E.; Sokolsky, P.; Streitmatter, R.

2004-01-01

We propose a large-area, low-power instrument to perform CLoud detection and Ultraviolet monitoring, CLUE. CLUE will combine the W detection capabilities of the NIGHTGLOW payload, with an array of infrared sensors to perform cloud slicing measurements. Missions such as EUSO and OWL which seek to measure UHE cosmic-rays at 1W20 eV use the atmosphere as a fluorescence detector. CLUE will provide several important correlated measurements for these missions, including: monitoring the atmospheric W emissions &om 330 - 400 nm, determining the ambient cloud cover during those W measurements (with active LIDAR), measuring the optical depth of the clouds (with an array of narrow band-pass IR sensors), and correlating LIDAR and IR cloud cover measurements. This talk will describe the instrument as we envision it.

Detection of hydrogen sulfide above the clouds in Uranus's atmosphere

NASA Astrophysics Data System (ADS)

Irwin, Patrick G. J.; Toledo, Daniel; Garland, Ryan; Teanby, Nicholas A.; Fletcher, Leigh N.; Orton, Glenn A.; Bézard, Bruno

2018-04-01

Visible-to-near-infrared observations indicate that the cloud top of the main cloud deck on Uranus lies at a pressure level of between 1.2 bar and 3 bar. However, its composition has never been unambiguously identified, although it is widely assumed to be composed primarily of either ammonia or hydrogen sulfide (H2S) ice. Here, we present evidence of a clear detection of gaseous H2S above this cloud deck in the wavelength region 1.57-1.59 μm with a mole fraction of 0.4-0.8 ppm at the cloud top. Its detection constrains the deep bulk sulfur/nitrogen abundance to exceed unity (>4.4-5.0 times the solar value) in Uranus's bulk atmosphere, and places a lower limit on the mole fraction of H2S below the observed cloud of (1.0 -2.5 ) ×1 0-5. The detection of gaseous H2S at these pressure levels adds to the weight of evidence that the principal constituent of 1.2-3-bar cloud is likely to be H2S ice.

Detection of hydrogen sulfide above the clouds in Uranus's atmosphere

NASA Astrophysics Data System (ADS)

Irwin, Patrick G. J.; Toledo, Daniel; Garland, Ryan; Teanby, Nicholas A.; Fletcher, Leigh N.; Orton, Glenn A.; Bézard, Bruno

2018-05-01

Visible-to-near-infrared observations indicate that the cloud top of the main cloud deck on Uranus lies at a pressure level of between 1.2 bar and 3 bar. However, its composition has never been unambiguously identified, although it is widely assumed to be composed primarily of either ammonia or hydrogen sulfide (H2S) ice. Here, we present evidence of a clear detection of gaseous H2S above this cloud deck in the wavelength region 1.57-1.59 μm with a mole fraction of 0.4-0.8 ppm at the cloud top. Its detection constrains the deep bulk sulfur/nitrogen abundance to exceed unity (>4.4-5.0 times the solar value) in Uranus's bulk atmosphere, and places a lower limit on the mole fraction of H2S below the observed cloud of (1.0 -2.5 ) ×1 0-5. The detection of gaseous H2S at these pressure levels adds to the weight of evidence that the principal constituent of 1.2-3-bar cloud is likely to be H2S ice.

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-04-01

The cloud processing scheme APOLLO (Avhrr Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. While building upon the physical principles having served well in the original APOLLO a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is not performed as a binary yes/no decision based on these physical principals but is expressed as cloud probability for each satellite pixel. Consequently the outcome of the algorithm can be tuned from clear confident to cloud confident depending on the purpose. The probabilistic approach allows to retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for the application with large amounts of historical satellite data. Thus the radiative transfer solution is approximated by the same two stream approach which also had been used for the original APOLLO. This allows the algorithm to be robust enough for being applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e. within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from on NOAA-18 are presented.

Infrared Cloud Imager Development for Atmospheric Optical Communication Characterization, and Measurements at the JPL Table Mountain Facility

NASA Astrophysics Data System (ADS)

Nugent, P. W.; Shaw, J. A.; Piazzolla, S.

2013-02-01

The continuous demand for high data return in deep space and near-Earth satellite missions has led NASA and international institutions to consider alternative technologies for high-data-rate communications. One solution is the establishment of wide-bandwidth Earth-space optical communication links, which require (among other things) a nearly obstruction-free atmospheric path. Considering the atmospheric channel, the most common and most apparent impairments on Earth-space optical communication paths arise from clouds. Therefore, the characterization of the statistical behavior of cloud coverage for optical communication ground station candidate sites is of vital importance. In this article, we describe the development and deployment of a ground-based, long-wavelength infrared cloud imaging system able to monitor and characterize the cloud coverage. This system is based on a commercially available camera with a 62-deg diagonal field of view. A novel internal-shutter-based calibration technique allows radiometric calibration of the camera, which operates without a thermoelectric cooler. This cloud imaging system provides continuous day-night cloud detection with constant sensitivity. The cloud imaging system also includes data-processing algorithms that calculate and remove atmospheric emission to isolate cloud signatures, and enable classification of clouds according to their optical attenuation. Measurements of long-wavelength infrared cloud radiance are used to retrieve the optical attenuation (cloud optical depth due to absorption and scattering) in the wavelength range of interest from visible to near-infrared, where the cloud attenuation is quite constant. This article addresses the specifics of the operation, calibration, and data processing of the imaging system that was deployed at the NASA/JPL Table Mountain Facility (TMF) in California. Data are reported from July 2008 to July 2010. These data describe seasonal variability in cloud cover at the TMF site, with cloud amount (percentage of cloudy pixels) peaking at just over 51 percent during February, of which more than 60 percent had optical attenuation exceeding 12 dB at wavelengths in the range from the visible to the near-infrared. The lowest cloud amount was found during August, averaging 19.6 percent, and these clouds were mostly optically thin, with low attenuation.

Contributions of Heterogeneous Ice Nucleation, Large-Scale Circulation, and Shallow Cumulus Detrainment to Cloud Phase Transition in Mixed-Phase Clouds with NCAR CAM5

NASA Astrophysics Data System (ADS)

Liu, X.; Wang, Y.; Zhang, D.; Wang, Z.

2016-12-01

Mixed-phase clouds consisting of both liquid and ice water occur frequently at high-latitudes and in mid-latitude storm track regions. This type of clouds has been shown to play a critical role in the surface energy balance, surface air temperature, and sea ice melting in the Arctic. Cloud phase partitioning between liquid and ice water determines the cloud optical depth of mixed-phase clouds because of distinct optical properties of liquid and ice hydrometeors. The representation and simulation of cloud phase partitioning in state-of-the-art global climate models (GCMs) are associated with large biases. In this study, the cloud phase partition in mixed-phase clouds simulated from the NCAR Community Atmosphere Model version 5 (CAM5) is evaluated against satellite observations. Observation-based supercooled liquid fraction (SLF) is calculated from CloudSat, MODIS and CPR radar detected liquid and ice water paths for clouds with cloud-top temperatures between -40 and 0°C. Sensitivity tests with CAM5 are conducted for different heterogeneous ice nucleation parameterizations with respect to aerosol influence (Wang et al., 2014), different phase transition temperatures for detrained cloud water from shallow convection (Kay et al., 2016), and different CAM5 model configurations (free-run versus nudged winds and temperature, Zhang et al., 2015). A classical nucleation theory-based ice nucleation parameterization in mixed-phase clouds increases the SLF especially at temperatures colder than -20°C, and significantly improves the model agreement with observations in the Arctic. The change of transition temperature for detrained cloud water increases the SLF at higher temperatures and improves the SLF mostly over the Southern Ocean. Even with the improved SLF from the ice nucleation and shallow cumulus detrainment, the low SLF biases in some regions can only be improved through the improved circulation with the nudging technique. Our study highlights the challenges of representations of large-scale moisture transport, cloud microphysics, ice nucleation, and cumulus detrainment in order to improve the mixed-phase transition in GCMs.

Modeling the Performance of Direct-Detection Doppler Lidar Systems in Real Atmospheres

NASA Technical Reports Server (NTRS)

McGill, Matthew J.; Hart, William D.; McKay, Jack A.; Spinhirne, James D.

1999-01-01

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems has assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar systems: the double-edge and the multi-channel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only about 10-20% compared to nighttime performance, provided a proper solar filter is included in the instrument design.

Wind shear predictive detector technology study status

NASA Technical Reports Server (NTRS)

Gandolfi, C.

1990-01-01

Among the different elements to be investigated when considering the Wind Shear hazard, the Aeronautical Navigation Technical Service (STNA/3E), whose task is to participate in the development of new technologies and equipments, focused its effort on airborne and ground sensors for the detection of low-level wind shear. The first task, initiated in 1986, consists in the evaluation of three candidate techniques for forward-looking sensors: lidar, sodar, and radar. No development is presently foreseen for an infrared based air turbulence advance warning system although some flight experiments took place in the 70's. A Thomson infrared radiometer was then installed on an Air France Boeing 707 to evaluate its capability of detecting clear air turbulence. The conclusion showed that this technique was apparently able to detect cloud layers but that additional experiments were needed; on the other hand, the rarity of the phenomenon and the difficulty in operating on a commercial aircraft were also mentioned.

Enhancing Security by System-Level Virtualization in Cloud Computing Environments

NASA Astrophysics Data System (ADS)

Sun, Dawei; Chang, Guiran; Tan, Chunguang; Wang, Xingwei

Many trends are opening up the era of cloud computing, which will reshape the IT industry. Virtualization techniques have become an indispensable ingredient for almost all cloud computing system. By the virtual environments, cloud provider is able to run varieties of operating systems as needed by each cloud user. Virtualization can improve reliability, security, and availability of applications by using consolidation, isolation, and fault tolerance. In addition, it is possible to balance the workloads by using live migration techniques. In this paper, the definition of cloud computing is given; and then the service and deployment models are introduced. An analysis of security issues and challenges in implementation of cloud computing is identified. Moreover, a system-level virtualization case is established to enhance the security of cloud computing environments.

Study and Application on Cloud Covered Rate for Agroclimatical Distribution Using In Guangxi Based on Modis Data

NASA Astrophysics Data System (ADS)

Yang, Xin; Zhong, Shiquan; Sun, Han; Tan, Zongkun; Li, Zheng; Ding, Meihua

Based on analyzing of the physical characteristics of cloud and importance of cloud in agricultural production and national economy, cloud is a very important climatic resources such as temperature, precipitation and solar radiation. Cloud plays a very important role in agricultural climate division .This paper analyzes methods of cloud detection based on MODIS data in China and Abroad . The results suggest that Quanjun He method is suitable to detect cloud in Guangxi. State chart of cloud cover in Guangxi is imaged by using Quanjun He method .We find out the approach of calculating cloud covered rate by using the frequency spectrum analysis. At last, the Guangxi is obtained. Taking Rongxian County Guangxi as an example, this article analyze the preliminary application of cloud covered rate in distribution of Rong Shaddock pomelo . Analysis results indicate that cloud covered rate is closely related to quality of Rong Shaddock pomelo.

Evaluation and Applications of Cloud Climatologies from CALIOP

NASA Technical Reports Server (NTRS)

Winker, David; Getzewitch, Brian; Vaughan, Mark

2008-01-01

Clouds have a major impact on the Earth radiation budget and differences in the representation of clouds in global climate models are responsible for much of the spread in predicted climate sensitivity. Existing cloud climatologies, against which these models can be tested, have many limitations. The CALIOP lidar, carried on the CALIPSO satellite, has now acquired over two years of nearly continuous cloud and aerosol observations. This dataset provides an improved basis for the characterization of 3-D global cloudiness. Global average cloud cover measured by CALIOP is about 75%, significantly higher than for existing cloud climatologies due to the sensitivity of CALIOP to optically thin cloud. Day/night biases in cloud detection appear to be small. This presentation will discuss detection sensitivity and other issues associated with producing a cloud climatology, characteristics of cloud cover statistics derived from CALIOP data, and applications of those statistics.

Hubble Spots Northern Hemispheric Clouds on Uranus

NASA Technical Reports Server (NTRS)

1997-01-01

Using visible light, astronomers for the first time this century have detected clouds in the northern hemisphere of Uranus. The newest images, taken July 31 and Aug. 1, 1997 with NASA Hubble Space Telescope's Wide Field and Planetary Camera 2, show banded structure and multiple clouds. Using these images, Dr. Heidi Hammel (Massachusetts Institute of Technology) and colleagues Wes Lockwood (Lowell Observatory) and Kathy Rages (NASA Ames Research Center) plan to measure the wind speeds in the northern hemisphere for the first time.

Uranus is sometimes called the 'sideways' planet, because its rotation axis tipped more than 90 degrees from the planet's orbit around the Sun. The 'year' on Uranus lasts 84 Earth years, which creates extremely long seasons - winter in the northern hemisphere has lasted for nearly 20 years. Uranus has also been called bland and boring, because no clouds have been detectable in ground-based images of the planet. Even to the cameras of the Voyager spacecraft in 1986, Uranus presented a nearly uniform blank disk, and discrete clouds were detectable only in the southern hemisphere. Voyager flew over the planet's cloud tops near the dead of northern winter (when the northern hemisphere was completely shrouded in darkness).

Spring has finally come to the northern hemisphere of Uranus. The newest images, both the visible-wavelength ones described here and those taken a few days earlier with the Near Infrared and Multi-Object Spectrometer (NICMOS) by Erich Karkoschka (University of Arizona), show a planet with banded structure and detectable clouds.

Two images are shown here. The 'aqua' image (on the left) is taken at 5,470 Angstroms, which is near the human eye's peak response to wavelength. Color has been added to the image to show what a person on a spacecraft near Uranus might see. Little structure is evident at this wavelength, though with image-processing techniques, a small cloud can be seen near the planet's northern limb (rightmost edge). The 'red' image (on the right) is taken at 6,190 Angstroms, and is sensitive to absorption by methane molecules in the planet's atmosphere. The banded structure of Uranus is evident, and the small cloud near the northern limb is now visible.

Scientists are expecting that the discrete clouds and banded structure may become even more pronounced as Uranus continues in its slow pace around the Sun. 'Some parts of Uranus haven't seen the Sun in decades,' says Dr. Hammel, 'and historical records suggest that we may see the development of more banded structure and patchy clouds as the planet's year progresses.'

Some scientists have speculated that the winds of Uranus are not symmetric around the planet's equator, but no clouds were visible to test those theories. The new data will provide the opportunity to measure the northern winds. Hammel and colleagues expect to have results soon.

The Wide Field/Planetary Camera 2 was developed by the Jet Propulsion Laboratory and managed by the Goddard Spaced Flight Center for NASA's Office of Space Science.

This image and other images and data received from the Hubble Space Telescope are posted on the World Wide Web on the Space Telescope Science Institute home page at URL http:// oposite.stsci.edu/pubinfo/

Biogenic Aerosols – Effects on Climate and Clouds. Cloud Optical Depth (COD) Sensor Three-Waveband Spectrally-Agile Technique (TWST) Field Campaign Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niple, E. R.; Scott, H. E.

2016-04-01

This report describes the data collected by the Three-Waveband Spectrally-agile Technique (TWST) sensor deployed at Hyytiälä, Finland from 16 July to 31 August 2014 as a guest on the Biogenic Aerosols Effects on Climate and Clouds (BAECC) campaign. These data are currently available from the Atmospheric Radiation Measurement (ARM) Data Archive website and consists of Cloud Optical Depth (COD) measurements for the clouds directly overhead approximately every second (with some dropouts described below) during the daylight periods. A good range of cloud conditions were observed from clear sky to heavy rainfall.

Introducing two Random Forest based methods for cloud detection in remote sensing images

NASA Astrophysics Data System (ADS)

Ghasemian, Nafiseh; Akhoondzadeh, Mehdi

2018-07-01

Cloud detection is a necessary phase in satellite images processing to retrieve the atmospheric and lithospheric parameters. Currently, some cloud detection methods based on Random Forest (RF) model have been proposed but they do not consider both spectral and textural characteristics of the image. Furthermore, they have not been tested in the presence of snow/ice. In this paper, we introduce two RF based algorithms, Feature Level Fusion Random Forest (FLFRF) and Decision Level Fusion Random Forest (DLFRF) to incorporate visible, infrared (IR) and thermal spectral and textural features (FLFRF) including Gray Level Co-occurrence Matrix (GLCM) and Robust Extended Local Binary Pattern (RELBP_CI) or visible, IR and thermal classifiers (DLFRF) for highly accurate cloud detection on remote sensing images. FLFRF first fuses visible, IR and thermal features. Thereafter, it uses the RF model to classify pixels to cloud, snow/ice and background or thick cloud, thin cloud and background. DLFRF considers visible, IR and thermal features (both spectral and textural) separately and inserts each set of features to RF model. Then, it holds vote matrix of each run of the model. Finally, it fuses the classifiers using the majority vote method. To demonstrate the effectiveness of the proposed algorithms, 10 Terra MODIS and 15 Landsat 8 OLI/TIRS images with different spatial resolutions are used in this paper. Quantitative analyses are based on manually selected ground truth data. Results show that after adding RELBP_CI to input feature set cloud detection accuracy improves. Also, the average cloud kappa values of FLFRF and DLFRF on MODIS images (1 and 0.99) are higher than other machine learning methods, Linear Discriminate Analysis (LDA), Classification And Regression Tree (CART), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) (0.96). The average snow/ice kappa values of FLFRF and DLFRF on MODIS images (1 and 0.85) are higher than other traditional methods. The quantitative values on Landsat 8 images show similar trend. Consequently, while SVM and K-nearest neighbor show overestimation in predicting cloud and snow/ice pixels, our Random Forest (RF) based models can achieve higher cloud, snow/ice kappa values on MODIS and thin cloud, thick cloud and snow/ice kappa values on Landsat 8 images. Our algorithms predict both thin and thick cloud on Landsat 8 images while the existing cloud detection algorithm, Fmask cannot discriminate them. Compared to the state-of-the-art methods, our algorithms have acquired higher average cloud and snow/ice kappa values for different spatial resolutions.

Building a LiDAR point cloud simulator: Testing algorithms for high resolution topographic change

NASA Astrophysics Data System (ADS)

Carrea, Dario; Abellán, Antonio; Derron, Marc-Henri; Jaboyedoff, Michel

2014-05-01

Terrestrial laser technique (TLS) is becoming a common tool in Geosciences, with clear applications ranging from the generation of a high resolution 3D models to the monitoring of unstable slopes and the quantification of morphological changes. Nevertheless, like every measurement techniques, TLS still has some limitations that are not clearly understood and affect the accuracy of the dataset (point cloud). A challenge in LiDAR research is to understand the influence of instrumental parameters on measurement errors during LiDAR acquisition. Indeed, different critical parameters interact with the scans quality at different ranges: the existence of shadow areas, the spatial resolution (point density), and the diameter of the laser beam, the incidence angle and the single point accuracy. The objective of this study is to test the main limitations of different algorithms usually applied on point cloud data treatment, from alignment to monitoring. To this end, we built in MATLAB(c) environment a LiDAR point cloud simulator able to recreate the multiple sources of errors related to instrumental settings that we normally observe in real datasets. In a first step we characterized the error from single laser pulse by modelling the influence of range and incidence angle on single point data accuracy. In a second step, we simulated the scanning part of the system in order to analyze the shifting and angular error effects. Other parameters have been added to the point cloud simulator, such as point spacing, acquisition window, etc., in order to create point clouds of simple and/or complex geometries. We tested the influence of point density and vitiating point of view on the Iterative Closest Point (ICP) alignment and also in some deformation tracking algorithm with same point cloud geometry, in order to determine alignment and deformation detection threshold. We also generated a series of high resolution point clouds in order to model small changes on different environments (erosion, landslide monitoring, etc) and we then tested the use of filtering techniques using 3D moving windows along the space and time, which considerably reduces data scattering due to the benefits of data redundancy. In conclusion, the simulator allowed us to improve our different algorithms and to understand how instrumental error affects final results. And also, improve the methodology of scans acquisition to find the best compromise between point density, positioning and acquisition time with the best accuracy possible to characterize the topographic change.

Detecting Distributed SQL Injection Attacks in a Eucalyptus Cloud Environment

NASA Technical Reports Server (NTRS)

Kebert, Alan; Barnejee, Bikramjit; Solano, Juan; Solano, Wanda

2013-01-01

The cloud computing environment offers malicious users the ability to spawn multiple instances of cloud nodes that are similar to virtual machines, except that they can have separate external IP addresses. In this paper we demonstrate how this ability can be exploited by an attacker to distribute his/her attack, in particular SQL injection attacks, in such a way that an intrusion detection system (IDS) could fail to identify this attack. To demonstrate this, we set up a small private cloud, established a vulnerable website in one instance, and placed an IDS within the cloud to monitor the network traffic. We found that an attacker could quite easily defeat the IDS by periodically altering its IP address. To detect such an attacker, we propose to use multi-agent plan recognition, where the multiple source IPs are considered as different agents who are mounting a collaborative attack. We show that such a formulation of this problem yields a more sophisticated approach to detecting SQL injection attacks within a cloud computing environment.

Comparison of the MODIS Collection 5 Multilayer Cloud Detection Product with CALIPSO

NASA Technical Reports Server (NTRS)

Platnick, Steven; Wind, Gala; King, Michael D.; Holz, Robert E.; Ackerman, Steven A.; Nagle, Fred W.

2010-01-01

CALIPSO, launched in June 2006, provides global active remote sensing measurements of clouds and aerosols that can be used for validation of a variety of passive imager retrievals derived from instruments flying on the Aqua spacecraft and other A-Train platforms. The most recent processing effort for the MODIS Atmosphere Team, referred to as the Collection 5 scream, includes a research-level multilayer cloud detection algorithm that uses both thermodynamic phase information derived from a combination of solar and thermal emission bands to discriminate layers of different phases, as well as true layer separation discrimination using a moderately absorbing water vapor band. The multilayer detection algorithm is designed to provide a means of assessing the applicability of 1D cloud models used in the MODIS cloud optical and microphysical product retrieval, which are generated at a 1 km resolution. Using pixel-level collocations of MODIS Aqua, CALIOP, we investigate the global performance of multilayer cloud detection algorithms (and thermodynamic phase).

ASSURED CLOUD COMPUTING UNIVERSITY CENTER OFEXCELLENCE (ACC UCOE)

DTIC Science & Technology

2018-01-18

average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed...infrastructure security -Design of algorithms and techniques for real- time assuredness in cloud computing -Map-reduce task assignment with data locality...46 DESIGN OF ALGORITHMS AND TECHNIQUES FOR REAL- TIME ASSUREDNESS IN CLOUD COMPUTING

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-10-01

The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

Detection of supercooled liquid water-topped mixed-phase clouds >from shortwave-infrared satellite observations

NASA Astrophysics Data System (ADS)

NOH, Y. J.; Miller, S. D.; Heidinger, A. K.

2015-12-01

Many studies have demonstrated the utility of multispectral information from satellite passive radiometers for detecting and retrieving the properties of cloud globally, which conventionally utilizes shortwave- and thermal-infrared bands. However, the satellite-derived cloud information comes mainly from cloud top or represents a vertically integrated property. This can produce a large bias in determining cloud phase characteristics, in particular for mixed-phase clouds which are often observed to have supercooled liquid water at cloud top but a predominantly ice phase residing below. The current satellite retrieval algorithms may report these clouds simply as supercooled liquid without any further information regarding the presence of a sub-cloud-top ice phase. More accurate characterization of these clouds is very important for climate models and aviation applications. In this study, we present a physical basis and preliminary results for the algorithm development of supercooled liquid-topped mixed-phase cloud detection using satellite radiometer observations. The detection algorithm is based on differential absorption properties between liquid and ice particles in the shortwave-infrared bands. Solar reflectance data in narrow bands at 1.6 μm and 2.25 μm are used to optically probe below clouds for distinction between supercooled liquid-topped clouds with and without an underlying mixed phase component. Varying solar/sensor geometry and cloud optical properties are also considered. The spectral band combination utilized for the algorithm is currently available on Suomi NPP Visible/Infrared Imaging Radiometer Suite (VIIRS), Himawari-8 Advanced Himawari Imager (AHI), and the future GOES-R Advance Baseline Imager (ABI). When tested on simulated cloud fields from WRF model and synthetic ABI data, favorable results were shown with reasonable threat scores (0.6-0.8) and false alarm rates (0.1-0.2). An ARM/NSA case study applied to VIIRS data also indicated promising potential of the algorithm.

An experimental comparison of standard stereo matching algorithms applied to cloud top height estimation from satellite IR images

NASA Astrophysics Data System (ADS)

Anzalone, Anna; Isgrò, Francesco

2016-10-01

The JEM-EUSO (Japanese Experiment Module-Extreme Universe Space Observatory) telescope will measure Ultra High Energy Cosmic Ray properties by detecting the UV fluorescent light generated in the interaction between cosmic rays and the atmosphere. Cloud information is crucial for a proper interpretation of these data. The problem of recovering the cloud-top height from satellite images in infrared has struck some attention over the last few decades, as a valuable tool for the atmospheric monitoring. A number of radiative methods do exist, like C02 slicing and Split Window algorithms, using one or more infrared bands. A different way to tackle the problem is, when possible, to exploit the availability of multiple views, and recover the cloud top height through stereo imaging and triangulation. A crucial step in the 3D reconstruction is the process that attempts to match a characteristic point or features selected in one image, with one of those detected in the second image. In this article the performance of a group matching algorithms that include both area-based and global techniques, has been tested. They are applied to stereo pairs of satellite IR images with the final aim of evaluating the cloud top height. Cloudy images from SEVIRI on the geostationary Meteosat Second Generation 9 and 10 (MSG-2, MSG-3) have been selected. After having applied to the cloudy scenes the algorithms for stereo matching, the outcoming maps of disparity are transformed in depth maps according to the geometry of the reference data system. As ground truth we have used the height maps provided by the database of MODIS (Moderate Resolution Imaging Spectroradiometer) on-board Terra/Aqua polar satellites, that contains images quasi-synchronous to the imaging provided by MSG.

Mg II Spectra of Late Type Stars Used to Probe the LISM

NASA Technical Reports Server (NTRS)

Beckman, J. E.; Crivellari, L.; Franco, M.; Molaro, P.; Vladilo, G.

1984-01-01

IUE spectra of Mg II h and k in late type dwarfs and giants were used to detect and measure absorption components due to the LISM. This technique gives a method of probing the awkward range from d = 3 pc to d = 80 pc from the Sun. In spite of interpretational uncertainties the HI component of the LISM can be plotted well enough to confirm it as a cloud some 20 to 30 pc in extent, peaking sharply in density towards l(II)-25 deg., moving towards the Sun from l(II)-25 deg, b(II) = + 10 deg., at 28 Km/sec. The hole towards l(II) = 150 deg is confirmed, suggesting a solar position close to the cloud's edge in this direction.

Monitoring Cirrus Clouds Using Lamp Observations in Association with Balloon-Borne Radiosonde Over Nainital: Few Case Studies

NASA Astrophysics Data System (ADS)

Solanki, R.; Singh, N.

2012-12-01

Upper tropospheric clouds such as cirrus have been identified as one of the important regulator of the radiation balance of the earth atmospheric-system. Though the satellite observation provide valuable information on cirrus clouds, they have limitations on spectral, temporal and spatial coverage, hence the need for local remote sensing, such as LiDAR a leading technique for studying the characteristics and properties of cirrus clouds. The upgraded version of a micro pulse LiDAR popularly known as LiDAR for Atmospheric Measurements and Probing (LAMP) developed by National Atmospheric Research Laboratory (NARL) is operational since October 2011, at ARIES Nainital (29.4oN, 79.5oE, ~2 km above the mean sea level) a high altitude location in the central Himalayas. Regular observations are being carried out in order to study the vertical distribution of aerosols, clouds and boundary layer structure etc. Altitude profiles of range corrected photon count and derived aerosol back scatter coefficients have depicted the occurrence of high altitude cirrus clouds/ ice clouds in an altitude range of 7 to 11 Km. Among the total observations in 27% of the cases the occurrence of cirrus clouds were detected. The corresponding cloud parameters such as temperature (-59 0C), horizontal wind speed (26 m/s), vertical wind speed (24 m/s), Relative Humidity (61%), at a height (~9 Km) were measured with Radiosonde observations. The prevailing region for cirrus cloud is found to be highly turbulent, indicating the region of divergence followed by a convergence, showing the favorable conditions for cirrus cloud formation. Optical and geometrical characteristics of Cirrus clouds have been analyzed using LiDAR and radiosonde measurements. The temperature and thickness dependence of optical properties have also been studied. The results will be further substantiated with CALIPSO satellite and details will be discussed during the presentation.

Improvements in Near-Terminator and Nocturnal Cloud Masks using Satellite Imager Data over the Atmospheric Radiation Measurement Sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trepte, Q.Z.; Minnis, P.; Heck, P.W.

2005-03-18

Cloud detection using satellite measurements presents a big challenge near the terminator where the visible (VIS; 0.65 {micro}m) channel becomes less reliable and the reflected solar component of the solar infrared 3.9-{micro}m channel reaches very low signal-to-noise ratio levels. As a result, clouds are underestimated near the terminator and at night over land and ocean in previous Atmospheric Radiation Measurement (ARM) Program cloud retrievals using Geostationary Operational Environmental Satellite (GOES) imager data. Cloud detection near the terminator has always been a challenge. For example, comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geosciencemore » Laser Altimeter System (GLAS) measurements north of 60{sup o}N indicate significant amounts of missing clouds from AVHRR because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products and GLAS at the same regions also shows the same difficulty in the MODIS cloud retrieval (Pavolonis and Heidinger 2005). Consistent detection of clouds at all times of day is needed to provide reliable cloud and radiation products for ARM and other research efforts involving the modeling of clouds and their interaction with the radiation budget. To minimize inconsistencies between daytime and nighttime retrievals, this paper develops an improved twilight and nighttime cloud mask using GOES-9, 10, and 12 imager data over the ARM sites and the continental United States (CONUS).« less

Improvements in Near-Terminator and Nocturnal Cloud Masks using Satellite Image Data over the Atmospheric Radiation Measurement Sites

NASA Technical Reports Server (NTRS)

Trepte, Q. Z.; Minnis, P.; Heck, R. W.; Palikonda, R.

2005-01-01

Cloud detection using satellite measurements presents a big challenge near the terminator where the visible (VIS; 0.65 (micro)m) channel becomes less reliable and the reflected solar component of the solar infrared 3.9-(micro)m channel reaches very low signal-to-noise ratio levels. As a result, clouds are underestimated near the terminator and at night over land and ocean in previous Atmospheric Radiation Measurement (ARM) Program cloud retrievals using Geostationary Operational Environmental Satellite (GOES) imager data. Cloud detection near the terminator has always been a challenge. For example, comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer (AVHRR)) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 degrees N indicate significant amounts of missing clouds from AVHRR because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products and GLAS at the same regions also shows the same difficulty in the MODIS cloud retrieval (Pavolonis and Heidinger 2005). Consistent detection of clouds at all times of day is needed to provide reliable cloud and radiation products for ARM and other research efforts involving the modeling of clouds and their interaction with the radiation budget. To minimize inconsistencies between daytime and nighttime retrievals, this paper develops an improved twilight and nighttime cloud mask using GOES-9, 10, and 12 imager data over the ARM sites and the continental United States (CONUS).

The application of data mining and cloud computing techniques in data-driven models for structural health monitoring

NASA Astrophysics Data System (ADS)

Khazaeli, S.; Ravandi, A. G.; Banerji, S.; Bagchi, A.

2016-04-01

Recently, data-driven models for Structural Health Monitoring (SHM) have been of great interest among many researchers. In data-driven models, the sensed data are processed to determine the structural performance and evaluate the damages of an instrumented structure without necessitating the mathematical modeling of the structure. A framework of data-driven models for online assessment of the condition of a structure has been developed here. The developed framework is intended for automated evaluation of the monitoring data and structural performance by the Internet technology and resources. The main challenges in developing such framework include: (a) utilizing the sensor measurements to estimate and localize the induced damage in a structure by means of signal processing and data mining techniques, and (b) optimizing the computing and storage resources with the aid of cloud services. The main focus in this paper is to demonstrate the efficiency of the proposed framework for real-time damage detection of a multi-story shear-building structure in two damage scenarios (change in mass and stiffness) in various locations. Several features are extracted from the sensed data by signal processing techniques and statistical methods. Machine learning algorithms are deployed to select damage-sensitive features as well as classifying the data to trace the anomaly in the response of the structure. Here, the cloud computing resources from Amazon Web Services (AWS) have been used to implement the proposed framework.

A one year Landsat 8 conterminous United States study of spatial and temporal patterns of cirrus and non-cirrus clouds and implications for the long term Landsat archive.

NASA Astrophysics Data System (ADS)

Kovalskyy, V.; Roy, D. P.

2014-12-01

The successful February 2013 launch of the Landsat 8 satellite is continuing the 40+ year legacy of the Landsat mission. The payload includes the Operational Land Imager (OLI) that has a new 1370 mm band designed to monitor cirrus clouds and the Thermal Infrared Sensor (TIRS) that together provide 30m low, medium and high confidence cloud detections and 30m low and high confidence cirrus cloud detections. A year of Landsat 8 data over the Conterminous United States (CONUS), composed of 11,296 acquisitions, was analyzed comparing the spatial and temporal incidence of these cloud and cirrus states. This revealed (i) 36.5% of observations were detected with high confidence cloud with spatio-temporal patterns similar to those observed by previous Landsat 7 cloud analyses, (ii) 29.2% were high confidence cirrus, (iii) 20.9% were both high confidence cloud and high confidence cirrus, (iv) 8.3% were detected as high confidence cirrus but not as high confidence cloud. The results illustrate the value of the cirrus band for improved Landsat 8 terrestrial monitoring but imply that the historical CONUS Landsat archive has a similar 8% of undetected cirrus contaminated pixels. The implications for long term Landsat time series records, including the global Web Enabled Landsat Data (WELD) product record, are discussed.

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

NASA Technical Reports Server (NTRS)

Kalia, Subodh; Ganguly, Sangram; Li, Shuang; Nemani, Ramakrishna R.

2017-01-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Quantifying photometric observing conditions on Paranal using an IR camera

NASA Astrophysics Data System (ADS)

Kerber, Florian; Querel, Richard R.; Hanuschik, Reinhard

2014-08-01

A Low Humidity and Temperature Profiling (LHATPRO) microwave radiometer, manufactured by Radiometer Physics GmbH (RPG), is used to monitor sky conditions over ESO's Paranal observatory in support of VLT science operations. In addition to measuring precipitable water vapour (PWV) the instrument also contains an IR camera measuring sky brightness temperature at 10.5 μm. Due to its extended operating range down to -100 °C it is capable of detecting very cold and very thin, even sub-visual, cirrus clouds. We present a set of instrument flux calibration values as compared with a detrended fluctuation analysis (DFA) of the IR camera zenith-looking sky brightness data measured above Paranal taken over the past two years. We show that it is possible to quantify photometric observing conditions and that the method is highly sensitive to the presence of even very thin clouds but robust against variations of sky brightness caused by effects other than clouds such as variations of precipitable water vapour. Hence it can be used to determine photometric conditions for science operations. About 60 % of nights are free of clouds on Paranal. More work will be required to classify the clouds using this technique. For the future this approach might become part of VLT science operations for evaluating nightly sky conditions.

Modeling the Diffuse Cloud-Top Optical Emissions from Ground and Cloud Flashes

NASA Technical Reports Server (NTRS)

Solakiewicz, Richard; Koshak, William

2008-01-01

A number of studies have indicated that the diffuse cloud-top optical emissions from intra-cloud (IC) lightning are brighter than that from normal negative cloud-to-ground (CG) lightning, and hence would be easier to detect from a space-based sensor. The primary reason provided to substantiate this claim has been that the IC is at a higher altitude within the cloud and therefore is less obscured by the cloud multiple scattering medium. CGs at lower altitudes embedded deep within the cloud are more obscured, so CG detection is thought to be more difficult. However, other authors claim that because the CG source current (and hence luminosity) is typically substantially larger than IC currents, the greater CG source luminosity is large enough to overcome the effects of multiple scattering. These investigators suggest that the diffuse cloud top emissions from CGs are brighter than from ICs, and hence are easier to detect from space. Still other investigators claim that the detection efficiency of CGs and ICs is about the same because modern detector sensitivity is good enough to "see" either flash type no matter which produces a brighter cloud top emission. To better assess which of these opinions should be accepted, we introduce an extension of a Boltzmann lightning radiative transfer model previously developed. It considers characteristics of the cloud (geometry, dimensions, scattering properties) and specific lightning channel properties (length, geometry, location, current, optical wave front propagation speed/direction). As such, it represents the most detailed modeling effort to date. At least in the few cases studied thus far, it was found that IC flashes appear brighter at cloud top than the lower altitude negative ground flashes, but additional model runs are to be examined before finalizing our general conclusions.

Cloud Statistics and Discrimination in the Polar Regions

NASA Astrophysics Data System (ADS)

Chan, M.; Comiso, J. C.

2012-12-01

Despite their important role in the climate system, cloud cover and their statistics are poorly known, especially in the polar regions, where clouds are difficult to discriminate from snow covered surfaces. The advent of the A-train, which included Aqua/MODIS, CALIPSO/CALIOP and CloudSat/CPR sensors has provided an opportunity to improve our ability to accurately characterize the cloud cover. MODIS provides global coverage at a relatively good temporal and spatial resolution while CALIOP and CPR provide limited nadir sampling but accurate characterization of the vertical structure and phase of the cloud cover. Over the polar regions, cloud detection from a passive sensors like MODIS is challenging because of the presence of cold and highly reflective surfaces such as snow, sea-ice, glaciers, and ice-sheet, which have surface signatures similar to those of clouds. On the other hand, active sensors such as CALIOP and CPR are not only very sensitive to the presence of clouds but can also provide information about its microphysical characteristics. However, these nadir-looking sensors have sparse spatial coverage and their global data can have data spatial gaps of up to 100 km. We developed a polar cloud detection system for MODIS that is trained using collocated data from CALIOP and CPR. In particular, we employ a machine learning system that reads the radiative profile observed by MODIS and determine whether the field of view is cloudy or clear. Results have shown that the improved cloud detection scheme performs better than typical cloud mask algorithms using a validation data set not used for training. A one-year data set was generated and results indicate that daytime cloud detection accuracies improved from 80.1% to 92.6% (over sea-ice) and 71.2% to 87.4% (over ice-sheet) with CALIOP data used as the baseline. Significant improvements are also observed during nighttime, where cloud detection accuracies increase by 19.8% (over sea-ice) and 11.6% (over ice-sheet). The immediate impact of the new algorithm is that it can minimize large biases of MODIS-derived cloud amount over the Polar Regions and thus a more realistic and high quality global cloud statistics. In particular, our results show that cloud fraction in the Arctic is typically 81.2 % during daytime and 84.0% during nighttime. This is significantly higher than the 71.8% and 58.5%, respectively, derived from standard MODIS cloud product.

Quasi real-time analysis of mixed-phase clouds using interferometric out-of-focus imaging: development of an algorithm to assess liquid and ice water content

NASA Astrophysics Data System (ADS)

Lemaitre, P.; Brunel, M.; Rondeau, A.; Porcheron, E.; Gréhan, G.

2015-12-01

According to changes in aircraft certifications rules, instrumentation has to be developed to alert the flight crews of potential icing conditions. The technique developed needs to measure in real time the amount of ice and liquid water encountered by the plane. Interferometric imaging offers an interesting solution: It is currently used to measure the size of regular droplets, and it can further measure the size of irregular particles from the analysis of their speckle-like out-of-focus images. However, conventional image processing needs to be speeded up to be compatible with the real-time detection of icing conditions. This article presents the development of an optimised algorithm to accelerate image processing. The algorithm proposed is based on the detection of each interferogram with the use of the gradient pair vector method. This method is shown to be 13 times faster than the conventional Hough transform. The algorithm is validated on synthetic images of mixed phase clouds, and finally tested and validated in laboratory conditions. This algorithm should have important applications in the size measurement of droplets and ice particles for aircraft safety, cloud microphysics investigation, and more generally in the real-time analysis of triphasic flows using interferometric particle imaging.

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

PubMed

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

Current Status of Aerosol Retrievals from TOMS

NASA Technical Reports Server (NTRS)

Torres, O.; Herman, J. R.; Bhartia, P. K.; Ginoux, P.

1999-01-01

Properties of atmospheric aerosols over all land and water surfaces are retrieved from TOMS measurements of backscattered radiances. The TOMS technique, uses observations at two wavelengths. In the near ultraviolet (330-380 nm) range, where the effects of gaseous absorption are negligible. The retrieved properties are optical depth and a measure of aerosol absorptivity, generally expressed as single scattering albedo. The main sources of error of the TOMS aerosol products are sub-pixel cloud contamination and uncertainty on the height above the surface of UV-absorbing aerosol layers. The first error source is related to the large footprint (50 x 50 km at nadir) of the sensor, and the lack of detection capability of sub-pixel size clouds. The uncertainty associated with the height of the absorbing aerosol layers, on the other hand, is related to the pressure dependence of the molecular scattering process, which is the basis of the near-UV method of absorbing aerosol detection. The detection of non-absorbing aerosols is not sensitive to aerosol layer height. We will report on the ongoing work to overcome both of these difficulties. Coincident measurements of high spatial resolution thermal infrared radiances are used to address the cloud contamination issue. Mostly clear scenes for aerosol retrieval are selected by examining the spatial homogeneity of the IR radiance measurements within a TOMS pixel. The approach to reduce the uncertainty associated with the height of the aerosol layer by making use of a chemical transport model will also be discussed.

Cumulus cloud base height estimation from high spatial resolution Landsat data - A Hough transform approach

NASA Technical Reports Server (NTRS)

Berendes, Todd; Sengupta, Sailes K.; Welch, Ron M.; Wielicki, Bruce A.; Navar, Murgesh

1992-01-01

A semiautomated methodology is developed for estimating cumulus cloud base heights on the basis of high spatial resolution Landsat MSS data, using various image-processing techniques to match cloud edges with their corresponding shadow edges. The cloud base height is then estimated by computing the separation distance between the corresponding generalized Hough transform reference points. The differences between the cloud base heights computed by these means and a manual verification technique are of the order of 100 m or less; accuracies of 50-70 m may soon be possible via EOS instruments.

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

NASA Technical Reports Server (NTRS)

Logan, T. L.; Huning, J. R.; Glackin, D. L.

1983-01-01

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Lightning studies using LDAR and LLP data

NASA Technical Reports Server (NTRS)

Forbes, Gregory S.

1993-01-01

This study intercompared lightning data from LDAR and LLP systems in order to learn more about the spatial relationships between thunderstorm electrical discharges aloft and lightning strikes to the surface. The ultimate goal of the study is to provide information that can be used to improve the process of real-time detection and warning of lightning by weather forecasters who issue lightning advisories. The Lightning Detection and Ranging (LDAR) System provides data on electrical discharges from thunderstorms that includes cloud-ground flashes as well as lightning aloft (within cloud, cloud-to-cloud, and sometimes emanating from cloud to clear air outside or above cloud). The Lightning Location and Protection (LLP) system detects primarily ground strikes from lightning. Thunderstorms typically produce LDAR signals aloft prior to the first ground strike, so that knowledge of preferred positions of ground strikes relative to the LDAR data pattern from a thunderstorm could allow advance estimates of enhanced ground strike threat. Studies described in the report examine the position of LLP-detected ground strikes relative to the LDAR data pattern from the thunderstorms. The report also describes other potential approaches to the use of LDAR data in the detection and forecasting of lightning ground strikes.

A New Algorithm for Detecting Cloud Height using OMPS/LP Measurements

NASA Technical Reports Server (NTRS)

Chen, Zhong; DeLand, Matthew; Bhartia, Pawan K.

2016-01-01

The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) ozone product requires the determination of cloud height for each event to establish the lower boundary of the profile for the retrieval algorithm. We have created a revised cloud detection algorithm for LP measurements that uses the spectral dependence of the vertical gradient in radiance between two wavelengths in the visible and near-IR spectral regions. This approach provides better discrimination between clouds and aerosols than results obtained using a single wavelength. Observed LP cloud height values show good agreement with coincident Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements.

Looking Down Through the Clouds – Optical Attenuation through Real-Time Clouds

NASA Astrophysics Data System (ADS)

Burley, J.; Lazarewicz, A.; Dean, D.; Heath, N.

Detecting and identifying nuclear explosions in the atmosphere and on the surface of the Earth is critical for the Air Force Technical Applications Center (AFTAC) treaty monitoring mission. Optical signals, from surface or atmospheric nuclear explosions detected by satellite sensors, are attenuated by the atmosphere and clouds. Clouds present a particularly complex challenge as they cover up to seventy percent of the earth's surface. Moreover, their highly variable and diverse nature requires physics-based modeling. Determining the attenuation for each optical ray-path is uniquely dependent on the source geolocation, the specific optical transmission characteristics along that ray path, and sensor detection capabilities. This research details a collaborative AFTAC and AFIT effort to fuse worldwide weather data, from a variety of sources, to provide near-real-time profiles of atmospheric and cloud conditions and the resulting radiative transfer analysis for virtually any wavelength(s) of interest from source to satellite. AFIT has developed a means to model global clouds using the U.S. Air Force’s World Wide Merged Cloud Analysis (WWMCA) cloud data in a new toolset that enables radiance calculations through clouds from UV to RF wavelengths.

Cloud Detection of Optical Satellite Images Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Lee, Kuan-Yi; Lin, Chao-Hung

2016-06-01

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

Polluted and turbid water masses in Osaka Bay and its vicinity revealed with ERTS-A imageries

NASA Technical Reports Server (NTRS)

Watanabe, K.

1973-01-01

ERTS-1 took very valuable MSS imageries of Osaka Bay and its vicinity on October 24, 1972. In the MSS-4 and MSS-5 imageries a complex grey pattern of water masses can be seen. Though some of grey colored patterns seen in black and white prints of the MSS-4 and MSS-5 imageries are easily identified from their shapes as cloud covers or polluted water masses characterized by their color tone in longer wavelengths in the visible region, any correct distribution pattern of polluted or turbid water masses can be hardly detected separately from thin cloud covers in a quick look analysis. In the present investigation, a simple photographic technique was applied using the fact that reflected sun light from cloud including smog and inclined water surfaces of wave have a certain component in the near infrared region, that MSS-7, whereas the light scattered from fine materials suspended in the sea water has nearly no component sensible in MSS-4 and MSS-5 channels.

Estimation of cylinder orientation in three-dimensional point cloud using angular distance-based optimization

NASA Astrophysics Data System (ADS)

Su, Yun-Ting; Hu, Shuowen; Bethel, James S.

2017-05-01

Light detection and ranging (LIDAR) has become a widely used tool in remote sensing for mapping, surveying, modeling, and a host of other applications. The motivation behind this work is the modeling of piping systems in industrial sites, where cylinders are the most common primitive or shape. We focus on cylinder parameter estimation in three-dimensional point clouds, proposing a mathematical formulation based on angular distance to determine the cylinder orientation. We demonstrate the accuracy and robustness of the technique on synthetically generated cylinder point clouds (where the true axis orientation is known) as well as on real LIDAR data of piping systems. The proposed algorithm is compared with a discrete space Hough transform-based approach as well as a continuous space inlier approach, which iteratively discards outlier points to refine the cylinder parameter estimates. Results show that the proposed method is more computationally efficient than the Hough transform approach and is more accurate than both the Hough transform approach and the inlier method.

Detection of long duration cloud contamination in hyper-temporal NDVI imagery

NASA Astrophysics Data System (ADS)

Ali, A.; de Bie, C. A. J. M.; Skidmore, A. K.; Scarrott, R. G.

2012-04-01

NDVI time series imagery are commonly used as a reliable source for land use and land cover mapping and monitoring. However long duration cloud can significantly influence its precision in areas where persistent clouds prevails. Therefore quantifying errors related to cloud contamination are essential for accurate land cover mapping and monitoring. This study aims to detect long duration cloud contamination in hyper-temporal NDVI imagery based land cover mapping and monitoring. MODIS-Terra NDVI imagery (250 m; 16-day; Feb'03-Dec'09) were used after necessary pre-processing using quality flags and upper envelope filter (ASAVOGOL). Subsequently stacked MODIS-Terra NDVI image (161 layers) was classified for 10 to 100 clusters using ISODATA. After classifications, 97 clusters image was selected as best classified with the help of divergence statistics. To detect long duration cloud contamination, mean NDVI class profiles of 97 clusters image was analyzed for temporal artifacts. Results showed that long duration clouds affect the normal temporal progression of NDVI and caused anomalies. Out of total 97 clusters, 32 clusters were found with cloud contamination. Cloud contamination was found more prominent in areas where high rainfall occurs. This study can help to stop error propagation in regional land cover mapping and monitoring, caused by long duration cloud contamination.

Powerful Hurricane Irma Seen in 3D by NASA's CloudSat

NASA Image and Video Library

2017-09-08

NASA's CloudSat satellite flew over Hurricane Irma on Sept. 6, 2017 at 1:45 p.m. EDT (17:45 UTC) as the storm was approaching Puerto Rico in the Atlantic Ocean. Hurricane Irma contained estimated maximum sustained winds of 185 miles per hour (160 knots) with a minimum pressure of 918 millibars. CloudSat transected the eastern edge of Hurricane Irma's eyewall, revealing details of the storm's cloud structure beneath its thick canopy of cirrus clouds. The CloudSat Cloud Profiling Radar excels in detecting the organization and placement of cloud layers beneath a storm's cirrus canopy, which are not readily detected by other satellite sensors. The CloudSat overpass reveals the inner details beneath the cloud tops of this large system; intense areas of convection with moderate to heavy rainfall (deep red and pink colors), cloud-free areas (moats) in between the inner and outer cloud bands of Hurricane Irma and cloud top heights averaging around 9 to 10 miles (15 to 16 kilometers). Lower values of reflectivity (areas of green and blue) denote smaller-sized ice and water particle sizes typically located at the top of a storm system (in the anvil area). The Cloud Profiling Radar loses signal at around 3 miles (5 kilometers) in height (in the melting layer) due to water (ice) particles larger than 0.12 inches (3 millimeters) in diameter. Moderate to heavy rainfall occurs in these areas where signal weakening is detectable. Smaller cumulus and cumulonimbus cloud types are evident as CloudSat moves farther south, beneath the thick cirrus canopy. An animation is available at https://photojournal.jpl.nasa.gov/catalog/PIA21947

A comparison of several techniques to assign heights to cloud tracers

NASA Technical Reports Server (NTRS)

Nieman, Steven J.; Schmetz, Johannes; Menzel, W. P.

1993-01-01

Experimental results are presented which suggest that the water-vapor technique of radiance measurement is a viable alternative to the CO2 technique for inferring the height of semitransparent cloud elements. Future environmental satellites will rely on H2O-derived cloud-height assignments in the wind-field determinations with the next operational geostationary satellite. On a given day, the heights from the H2O and CO2 approaches compare to within 60-110 hPa rms.

Ambient in-situ immersion freezing measurements - findings from the ZAMBIS 2014 field campaign for three ice nucleation techniques

NASA Astrophysics Data System (ADS)

Kohn, Monika; Atkinson, James D.; Lohmann, Ulrike; Kanji, Zamin A.

2015-04-01

To estimate the influence of clouds on the Earth's radiation budget, it is crucial to understand cloud formation processes in the atmosphere. A key process, which significantly affects cloud microphysical properties and the initiation of precipitation thus contributing to the hydrological cycle, is the prevailing type of ice nucleation mechanism. In mixed-phase clouds immersion freezing is the dominant ice crystal forming mechanism, whereby ice nucleating particles (INP) first act as cloud condensation nuclei (CCN) and are activated to cloud droplets followed by freezing upon supercooling. There are a number of experimental methods and techniques to investigate the ice nucleating ability in the immersion mode, however most techniques are offline for field sampling or only suitable for laboratory measurements. In-situ atmospheric studies are needed to understand the ice formation processes of 'real world' particles. Laboratory experiments simulate conditions of atmospheric processes like ageing or coating but are still idealized. Our method is able to measure ambient in-situ immersion freezing on single immersed aerosol particles. The instrumental setup consists of the recently developed portable immersion mode cooling chamber (PIMCA) as a vertical extension to the portable ice nucleation chamber (PINC, [1]), where the frozen fraction of activated aerosol particles are detected by the ice optical depolarization detector (IODE, [2]). Two additional immersion freezing techniques based on a droplet freezing array [3,4] are used to sample ambient aerosol particles either in a suspension (fraction larger ~0.6 μm) or on PM10-filters to compare different ice nucleation techniques. Here, we present ambient in-situ measurements at an urban forest site in Zurich, Switzerland held during the Zurich ambient immersion freezing study (ZAMBIS) in spring 2014. We investigated the ice nucleating ability of natural atmospheric aerosol with the PIMCA/PINC immersion freezing setup as well as a droplet freezing method on aerosol particles either collected in a suspension or on PM10-filters to obtain atmospheric IN concentrations based on the measured ambient aerosol. Investigation of physical properties (number and size distribution) and chemical composition as well as the meteorological conditions provide supplementary information that help to understand the nature of particles and air masses that contribute to immersion freezing. Acknowledgements We thank Hannes Wydler and Hansjörg Frei from ETH Zurich for their technical support. Furthermore, the authors want thank Franz Conen from the University of Basel for sharing equipment and training in the drop freezing experiment. References [1] Chou et al. (2011), Atmos. Chem. Phys., 11, 4725-4738. [2] Nicolet et al. (2010), Atmos. Chem. Phys., 10, 313-325. [3] Conen et al. (2012), Atmos. Meas. Tech., 5, 321-327. [4] Stopelli et al. (2014), Atmos. Meas. Tech., 7, 129-134.

Introducing depolarisation into an inexpensive, simple cloud sensor for standoff aerosol detection

NASA Astrophysics Data System (ADS)

Hopkins, Rebecca J.; Jones, Joseph W.; Barrington, Stephen J.; Foot, Virginia; Baxter, Karen L.

2008-04-01

Light detection and ranging (LIDAR) has potential to be a successful technique for remote detection of airborne biological warfare agents (BWA) that pose a health hazard. Potential techniques for detecting BWA often use spectroscopy to probe molecular structure properties (e.g. UV-fluorescence, Raman and differential absorption spectroscopy). An alternative approach is to differentiate BWA from background interferents by their differing morphology; depolarisation offers one such method. Here, we investigate the feasibility of introducing depolarisation into a short range (approximately 10 m) LIDAR designed to be a simple, inexpensive, low power consumption, portable instrument. T-matrix calculations are presented for a randomly oriented, polydisperse size distribution of Bacillus atrophaeus spheroids. The relationship between backscatter depolarisation and particle aspect ratio is investigated at several incident wavelengths corresponding to those produced by low cost, commercially available laser sources. Through a series of simulations, we determine the best combination of wavelengths for a multi-wavelength instrument design that exploits the concept of normalised depolarisation to determine particle aspect ratio, with the possibility of facilitating BWA detection.

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

PubMed Central

Swetnam, Tyson L.; Gillan, Jeffrey K.; Sankey, Temuulen T.; McClaran, Mitchel P.; Nichols, Mary H.; Heilman, Philip; McVay, Jason

2018-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. PMID:29379511

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States.

PubMed

Swetnam, Tyson L; Gillan, Jeffrey K; Sankey, Temuulen T; McClaran, Mitchel P; Nichols, Mary H; Heilman, Philip; McVay, Jason

2017-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.

A Study of Global Cirrus Cloud Morphology with AIRS Cloud-clear Radiances (CCRs)

NASA Technical Reports Server (NTRS)

Wu, Dong L.; Gong, Jie

2012-01-01

Version 6 (V6) AIRS cloud-clear radiances (CCR) are used to derive cloud-induced radiance (Tcir=Tb-CCR) at the infrared frequencies of weighting functions peaked in the middle troposphere. The significantly improved V 6 CCR product allows a more accurate estimation of the expected clear-sky radiance as if clouds are absent. In the case where strong cloud scattering is present, the CCR becomes unreliable, which is reflected by its estimated uncertainty, and interpolation is employed to replace this CCR value. We find that Tcir derived from this CCR method are much better than other methods and detect more clouds in the upper and lower troposphere as well as in the polar regions where cloud detection is particularly challenging. The cloud morphology derived from the V6 test month, as well as some artifacts, will be shown.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Very high cloud detection in more than two decades of HIRS data

NASA Astrophysics Data System (ADS)

Kolat, Utkan; Menzel, W. Paul; Olson, Erik; Frey, Richard

2013-04-01

This paper reports on the use of High-resolution Infrared Radiation Sounder (HIRS) measurements to infer the presence of upper tropospheric and lower stratospheric (UT/LS) clouds. UT/LS cloud detection is based on the fact that, when viewing an opaque UT/LS cloud that fills the sensor field of view, positive lapse rates above the tropopause cause a more absorbing CO2 or H2O-sensitive spectral band to measure a brightness temperature warmer than that of a less absorbing or nearly transparent infrared window spectral band. The HIRS sensor has flown on 16 polar-orbiting satellites from TIROS-N through NOAA-19 and Metop-A and -B, forming the only 30 year record that includes H2O and CO2-sensitive spectral bands enabling the detection of these UT/LS clouds. Comparison with collocated Cloud-Aerosol Lidar with Orthogonal Polarization data reveals that 97% of the HIRS UT/LS cloud determinations are within 2.5 km of the tropopause (defined as the coldest level in the National Centers for Environmental Prediction Global Data Assimilation System); more clouds are found above the tropopause than below. From NOAA-14 data spanning 1995 through 2005, we find indications of UT/LS clouds in 0.7% of the observations from 60N to 60S using CO2 absorption bands; however, in the region of the Inter-Tropical Convergence Zone (ITCZ), this increases to 1.7%. During El Niño years, UT/LS clouds shift eastward out of their normal location in the western Pacific region. Monthly trends from 1987 through 2011 using data from NOAA-10 onwards show decreases in UT/LS cloud detection in the region of the ITCZ from 1987 until 1996, increases until 2001, and decreases thereafter.

External Influences on Modeled and Observed Cloud Trends

NASA Technical Reports Server (NTRS)

Marvel, Kate; Zelinka, Mark; Klein, Stephen A.; Bonfils, Celine; Caldwell, Peter; Doutriaux, Charles; Santer, Benjamin D.; Taylor, Karl E.

2015-01-01

Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 (Coupled Model Intercomparison Project - Phase 5) model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP (International Satellite Cloud Climatology Project) and PATMOS-x (Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres - Extended). The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Evaluation of Cloud Physical Properties of ECMWF Analysis and Re-Analysis (ERA-40 and ERA Interim) against CERES Tropical Deep Convective Cloud Object Observations

NASA Technical Reports Server (NTRS)

Xu, Kuan-Man

2008-01-01

This study presents an approach that converts the vertical profiles of grid-averaged cloud properties from large-scale models to probability density functions (pdfs) of subgrid-cell cloud physical properties measured at satellite footprints. Cloud physical and radiative properties, rather than just cloud and precipitation occurrences, of assimilated cloud systems by the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis (EOA) and ECMWF Re-Analyses (ERA-40 and ERA Interim) are validated against those obtained from Earth Observing System satellite cloud object data for January-August 1998 and March 2000 periods. These properties include ice water path (IWP), cloud-top height and temperature, cloud optical depth and solar and infrared radiative fluxes. Each cloud object, a contiguous region with similar cloud physical properties, is temporally and spatially matched with EOA and ERA-40 data. Results indicate that most pdfs of EOA and ERA-40 cloud physical and radiative properties agree with those of satellite observations of the tropical deep convective cloud-object type for the January-August 1998 period. There are, however, significant discrepancies in selected ranges of the cloud property pdfs such as the upper range of EOA cloud top height. A major discrepancy is that the dependence of the pdfs on the cloud object size for both EOA and ERA-40 is not as strong as in the observations. Modifications to the cloud parameterization in ECMWF that occurred in October 1999 eliminate the clouds near the tropopause but shift power of the pdf to lower cloud-top heights and greatly reduce the ranges of IWP and cloud optical depth pdfs. These features persist in ERA-40 due to the use of the same cloud parameterizations. The downgrade of data assimilation technique and the lack of snow water content information in ERA-40, not the coarser horizontal grid resolution, are also responsible for the disagreements with observed pdfs of cloud physical properties although the detection rates of cloud object occurrence are improved for small size categories. A possible improvement to the convective parameterization is to introduce a stronger dependence of updraft penetration heights with grid-cell dynamics. These conclusions will be rechecked using the ERA Interim data, due to recent changes in the ECMWF convective parameterization (Bechtold et al. 2004, 2008). Results from the ERA Interim will be presented at the meeting.

Assimilation of Satellite to Improve Cloud Simulation in Wrf Model

NASA Astrophysics Data System (ADS)

Park, Y. H.; Pour Biazar, A.; McNider, R. T.

2012-12-01

A simple approach has been introduced to improve cloud simulation spatially and temporally in a meteorological model. The first step for this approach is to use Geostationary Operational Environmental Satellite (GOES) observations to identify clouds and estimate the clouds structure. Then by comparing GOES observations to model cloud field, we identify areas in which model has under-predicted or over-predicted clouds. Next, by introducing subsidence in areas with over-prediction and lifting in areas with under-prediction, erroneous clouds are removed and new clouds are formed. The technique estimates a vertical velocity needed for the cloud correction and then uses a one dimensional variation schemes (1D_Var) to calculate the horizontal divergence components and the consequent horizontal wind components needed to sustain such vertical velocity. Finally, the new horizontal winds are provided as a nudging field to the model. This nudging provides the dynamical support needed to create/clear clouds in a sustainable manner. The technique was implemented and tested in the Weather Research and Forecast (WRF) Model and resulted in substantial improvement in model simulated clouds. Some of the results are presented here.

The temperature of large dust grains in molecular clouds

NASA Technical Reports Server (NTRS)

Clark, F. O.; Laureijs, R. J.; Prusti, T.

1991-01-01

The temperature of the large dust grains is calculated from three molecular clouds ranging in visual extinction from 2.5 to 8 mag, by comparing maps of either extinction derived from star counts or gas column density derived from molecular observations to I(100). Both techniques show the dust temperature declining into clouds. The two techniques do not agree in absolute scale.

Abundances and Excitation of H2, H3+ & CO in Star-Forming Regions

NASA Astrophysics Data System (ADS)

Kulesa, Craig A.

Although most of the 123 reported interstellar molecules to date have been detected through millimeter-wave emission-line spectroscopy, this technique is inapplicable to non-polar molecules like H2 and H3+, which are central to our understanding of interstellar chemistry. Thus high resolution infrared absorption-line spectroscopy bears an important role in interstellar studies: chemically important non-polar molecules can be observed, and their abundances and excitation conditions can be referred to the same ``pencil beam'' absorbing column. In particular, through a weak quadrupole absorption line spectrum at near-infrared wavelengths, the abundance of cold H2 in dark molecular clouds and star forming regions can now be accurately measured and compared along the same ``pencil beam'' line of sight with the abundance of its most commonly cited surrogate, CO, and its rare isotopomers. Also detected via infrared line absorption is the pivotal molecular ion H3+, whose abundance provides the most direct measurement of the cosmic ray ionization rate in dark molecular clouds, a process that initiates the formation of many other observed molecules there. Our growing sample of H2 and CO detections now includes detailed multi-beam studies of the ρ Ophiuchi molecular cloud and NGC 2024 in Orion. We explore the excitation and degree of ortho- and para-H2 thermalization in dark clouds, variation of the CO abundance over a cloud, and the relation of H2 column density to infrared extinction mapping, far-infrared/submillimeter dust continuum emission, and large scale submillimeter CO, [C I] and HCO+ line emission -- all commonly invoked to indirectly trace H2 during the past 30+ years. For each of the distinct velocity components seen toward some embedded young stellar objects, we are also able to determine the temperature, density, and a CO/H2 abundance ratio, thus unraveling some of the internal structure of a star-forming cloud. H2 and H3+ continue to surprise and delight us with more mysteries. We present imaging and spectroscopy of excited H2 line emission from two Crab Nebula filaments, leading to intriguing questions -- such as the rapid formation, excitation, and continued survival of hydrogen molecules in such a hostile environment. Similarly, we depict the recent detection of CO and H3+ emission from the circumstellar disks of nearby Herbig AeBe stars, providing an outstanding diagnostic of energetic pre-planetary environments and a valuable study of the non-thermal excitation of H3+ in its own right. These studies spotlight the role of molecules as regulators and probes of physical processes in molecular clouds and star- & planet-forming regions. See: http://loke.as.arizona.edu/˜ckulesa/research/ for preprints & more information

Cloud vertical profiles derived from CALIPSO and CloudSat and a comparison with MODIS derived clouds

NASA Astrophysics Data System (ADS)

Kato, S.; Sun-Mack, S.; Miller, W. F.; Rose, F. G.; Minnis, P.; Wielicki, B. A.; Winker, D. M.; Stephens, G. L.; Charlock, T. P.; Collins, W. D.; Loeb, N. G.; Stackhouse, P. W.; Xu, K.

2008-05-01

CALIPSO and CloudSat from the a-train provide detailed information of vertical distribution of clouds and aerosols. The vertical distribution of cloud occurrence is derived from one month of CALIPSO and CloudSat data as a part of the effort of merging CALIPSO, CloudSat and MODIS with CERES data. This newly derived cloud profile is compared with the distribution of cloud top height derived from MODIS on Aqua from cloud algorithms used in the CERES project. The cloud base from MODIS is also estimated using an empirical formula based on the cloud top height and optical thickness, which is used in CERES processes. While MODIS detects mid and low level clouds over the Arctic in April fairly well when they are the topmost cloud layer, it underestimates high- level clouds. In addition, because the CERES-MODIS cloud algorithm is not able to detect multi-layer clouds and the empirical formula significantly underestimates the depth of high clouds, the occurrence of mid and low-level clouds is underestimated. This comparison does not consider sensitivity difference to thin clouds but we will impose an optical thickness threshold to CALIPSO derived clouds for a further comparison. The effect of such differences in the cloud profile to flux computations will also be discussed. In addition, the effect of cloud cover to the top-of-atmosphere flux over the Arctic using CERES SSF and FLASHFLUX products will be discussed.

Processing Uav and LIDAR Point Clouds in Grass GIS

NASA Astrophysics Data System (ADS)

Petras, V.; Petrasova, A.; Jeziorska, J.; Mitasova, H.

2016-06-01

Today's methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.

Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

NASA Astrophysics Data System (ADS)

Champion, Nicolas

2016-06-01

Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.

Domain-averaged, Shallow Precipitation Measurements During the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA)

NASA Astrophysics Data System (ADS)

Lamer, K.; Luke, E. P.; Kollias, P.; Oue, M.; Wang, J.

2017-12-01

The Atmospheric Radiation Measurement (ARM) Climate Research Facility operates a fixed observatory in the Eastern North Atlantic (ENA) on Graciosa Island in the Azores. Straddling the tropics and extratropics, the Azores receive air transported from North America, the Arctic and sometimes Europe. At the ARM ENA site, marine boundary layer clouds are frequently observed all year round. Estimates of drizzle mass flux from the surface to cloud base height are documented using a combination of high sensitivity profiling 35-GHz radar and ceilometer observations. Three years of drizzle mass flux retrievals reveal that statistically, directly over the ENA site, marine boundary layer cloud drizzle rates tend to be weak with few heavy drizzle events. In the summer of 2017, this site hosted the first phase of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign, which is motivated by the need for comprehensive in situ characterization of boundary layer structure, low clouds and aerosols. During this phase, the 35-GHz scanning ARM cloud radar was operated as a surveillance radar, providing regional context for the profiling observations. While less sensitive, the scanning radar measurements document a larger number of heavier drizzle events and provide domain-representative estimates of shallow precipitation. A best estimate, domain averaged, shallow precipitation rate for the region around the ARM ENA site is presented. The methodology optimally combines the ability of the profiling observations to detect the weak but frequently occurring drizzle events with the scanning cloud radar's ability to capture the less frequent heavier drizzle events. The technique is also evaluated using high resolution model output and a sophisticated forward radar operator.

CNES studies for on-board implementation via HLS tools of a cloud-detection module for selective compression

NASA Astrophysics Data System (ADS)

Camarero, R.; Thiebaut, C.; Dejean, Ph.; Speciel, A.

2010-08-01

Future CNES high resolution instruments for remote sensing missions will lead to higher data-rates because of the increase in resolution and dynamic range. For example, the ground resolution improvement has induced a data-rate multiplied by 8 from SPOT4 to SPOT5 [1] and by 28 to PLEIADES-HR [2]. Innovative "smart" compression techniques will be then required, performing different types of compression inside a scene, in order to reach higher global compression ratios while complying with image quality requirements. This socalled "selective compression", allows important compression gains by detecting and then differently compressing the regions-of-interest (ROI) and non-interest in the image (e.g. higher compression ratios are assigned to the non-interesting data). Given that most of CNES high resolution images are cloudy [1], significant mass-memory and transmission gain could be reached by just detecting and suppressing (or compressing significantly) the areas covered by clouds. Since 2007, CNES works on a cloud detection module [3] as a simplification for on-board implementation of an already existing module used on-ground for PLEIADES-HR album images [4]. The different steps of this Support Vector Machine classifier have already been analyzed, for simplification and optimization, during this on-board implementation study: reflectance computation, characteristics vector computation (based on multispectral criteria) and computation of the SVM output. In order to speed up the hardware design phase, a new approach based on HLS [5] tools is being tested for the VHDL description stage. The aim is to obtain a bit-true VDHL design directly from a high level description language as C or Matlab/Simulink [6].

Wheat Ear Detection in Plots by Segmenting Mobile Laser Scanner Data

NASA Astrophysics Data System (ADS)

Velumani, K.; Oude Elberink, S.; Yang, M. Y.; Baret, F.

2017-09-01

The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16  points/cm2. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.

Results from the Two-Year Infrared Cloud Imager Deployment at ARM's NSA Observatory in Barrow, Alaska

NASA Astrophysics Data System (ADS)

Shaw, J. A.; Nugent, P. W.

2016-12-01

Ground-based longwave-infrared (LWIR) cloud imaging can provide continuous cloud measurements in the Arctic. This is of particular importance during the Arctic winter when visible wavelength cloud imaging systems cannot operate. This method uses a thermal infrared camera to observe clouds and produce measurements of cloud amount and cloud optical depth. The Montana State University Optical Remote Sensor Laboratory deployed an infrared cloud imager (ICI) at the Atmospheric Radiation Monitoring North Slope of Alaska site at Barrow, AK from July 2012 through July 2014. This study was used to both understand the long-term operation of an ICI in the Arctic and to study the consistency of the ICI data products in relation to co-located active and passive sensors. The ICI was found to have a high correlation (> 0.92) with collocated cloud instruments and to produce an unbiased data product. However, the ICI also detects thin clouds that are not detected by most operational cloud sensors. Comparisons with high-sensitivity actively sensed cloud products confirm the existence of these thin clouds. Infrared cloud imaging systems can serve a critical role in developing our understanding of cloud cover in the Arctic by provided a continuous annual measurement of clouds at sites of interest.

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

Terrestrial laser scanning used to detect asymmetries in boat hulls

NASA Astrophysics Data System (ADS)

Roca-Pardiñas, Javier; López-Alvarez, Francisco; Ordóñez, Celestino; Menéndez, Agustín; Bernardo-Sánchez, Antonio

2012-01-01

We describe a methodology for identifying asymmetries in boat hull sections reconstructed from point clouds captured using a terrestrial laser scanner (TLS). A surface was first fit to the point cloud using a nonparametric regression method that permitted the construction of a continuous smooth surface. Asymmetries in cross-sections of the surface were identified using a bootstrap resampling technique that took into account uncertainty in the coordinates of the scanned points. Each reconstructed section was analyzed to check, for a given level of significance, that it was within the confidence interval for the theoretical symmetrical section. The method was applied to the study of asymmetries in a medium-sized yacht. Identified were differences of up to 5 cm between the real and theoretical sections in some parts of the hull.

On the Use of Deep Convective Clouds to Calibrate AVHRR Data

NASA Technical Reports Server (NTRS)

Doelling, David R.; Nguyen, Louis; Minnis, Patrick

2004-01-01

Remote sensing of cloud and radiation properties from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites requires constant monitoring of the visible sensors. NOAA satellites do not have onboard visible calibration and need to be calibrated vicariously in order to determine the calibration and the degradation rate. Deep convective clouds are extremely bright and cold, are at the tropopause, have nearly a Lambertian reflectance, and provide predictable albedos. The use of deep convective clouds as calibration targets is developed into a calibration technique and applied to NOAA-16 and NOAA-17. The technique computes the relative gain drift over the life-span of the satellite. This technique is validated by comparing the gain drifts derived from inter-calibration of coincident AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) radiances. A ray-matched technique, which uses collocated, coincident, and co-angled pixel satellite radiance pairs is used to intercalibrate MODIS and AVHRR. The deep convective cloud calibration technique was found to be independent of solar zenith angle, by using well calibrated Visible Infrared Scanner (VIRS) radiances onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which precesses through all solar zenith angles in 23 days.

Green Bank Telescope Detection of HI Clouds in the Fermi Bubble Wind

NASA Astrophysics Data System (ADS)

Lockman, Felix; Di Teodoro, Enrico M.; McClure-Griffiths, Naomi M.

2018-01-01

We used the Robert C. Byrd Green Bank Telescope to map HI 21cm emission in two large regions around the Galactic Center in a search for HI clouds that might be entrained in the nuclear wind that created the Fermi bubbles. In a ~160 square degree region at |b|>4 deg. and |long|<10 deg we detect 106 HI clouds that have large non-circular velocities consistent with their acceleration by the nuclear wind. Rapidly moving clouds are found as far as 1.5 kpc from the center; there are no detectable asymmetries in the cloud populations above and below the Galactic Center. The cloud kinematics is modeled as a population with an outflow velocity of 330 km/s that fills a cone with an opening angle ~140 degrees. The total mass in the clouds is ~10^6 solar masses and we estimate cloud lifetimes to be between 2 and 8 Myr, implying a cold gas mass-loss rate of about 0.1 solar masses per year into the nuclear wind.The Green Bank Telescope is a facility of the National Science Foundation, operated under a cooperative agreement by Associated Universities, Inc.

3D cloud detection and tracking system for solar forecast using multiple sky imagers

DOE PAGES

Peng, Zhenzhou; Yu, Dantong; Huang, Dong; ...

2015-06-23

We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. Thus, this information is then applied to stitch images together intomore » larger views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

Using sky radiances measured by ground based AERONET Sun-Radiometers for cirrus cloud detection

NASA Astrophysics Data System (ADS)

Sinyuk, A.; Holben, B. N.; Eck, T. F.; Slutsker, I.; Lewis, J. R.

2013-12-01

Screening of cirrus clouds using observations of optical depth (OD) only has proven to be a difficult task due mostly to some clouds having temporally and spatially stable OD. On the other hand, the sky radiances measurements which in AERONET protocol are taken throughout the day may contain additional cloud information. In this work the potential of using sky radiances for cirrus cloud detection is investigated. The detection is based on differences in the angular shape of sky radiances due to cirrus clouds and aerosol (see Figure). The range of scattering angles from 3 to 6 degrees was selected due to two primary reasons: high sensitivity to cirrus clouds presence, and close proximity to the Sun. The angular shape of sky radiances was parametrized by its curvature, which is a parameter defined as a combination of the first and second derivatives as a function of scattering angle. We demonstrate that a slope of the logarithm of curvature versus logarithm of scattering angle in this selected range of scattering angles is sensitive to cirrus cloud presence. We also demonstrate that restricting the values of the slope below some threshold value can be used for cirrus cloud screening. The threshold value of the slope was estimated using collocated measurements of AERONET data and MPLNET lidars.

A multi-sensor approach to the retrieval and model validation of global cloudiness

NASA Astrophysics Data System (ADS)

Miller, Steven D.

2000-11-01

The ephemeral clouds have represented a daunting challenge to the atmospheric modeling community from the very beginning. Our inability to resolve them by means of traditional passive sensors to the level of detail required for characterizing their complicated role in the climate feedback system has lead us to explore other resources at our disposal. This research seeks to illustrate and, where applicable, quantify the ways in which active (e.g., radar and lidar) remote sensing devices on existing and proposed platforms can serve to improve our current understanding of cloud and cloud processes in terms of (1)their role in the improvement of cloud property retrievals and (2)their application to the validation/development of clouds in numerical weather prediction models. A new retrieval technique which employs active sensors to constrain cloud boundaries in the vertical is shown to decrease the parameter uncertainties with respect to traditional passive methods in excess of 20% for effective particle radius, and 10-20% for optical depth when considering night-time retrievals of cirrus. These results are brought together with detailed cloud profile sampling from the Lidar In-space Technology Experiment (LITE) to conduct the first global-scale active sensor validation of ECMWF short-range forecasts. The comparisons display remarkable agreement in cloud spatial distribution. A weighted statistical analysis yields hit rates between 75-90%, threat scores 45-75%, probabilities of detection ~80%, and false alarm rates 10-45%. The results suggest that, given the level of realism displayed currently by the ECMWF prognostic cloud scheme forecasts, the reanalysis data may be considered as a new resource for global cloud information. A practical application of these findings has been outlined in the context of defining Cloud-Sat instrument requirements based on virtual orbital observations created from ECMWF global cloud distributions of liquid and ice water contents. This research gives cause for new hope in capturing the complex radiative, convective, and dynamical feedback mechanisms associated with clouds in the climate feedback system. Further, it appeals to the need for an improved collaborative rapport between the now largely disjoint modeling and measurement communities.

Lidar point density analysis: implications for identifying water bodies

USGS Publications Warehouse

Worstell, Bruce B.; Poppenga, Sandra K.; Evans, Gayla A.; Prince, Sandra

2014-01-01

Most airborne topographic light detection and ranging (lidar) systems operate within the near-infrared spectrum. Laser pulses from these systems frequently are absorbed by water and therefore do not generate reflected returns on water bodies in the resulting void regions within the lidar point cloud. Thus, an analysis of lidar voids has implications for identifying water bodies. Data analysis techniques to detect reduced lidar return densities were evaluated for test sites in Blackhawk County, Iowa, and Beltrami County, Minnesota, to delineate contiguous areas that have few or no lidar returns. Results from this study indicated a 5-meter radius moving window with fewer than 23 returns (28 percent of the moving window) was sufficient for delineating void regions. Techniques to provide elevation values for void regions to flatten water features and to force channel flow in the downstream direction also are presented.

Constructing a Merged Cloud-Precipitation Radar Dataset for Tropical Convective Clouds during the DYNAMO/AMIE Experiment at Addu Atoll

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Zhe; McFarlane, Sally A.; Schumacher, Courtney

2014-05-16

To improve understanding of the convective processes key to the Madden-Julian-Oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and Atmospheric Radiation Measurement MJO Investigation Experiment (AMIE) collected four months of observations from three radars, the S-band Polarization Radar (S-Pol), the C-band Shared Mobile Atmospheric Research & Teaching Radar (SMART-R), and Ka-band Zenith Radar (KAZR) on Addu Atoll in the tropical Indian Ocean. This study compares the measurements from the S-Pol and SMART-R to those from the more sensitive KAZR in order to characterize the hydrometeor detection capabilities of the two scanning precipitation radars. Frequency comparisons for precipitating convective cloudsmore » and non-precipitating high clouds agree much better than non-precipitating low clouds for both scanning radars due to issues in ground clutter. On average, SMART-R underestimates convective and high cloud tops by 0.3 to 1.1 km, while S-Pol underestimates cloud tops by less than 0.4 km for these cloud types. S-Pol shows excellent dynamic range in detecting various types of clouds and therefore its data are well suited for characterizing the evolution of the 3D cloud structures, complementing the profiling KAZR measurements. For detecting non-precipitating low clouds and thin cirrus clouds, KAZR remains the most reliable instrument. However, KAZR is attenuated in heavy precipitation and underestimates cloud top height due to rainfall attenuation 4.3% of the time during DYNAMO/AMIE. An empirical method to correct the KAZR cloud top heights is described, and a merged radar dataset is produced to provide improved cloud boundary estimates, microphysics and radiative heating retrievals.« less

A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI

NASA Astrophysics Data System (ADS)

Bley, S.; Deneke, H.

2013-10-01

A threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low-resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures that cannot be detected by the low-resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behavior for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test data set depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as an estimate of cloud fraction. The HRV cloud mask aims for small-scale convective sub-pixel clouds that are missed by the EUMETSAT cloud mask. The major limit of the HRV cloud mask is the minimum cloud optical thickness (COT) that can be detected. This threshold COT was found to be about 0.8 over ocean and 2 over land and is highly related to the albedo of the underlying surface.

Development of a Global Multilayered Cloud Retrieval System

NASA Technical Reports Server (NTRS)

Huang, J.; Minnis, P.; Lin, B.; Yi, Y.; Ayers, J. K.; Khaiyer, M. M.; Arduini, R.; Fan, T.-F

2004-01-01

A more rigorous multilayered cloud retrieval system has been developed to improve the determination of high cloud properties in multilayered clouds. The MCRS attempts a more realistic interpretation of the radiance field than earlier methods because it explicitly resolves the radiative transfer that would produce the observed radiances. A two-layer cloud model was used to simulate multilayered cloud radiative characteristics. Despite the use of a simplified two-layer cloud reflectance parameterization, the MCRS clearly produced a more accurate retrieval of ice water path than simple differencing techniques used in the past. More satellite data and ground observation have to be used to test the MCRS. The MCRS methods are quite appropriate for interpreting the radiances when the high cloud has a relatively large optical depth (tau(sub I) greater than 2). For thinner ice clouds, a more accurate retrieval might be possible using infrared methods. Selection of an ice cloud retrieval and a variety of other issues must be explored before a complete global application of this technique can be implemented. Nevertheless, the initial results look promising.

Infrared experiments for spaceborne planetary atmospheres research. Full report

NASA Technical Reports Server (NTRS)

1981-01-01

The role of infrared sensing in atmospheric science is discussed and existing infrared measurement techniques are reviewed. Proposed techniques for measuring planetary atmospheres are criticized and recommended instrument developments for spaceborne investigations are summarized for the following phenomena: global and local radiative budget; radiative flux profiles; winds; temperature; pressure; transient and marginal atmospheres; planetary rotation and global atmospheric activity; abundances of stable constituents; vertical, lateral, and temporal distribution of abundances; composition of clouds and aerosols; radiative properties of clouds and aerosols; cloud microstructure; cloud macrostructure; and non-LTE phenomena.

Application of the SRI cloud-tracking technique to rapid-scan GOES observations

NASA Technical Reports Server (NTRS)

Wolf, D. E.; Endlich, R. M.

1980-01-01

An automatic cloud tracking system was applied to multilayer clouds associated with severe storms. The method was tested using rapid scan observations of Hurricane Eloise obtained by the GOES satellite on 22 September 1975. Cloud tracking was performed using clustering based either on visible or infrared data. The clusters were tracked using two different techniques. The data of 4 km and 8 km resolution of the automatic system yielded comparable in accuracy and coverage to those obtained by NASA analysts using the Atmospheric and Oceanographic Information Processing System.

Analysis of geostationary satellite-derived cloud parameters associated with environments with high ice water content

NASA Astrophysics Data System (ADS)

de Laat, Adrianus; Defer, Eric; Delanoë, Julien; Dezitter, Fabien; Gounou, Amanda; Grandin, Alice; Guignard, Anthony; Fokke Meirink, Jan; Moisselin, Jean-Marc; Parol, Frédéric

2017-04-01

We present an evaluation of the ability of passive broadband geostationary satellite measurements to detect high ice water content (IWC > 1 g m-3) as part of the European High Altitude Ice Crystals (HAIC) project for detection of upper-atmospheric high IWC, which can be a hazard for aviation. We developed a high IWC mask based on measurements of cloud properties using the Cloud Physical Properties (CPP) algorithm applied to the geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Evaluation of the high IWC mask with satellite measurements of active remote sensors of cloud properties (CLOUDSAT/CALIPSO combined in the DARDAR (raDAR-liDAR) product) reveals that the high IWC mask is capable of detecting high IWC values > 1 g m-3 in the DARDAR profiles with a probability of detection of 60-80 %. The best CPP predictors of high IWC were the condensed water path, cloud optical thickness, cloud phase, and cloud top height. The evaluation of the high IWC mask against DARDAR provided indications that the MSG-CPP high IWC mask is more sensitive to cloud ice or cloud water in the upper part of the cloud, which is relevant for aviation purposes. Biases in the CPP results were also identified, in particular a solar zenith angle (SZA) dependence that reduces the performance of the high IWC mask for SZAs > 60°. Verification statistics show that for the detection of high IWC a trade-off has to be made between better detection of high IWC scenes and more false detections, i.e., scenes identified by the high IWC mask that do not contain IWC > 1 g m-3. However, the large majority of these detections still contain IWC values between 0.1 and 1 g m-3. Comparison of the high IWC mask against results from the Rapidly Developing Thunderstorm (RDT) algorithm applied to the same geostationary SEVIRI data showed that there are similarities and differences with the high IWC mask: the RDT algorithm is very capable of detecting young/new convective cells and areas, whereas the high IWC mask appears to be better capable of detecting more mature and ageing convection as well as cirrus remnants. The lack of detailed understanding of what causes aviation hazards related to high IWC, as well as the lack of clearly defined user requirements, hampers further tuning of the high IWC mask. Future evaluation of the high IWC mask against field campaign data, as well as obtaining user feedback and user requirements from the aviation industry, should provide more information on the performance of the MSG-CPP high IWC mask and contribute to improving the practical use of the high IWC mask.

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

NASA Technical Reports Server (NTRS)

Welch, Ronald M.

1996-01-01

The ASTER polar cloud mask algorithm is currently under development. Several classification techniques have been developed and implemented. The merits and accuracy of each are being examined. The classification techniques under investigation include fuzzy logic, hierarchical neural network, and a pairwise histogram comparison scheme based on sample histograms called the Paired Histogram Method. Scene adaptive methods also are being investigated as a means to improve classifier performance. The feature, arctan of Band 4 and Band 5, and the Band 2 vs. Band 4 feature space are key to separating frozen water (e.g., ice/snow, slush/wet ice, etc.) from cloud over frozen water, and land from cloud over land, respectively. A total of 82 Landsat TM circumpolar scenes are being used as a basis for algorithm development and testing. Numerous spectral features are being tested and include the 7 basic Landsat TM bands, in addition to ratios, differences, arctans, and normalized differences of each combination of bands. A technique for deriving cloud base and top height is developed. It uses 2-D cross correlation between a cloud edge and its corresponding shadow to determine the displacement of the cloud from its shadow. The height is then determined from this displacement, the solar zenith angle, and the sensor viewing angle.

Global Measurements of Optically Thin Ice Clouds Using CALIOP

NASA Technical Reports Server (NTRS)

Ryan, R.; Avery, M.; Tackett, J.

2017-01-01

Optically thin ice clouds have been shown to have a net warming effect on the globe but, because passive instruments are not sensitive to optically thin clouds, the occurrence frequency of this class of clouds is greatly underestimated in historical passive sensor cloud climatology. One major strength of CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), onboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) spacecraft, is its ability to detect these thin clouds, thus filling an important missing piece in the historical data record. This poster examines the full mission of CALIPSO Level 2 data, focusing on those CALIOP retrievals identified as thin ice clouds according to the definition shown to the right. Using this definition, thin ice clouds are identified and counted globally and vertically for each season. By examining the spatial and seasonal distributions of these thin clouds we hope to gain a better understanding these thin ice clouds and how their global distribution has changed over the mission. This poster showcases when and where CALIOP detects thin ice clouds and examines a case study of the eastern pacific and the effects seen from the El Nino-Southern Oscillation (ENSO).

The response of the Seasat and Magsat infrared horizon scanners to cold clouds

NASA Technical Reports Server (NTRS)

Bilanow, S.; Phenneger, M.

1980-01-01

Cold clouds over the Earth are shown to be the principal cause of pitch and roll measurement noise in flight data from the infrared horizon scanners onboard Seasat and Magsat. The observed effects of clouds on the fixed threshold horizon detection logic of the Magsat scanner and on the variable threshold detection logic of the Seasat scanner are discussed. National Oceanic and Atmospheric Administration (NOAA) Earth photographs marked with the scanner ground trace clearly confirm the relationship between measurement errors and Earth clouds. A one to one correspondence can be seen between excursion in the pitch and roll data and cloud crossings. The characteristics of the cloud-induced noise are discussed, and the response of the satellite control systems to the cloud errors is described. Changes to the horizon scanner designs that would reduce the effects of clouds are noted.

An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection

NASA Astrophysics Data System (ADS)

Cook, Kristen L.

2017-02-01

The measurement of topography and of topographic change is essential for the study of many geomorphic processes. In recent years, structure from motion (SfM) techniques applied to photographs taken by camera-equipped unmanned aerial vehicles (UAVs) has become a powerful new tool for the generation of high resolution topography. The variety of available UAV systems continues to increase rapidly, but it is not clear whether increased UAV sophistication translates into improved quality of the calculated topography. To evaluate the lower end of the UAV spectrum, a simple low cost UAV was deployed to calculate high resolution topography in the Daan River gorge in western Taiwan, a site with a complicated 3D morphology and a wide range of surface types, making it a challenging site for topographic measurement. Terrestrial lidar surveys were conducted in parallel with UAV surveys in both June and November 2014, enabling an assessment of the reliability of the UAV survey to detect geomorphic changes in the range of 30 cm to several meters. A further UAV survey was conducted in June 2015 in order to quantify changes resulting from the 2015 spring monsoon. To evaluate the accuracy of the UAV derived topography, it was compared to terrestrial lidar data collected during the same survey period using the cloud-to-cloud comparison algorithm M3C2. The UAV-generated point clouds match the lidar point clouds well, with RMS errors of 30-40 cm; however, the accuracy of the SfM point clouds depends strongly on the characteristics of the surface being considered, with vegetation, water, and small scale texture causing inaccuracies. The lidar and SfM data yield similar maps of change from June to November 2014, with the same areas of geomorphic change detected by both methods. The SfM-generated change map for November 2014 to June 2015 indicates that the 2015 spring monsoon caused erosion throughout the gorge and highlights the importance of event-driven erosion in the Daan River. The results suggest that even very basic UAVs can yield data suitable for measuring geomorphic change on the scale of a channel reach.

The first observed cloud echoes and microphysical parameter retrievals by China's 94-GHz cloud radar

NASA Astrophysics Data System (ADS)

Wu, Juxiu; Wei, Ming; Hang, Xin; Zhou, Jie; Zhang, Peichang; Li, Nan

2014-06-01

By using the cloud echoes first successfully observed by China's indigenous 94-GHz SKY cloud radar, the macrostructure and microphysical properties of drizzling stratocumulus clouds in Anhui Province on 8 June 2013 are analyzed, and the detection capability of this cloud radar is discussed. The results are as follows. (1) The cloud radar is able to observe the time-varying macroscopic and microphysical parameters of clouds, and it can reveal the microscopic structure and small-scale changes of clouds. (2) The velocity spectral width of cloud droplets is small, but the spectral width of the cloud containing both cloud droplets and drizzle is large. When the spectral width is more than 0.4 m s-1, the radar reflectivity factor is larger (over -10 dBZ). (3) The radar's sensitivity is comparatively higher because the minimum radar reflectivity factor is about -35 dBZ in this experiment, which exceeds the threshold for detecting the linear depolarized ratio (LDR) of stratocumulus (commonly -11 to -14 dBZ; decreases with increasing turbulence). (4) After distinguishing of cloud droplets from drizzle, cloud liquid water content and particle effective radius are retrieved. The liquid water content of drizzle is lower than that of cloud droplets at the same radar reflectivity factor.

Detection of nitric oxide in the dark cloud L134N

NASA Technical Reports Server (NTRS)

Mcgonagle, D.; Irvine, W. M.; Minh, Y. C.; Ziurys, L. M.

1990-01-01

The first detection of interstellar nitric oxide (NO) in a cold dark cloud, L134N is reported. Nitric oxide was observed by means of its two 2 Pi 1/2, J = 3/2 - 1/2, rotational transitions at 150.2 and 150.5 GHz, which occur because of Lambda-doubling. The inferred column density for L134N is about 5 x 10 to the 14th/sq cm toward the SO peak in that cloud. This value corresponds to a fractional abundance relative to molecular hydrogen of about 6 x 10 to the -8th and is in good agreement with predictions of quiescent cloud ion-molecule chemistry. NO was not detected toward the dark cloud TMC-1 at an upper limit of 3 x 10 to the -8th or less.

Detection and monitoring of H2O and CO2 ice clouds on Mars

USGS Publications Warehouse

Bell, J.F.; Calvin, W.M.; Ockert-Bell, M. E.; Crisp, D.; Pollack, James B.; Spencer, J.

1996-01-01

We have developed an observational scheme for the detection and discrimination of Mars atmospheric H2O and CO2 clouds using ground-based instruments in the near infrared. We report the results of our cloud detection and characterization study using Mars near IR images obtained during the 1990 and 1993 oppositions. We focused on specific wavelengths that have the potential, based on previous laboratory studies of H2O and CO2 ices, of yielding the greatest degree of cloud detectability and compositional discriminability. We have detected and mapped absorption features at some of these wavelengths in both the northern and southern polar regions of Mars. Compositional information on the nature of these absorption features was derived from comparisons with laboratory ice spectra and with a simplified radiative transfer model of a CO2 ice cloud overlying a bright surface. Our results indicate that both H2O and CO2 ices can be detected and distinguished in the polar hood clouds. The region near 3.00 ??m is most useful for the detection of water ice clouds because there is a strong H2O ice absorption at this wavelength but only a weak CO2 ice band. The region near 3.33 ??m is most useful for the detection of CO2 ice clouds because there is a strong, relatively narrow CO2 ice band at this wavelength but only broad "continuum" H2O ice absorption. Weaker features near 2.30 ??m could arise from CO2 ice at coarse grain sizes, or surface/dust minerals. Narrow features near 2.00 ??m, which could potentially be very diagnostic of CO2 ice clouds, suffer from contamination by Mars atmospheric CO2 absorptions and are difficult to interpret because of the rather poor knowledge of surface elevation at high latitudes. These results indicate that future ground-based, Earth-orbital, and spacecraft studies over a more extended span of the seasonal cycle should yield substantial information on the style and timing of volatile transport on Mars, as well as a more detailed understanding of the role of CO2 condensation in the polar heat budget. Copyright 1996 by the American Geophysical Union.

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2015-04-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud detection schemes were designed to be numerically efficient and suited for the processing of large numbers of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient numbers of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

A Comparative Observational Study of YSO Classification in Four Small Star-forming H ii Regions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Sung-Ju; Choi, Minho; Kang, Miju

We have developed a new young stellar object (YSO) identification and classification technique using mid-infrared Wide-field Infrared Survey Explorer (WISE) data. We compare this new technique with previous WISE YSO detection and classification methods that used either infrared colors or spectral energy distribution slopes. In this study, we also use the new technique to detect and examine the YSO population associated with four small H ii regions: KR 7, KR 81, KR 120, and KR 140. The relatively simple structure of these regions allows us to effectively use both spatial and temporal constraints to identify YSOs that are potential productsmore » of triggered star formation. We are also able to identify regions of active star formation around these H ii regions that are clearly not influenced by the H ii region expansion, and thus demonstrate that star formation is on-going on megayear timescales in some of these molecular clouds.« less

Traffic sign detection in MLS acquired point clouds for geometric and image-based semantic inventory

NASA Astrophysics Data System (ADS)

Soilán, Mario; Riveiro, Belén; Martínez-Sánchez, Joaquín; Arias, Pedro

2016-04-01

Nowadays, mobile laser scanning has become a valid technology for infrastructure inspection. This technology permits collecting accurate 3D point clouds of urban and road environments and the geometric and semantic analysis of data became an active research topic in the last years. This paper focuses on the detection of vertical traffic signs in 3D point clouds acquired by a LYNX Mobile Mapper system, comprised of laser scanning and RGB cameras. Each traffic sign is automatically detected in the LiDAR point cloud, and its main geometric parameters can be automatically extracted, therefore aiding the inventory process. Furthermore, the 3D position of traffic signs are reprojected on the 2D images, which are spatially and temporally synced with the point cloud. Image analysis allows for recognizing the traffic sign semantics using machine learning approaches. The presented method was tested in road and urban scenarios in Galicia (Spain). The recall results for traffic sign detection are close to 98%, and existing false positives can be easily filtered after point cloud projection. Finally, the lack of a large, publicly available Spanish traffic sign database is pointed out.

Comparasion of Cloud Cover restituted by POLDER and MODIS

NASA Astrophysics Data System (ADS)

Zeng, S.; Parol, F.; Riedi, J.; Cornet, C.; Thieuxleux, F.

2009-04-01

PARASOL and AQUA are two sun-synchronous orbit satellites in the queue of A-Train satellites that observe our earth within a few minutes apart from each other. Aboard these two platforms, POLDER and MODIS provide coincident observations of the cloud cover with very different characteristics. These give us a good opportunity to study the clouds system and evaluate strengths and weaknesses of each dataset in order to provide an accurate representation of global cloud cover properties. This description is indeed of outermost importance to quantify and understand the effect of clouds on global radiation budget of the earth-atmosphere system and their influence on the climate changes. We have developed a joint dataset containing both POLDER and MODIS level 2 cloud products collocated and reprojected on a common sinusoidal grid in order to make the data comparison feasible and veracious. Our foremost work focuses on the comparison of both spatial distribution and temporal variation of the global cloud cover. This simple yet critical cloud parameter need to be clearly understood to allow further comparison of the other cloud parameters. From our study, we demonstrate that on average these two sensors both detect the clouds fairly well. They provide similar spatial distributions and temporal variations:both sensors see high values of cloud amount associated with deep convection in ITCZ, over Indonesia, and in west-central Pacific Ocean warm pool region; they also provide similar high cloud cover associated to mid-latitude storm tracks, to Indian monsoon or to the stratocumulus along the west coast of continents; on the other hand small cloud amounts that typically present over subtropical oceans and deserts in subsidence aeras are well identified by both POLDER and MODIS. Each sensor has its advantages and inconveniences for the detection of a particular cloud types. With higher spatial resolution, MODIS can better detect the fractional clouds thus explaining as one part of a positive bias in any latitude and in any viewing angle with an order of 10% between the POLDER cloud amount and the so-called MODIS "combined" cloud amount. Nevertheless it is worthy to note that a negative bias of about 10% is obtained between the POLDER cloud amount and the MODIS "day-mean" cloud amount. Main differences between the two MODIS cloud amount values are known to be due to the filtering of remaining aerosols or cloud edges. due to both this high spatial resolution of MODIS and the fact that "combined" cloud amount filters cloud edges, we can also explain why appear the high positive bias regions over subtropical ocean in south hemisphere and over east Africa in summer. Thanks to several channels in the thermal infrared spectral domain, MODIS detects probably much better the thin cirrus especially over land, thus causing a general negative bias for ice clouds. The multi-spectral capability of MODIS also allows for a better detection of low clouds over snow or ice, Hence the (POLDER-MODIS) cloud amount difference is often negative over Greenland, Antarctica, and over the continents at middle-high latitudes in spring and autumn associated to the snow coverage. The multi-spectral capability of MODIS also makes the discrimination possible between the biomass burning aerosols and the fractional clouds over the continents. Thus a positive bias appears in central Africa in summer and autumn associated to important biomass burning events. Over transition region between desert and non-desert, the presence of large negative bias (POLDER-MODIS) of cloud amount maybe partly due to MODIS pixel falsely labeled the desert as cloudy, where MODIS algorithm uses static desert mask. This is clearly highlighted in south of Sahara in spring and summer where we find a bias negative with an order of -0.1. What is more, thanks to its multi-angular capability, POLDER can discriminate the sun-glint region thus minimizing the dependence of cloud amount on view angle. It makes the detection of high clouds easier over a black surface thanks to its polarization character.

WALES: water vapour lidar experiment in space

NASA Astrophysics Data System (ADS)

Guerin, F.; Pain, Th.; Palmade, J.-L.; Pailharey, E.; Giraud, D.; Jubineau, F.

2017-11-01

The WAter vapour Lidar Experiment in Space (WALES) mission aims at providing water vapour profiles with high accuracy and vertical resolution through the troposphere and the lower stratosphere on a global scale using an instrument based on Differential Absorption Lidar (DIAL) observation technique, and mounted on an Earth orbiting satellite. This active DIAL technique will also provide data on the cloud coverage by means of the signal reflection on the cloud layers. In DIAL operation, backscatter lidar signals at two wavelengths - at least - are detected. One wavelength (λ ON) is highly absorbed by the species of interest, while the other (λ OFF) is backscattered with minimal absorption. This difference in absorption at the two transmitted wavelengths leads to the determination of the concentration of the species of interest. The DIAL is therefore a dual-wavelength lidar in which the signals detected at the two wavelengths are processed to extract the absolute density of water vapour. The Phase A study performed by ALCATEL Space and their partners under contract of the European Space Agency has led to a credible and innovative concept of instrument, based on a mission performance modelling. The challenge is to foster the scientific return while minimising the development risks and costs of instrument development, in particular the laser transmitter. The paper describes the payload design and the implementation on a low Earth orbiting (LEO) satellite.

WALES: WAter vapour Lidar Experiment in Space

NASA Astrophysics Data System (ADS)

Guerin, F.; Pain, Th.; Palmade, J. L.; Pailharey, E.; Giraud, D.; Jubineau, F.

2004-06-01

The WAter vapour Lidar Experiment in Space (WALES) mission aims at providing water vapour profiles with high accuracy and vertical resolution through the troposphere and the lower stratosphere on a global scale using an instrument based on Differential Absorption Lidar (DIAL) observation technique, and mounted on an Earth orbiting satellite. This active DIAL technique will also provide data on the cloud coverage by means of the signal reflection on the cloud layers. In DIAL operation, backscatter lidar signals at two wavelengths - at least - are detected. One wavelength (λ ON) is highly absorbed by the species of interest, while the other (λ OFF) is backscattered with minimal absorption. This difference in absorption at the two transmitted wavelengths leads to the determination of the concentration of the species of interest. The DIAL is therefore a dual-wavelength lidar in which the signals detected at the two wavelengths are processed to extract the absolute density of water vapour. The Phase A study performed by ALCATEL Space and their partners under contract of the European Space Agency has led to a credible and innovative concept of instrument, based on a mission performance modelling. The challenge is to foster the scientific return while minimising the development risks and costs of instrument development, in particular the laser transmitter. The paper describes the payload design and the implementation on a low Earth orbiting (LEO) satellite.

Geometric Data Perturbation-Based Personal Health Record Transactions in Cloud Computing

PubMed Central

Balasubramaniam, S.; Kavitha, V.

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud. PMID:25767826

Geometric data perturbation-based personal health record transactions in cloud computing.

PubMed

Balasubramaniam, S; Kavitha, V

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud.

Cloud Coverage and Height Distribution from the GLAS Polar Orbiting Lidar: Comparison to Passive Cloud Retrievals

NASA Technical Reports Server (NTRS)

Spinhime, J. D.; Palm, S. P.; Hlavka, D. L.; Hart, W. D.; Mahesh, A.

2004-01-01

The Geoscience Laser Altimeter System (GLAS) began full on orbit operations in September 2003. A main application of the two-wavelength GLAS lidar is highly accurate detection and profiling of global cloud cover. Initial analysis indicates that cloud and aerosol layers are consistently detected on a global basis to cross-sections down to 10(exp -6) per meter. Images of the lidar data dramatically and accurately show the vertical structure of cloud and aerosol to the limit of signal attenuation. The GLAS lidar has made the most accurate measurement of global cloud coverage and height to date. In addition to the calibrated lidar signal, GLAS data products include multi level boundaries and optical depth of all transmissive layers. Processing includes a multi-variable separation of cloud and aerosol layers. An initial application of the data results is to compare monthly cloud means from several months of GLAS observations in 2003 to existing cloud climatologies from other satellite measurement. In some cases direct comparison to passive cloud retrievals is possible. A limitation of the lidar measurements is nadir only sampling. However monthly means exhibit reasonably good global statistics and coverage results, at other than polar regions, compare well with other measurements but show significant differences in height distribution. For polar regions where passive cloud retrievals are problematic and where orbit track density is greatest, the GLAS results are particularly an advance in cloud cover information. Direct comparison to MODIS retrievals show a better than 90% agreement in cloud detection for daytime, but less than 60% at night. Height retrievals are in much less agreement. GLAS is a part of the NASA EOS project and data products are thus openly available to the science community (see http://glo.gsfc.nasa.gov).

Digital image processing techniques for detecting surface alteration - An application on the Alaska Peninsula: A section in The United States Geological Survey in Alaska: Accomplishments during 1983

USGS Publications Warehouse

York, James; Wilson, Frederic H.; Gamble, Bruce M.

1985-01-01

The tectonic evolution of the Alaska Peninsula makes it a likely area for the discovery of significant mineral deposits. However, because of problems associated with remoteness and poor weather, little detailed mineral exploration work has been carried on there. This study focuses on using Landsat multispectral scanner data for the Port Moller, Stepovak Bay, and Simeon of Island Quadrangles to detect surface alteration, probably limonitic (iron oxide staining) and(or) argillic (secondary clay minerals) in character, that could be indicative of mineral deposits. The techniques used here are useful for mapping deposits that have exposed surface alteration of at least an hectare, the approximate spatial resolution of the Landsat data. Virtually cloud-free Landsat coverage was used, but to be detected, the alteration area must also be unobscured by vegetation. Not all mineral deposits will be associated with surface alteration, and not all areas of surface alteration will have valuable mineral deposits.

Recent improvements in hydrometeor sampling using airborne holography

NASA Astrophysics Data System (ADS)

Stith, J. L.; Bansemer, A.; Glienke, S.; Shaw, R. A.; Aquino, J.; Fugal, J. P.

2017-12-01

Airborne digital holography provides a new technique to study the sizes, shapes and locations of hydrometeors. Airborne holographic cameras are able to capture more optical information than traditional airborne hydrometeor instruments, which allows for more detailed information, such as the location and shape of individual hydrometeors over a relatively wide range of sizes. These cameras can be housed in an anti-shattering probe arm configuration, which minimizes the effects of probe tip shattering. Holographic imagery, with its three dimensional view of hydrometeor spacing, is also well suited to detecting shattering events when present. A major problem with digital holographic techniques has been the amount of machine time and human analysis involved in analyzing holographic data. Here, we present some recent examples showing how holographic analysis can improve our measurements of liquid and ice particles and we describe a format we have developed for routine archiving of Holographic data, so that processed results can be utilized more routinely by a wider group of investigators. We present a side-by-side comparison of the imagery obtained from holographic reconstruction of ice particles from a holographic camera (HOLODEC) with imagery from a 3VCPI instrument, which utilizes a tube-based sampling geometry. Both instruments were carried on the NSF/NCAR GV aircraft. In a second application of holographic imaging, we compare measurements of cloud droplets from a Cloud Droplet Probe (CDP) with simultaneous measurements from HOLODEC. In some cloud regions the CDP data exhibits a bimodal size distribution, while the more local data from HOLODEC suggests that two mono-modal size distributions are present in the cloud and that the bimodality observed in the CDP is due to the averaging length. Thus, the holographic techniques have the potential to improve our understanding of the warm rain process in future airborne field campaigns. The development of this instrument has been a university and national lab collaboration. Progress in automating the processing techniques has now reached a stage where processed data can be made readily available, so that holographic data from a field campaign can be utilized by a wider group of investigators.

CloudSat system engineering: techniques that point to a future success

NASA Technical Reports Server (NTRS)

Basilio, R. R.; Boain, R. J.; Lam, T.

2002-01-01

Over the past three years the CloutSat Project, a NASA Earth System Science Pathfinder mission to provide from space the first global survey of cloud profiles and cloud physical properties, has implemented a successful project system engineering approach. Techniques learned through heuristic reasoning of past project events and professional experience were applied along with select methods recently touted to increase effectiveness without compromising effiency.

Deep Learning for Discovery of Atmospheric Mountain Waves in MODIS and GPS Data

NASA Astrophysics Data System (ADS)

Pankratius, V.; Li, J. D.; Rude, C. M.; Gowanlock, M.; Herring, T.

2017-12-01

Airflow over mountains can produce gravity waves, called lee waves, which can generate atmospheric turbulence. Since this turbulence poses dangers to aviation, it is critical to identify such regions reliably in an automated fashion. This work leverages two sources of data to go beyond an ad-hoc human visual approach for such identification: MODIS imagery containing cloud patterns formed by lee waves, and patterns in GPS signals resulting from the transmission through atmospheric turbulence due to lee waves. We demonstrate a novel machine learning approach that fuses these two data types to detect atmospheric turbulence associated with lee waves. A convolutional neural network is trained on MODIS tile images to automatically classify the lee wave cloud patterns with 96% correct classifications on a validation set of 20,000 MODIS 64x64 tiles over a test region in the Sierra Nevada Mountains. Signals from GPS stations of the Plate Boundary Observatory are used for feature extraction related to lee waves, in order to improve the confidence of a detection in the MODIS imagery at a given position. To our knowledge, this is the first technique to combine these images and time series data types to improve the spatial and temporal resolutions for large-scale measurements of lee wave formations. First results of this work show great potential for improving weather condition monitoring, hazard and cloud pattern detection, as well as GPS navigation uncertainties. We acknowledge support from NASA AISTNNX15AG84G (PI Pankratius), NASA NNX14AQ03G (PI Herring), and NSF ACI1442997 (PI Pankratius).

Individual Rocks Segmentation in Terrestrial Laser Scanning Point Cloud Using Iterative Dbscan Algorithm

NASA Astrophysics Data System (ADS)

Walicka, A.; Jóźków, G.; Borkowski, A.

2018-05-01

The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.

Cloud Point Extraction for Electroanalysis: Anodic Stripping Voltammetry of Cadmium

PubMed Central

Rusinek, Cory A.; Bange, Adam; Papautsky, Ian; Heineman, William R.

2016-01-01

Cloud point extraction (CPE) is a well-established technique for the pre-concentration of hydrophobic species from water without the use of organic solvents. Subsequent analysis is then typically performed via atomic absorption spectroscopy (AAS), UV-Vis spectroscopy, or high performance liquid chromatography (HPLC). However, the suitability of CPE for electroanalytical methods such as stripping voltammetry has not been reported. We demonstrate the use of CPE for electroanalysis using the determination of cadmium (Cd2+) by anodic stripping voltammetry (ASV) as a representative example. Rather than using the chelating agents which are commonly used in CPE to form a hydrophobic, extractable metal complex, we used iodide and sulfuric acid to neutralize the charge on Cd2+ to form an extractable ion pair. Triton X-114 was chosen as the surfactant for the extraction because its cloud point temperature is near room temperature (22–25° C). Bare glassy carbon (GC), bismuth-coated glassy carbon (Bi-GC), and mercury-coated glassy carbon (Hg-GC) electrodes were compared for the CPE-ASV. A detection limit for Cd2+ of 1.7 nM (0.2 ppb) was obtained with the Hg-GC electrode. Comparison of ASV analysis without CPE was also investigated and a 20x decrease (4.0 ppb) in the detection limit was observed. The suitability of this procedure for the analysis of tap and river water samples was also demonstrated. This simple, versatile, environmentally friendly and cost-effective extraction method is potentially applicable to a wide variety of transition metals and organic compounds that are amenable to detection by electroanalytical methods. PMID:25996561

Submillimeter-Wave Cloud Ice Radiometry

NASA Technical Reports Server (NTRS)

Walter, Steven J.

1999-01-01

Submillimeter-wave cloud ice radiometry is a new and innovative technique for characterizing cirrus ice clouds. Cirrus clouds affect Earth's climate and hydrological cycle by reflecting incoming solar energy, trapping outgoing IR radiation, sublimating into vapor, and influencing atmospheric circulation. Since uncertainties in the global distribution of cloud ice restrict the accuracy of both climate and weather models, successful development of this technique could provide a valuable tool for investigating how clouds affect climate and weather. Cloud ice radiometry could fill an important gap in the observational capabilities of existing and planned Earth-observing systems. Using submillimeter-wave radiometry to retrieve properties of ice clouds can be understood with a simple model. There are a number of submillimeter-wavelength spectral regions where the upper troposphere is transparent. At lower tropospheric altitudes water vapor emits a relatively uniform flux of thermal radiation. When cirrus clouds are present, they scatter a portion of the upwelling flux of submillimeter-wavelength radiation back towards the Earth as shown in the diagram, thus reducing the upward flux o f energy. Hence, the power received by a down-looking radiometer decreases when a cirrus cloud passes through the field of view causing the cirrus cloud to appear radiatively cool against the warm lower atmospheric thermal emissions. The reduction in upwelling thermal flux is a function of both the total cloud ice content and mean crystal size. Radiometric measurements made at multiple widely spaced frequencies permit flux variations caused by changes in crystal size to be distinguished from changes in ice content, and polarized measurements can be used to constrain mean crystal shape. The goal of the cloud ice radiometry program is to further develop and validate this technique of characterizing cirrus. A multi-frequency radiometer is being designed to support airborne science and spacecraft validation missions. This program has already extended the initial millimeter-wave modeling studies to submillimeter-wavelengths and has improved the realism of the cloud scattering models. Additionally a proof-of-concept airborne submillimeter-wave radiometer was constructed and fielded. It measured a radiometric signal from cirrus confirming the basic technical feasibility of this technique. This program is a cooperative effort of the University of Colorado, Colorado State University, Swales Aerospace, and Jet Propulsion Laboratory. Additional information is contained in the original.

Dynamics and Morphology of Saturn’s North Polar Region During Cassini’s Final Year

NASA Astrophysics Data System (ADS)

Blalock, John J.; Sayanagi, Kunio M.; Ingersoll, Andrew P.; Dyudina, Ulyana A.; Ewald, Shawn; McCabe, Ryan M.; Gunnarson, Jacob; Garland, Justin; Gallego, Angelina

2017-10-01

We present an analysis of Saturn’s north polar region utilizing Cassini ISS images captured in visible and near-infrared wavelengths during late 2016 and 2017, including images captured during Cassini’s Grand Finale orbits. To measure the wind field in the region, we utilize the two-dimensional correlation imaging velocimetry (CIV) technique. We also calculate the relative vorticity and divergence from the wind field. To detect changes in the dynamics, we compare measurements of the wind, relative vorticity, and divergence in 2012 and 2013 with those from 2016/2017. We also compare cloud reflectivity between 2012/2013 and 2016/2017 in images that show the north pole under similar illumination conditions. To detect changes in cloud reflectivity, we utilize a Minnaert correction to calculate the zonal mean reflectivity as a function of latitude. Furthermore, we compare the winds and cloud reflectivity at several wavelengths in order to look for changes occurring at different altitudes. Our results indicate that while the dynamics of the north polar region have remained relatively stable, there have been significant morphology changes that have resulted in dramatic color changes. We hypothesize that these changes are a result of the seasonal cycle and linked to the increased production of photochemical hazes in the atmosphere. Our work has been supported by NASA PATM NNX14AK07G, NSF AAG 1212216, and NASA NESSF NNX15AQ70H.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1972-01-01

The author has identified the following significant results. Preliminary results on the feasibility of mapping snow cover extent have been obtained from a limited number of ERTS-1 images of mountains in Alaska, British Columbia, and Washington. The snowline on land can be readily distinguished, except in heavy forest where such distinction appears to be virtually impossible. The snowline on very large glaciers can also be distinguished remarkably easily, leading to a convenient way to measure glacier accumulation area ratios or equilibrium line altitude. Monitoring of large surging glaciers appears to be possible, but only through observation of a change in area and/or medial moraine extent. Under certain conditions, ERTS-1 imagery appears to have high potential for mapping snow cover in mountainous areas. Distinction between snow and clouds appears to require use of the human eye, but in a cloud-free scene the snow cover is sufficiently distinct to allow use of automated techniques. This technique may prove very useful as an aid in the monitoring of the snowpack water resource and the prediction of summer snowmelt runoff volume.

Using Himawari-8, estimation of SO2 cloud altitude at Aso volcano eruption, on October 8, 2016

NASA Astrophysics Data System (ADS)

Ishii, Kensuke; Hayashi, Yuta; Shimbori, Toshiki

2018-02-01

It is vital to detect volcanic plumes as soon as possible for volcanic hazard mitigation such as aviation safety and the life of residents. Himawari-8, the Japan Meteorological Agency's (JMA's) geostationary meteorological satellite, has high spatial resolution and sixteen observation bands including the 8.6 μm band to detect sulfur dioxide (SO2). Therefore, Ash RGB composite images (RED: brightness temperature (BT) difference between 12.4 and 10.4 μm, GREEN: BT difference between 10.4 and 8.6 μm, BLUE: 10.4 μm) discriminate SO2 clouds and volcanic ash clouds from meteorological clouds. Since the Himawari-8 has also high temporal resolution, the real-time monitoring of ash and SO2 clouds is of great use. A phreatomagmatic eruption of Aso volcano in Kyushu, Japan, occurred at 01:46 JST on October 8, 2016. For this eruption, the Ash RGB could detect SO2 cloud from Aso volcano immediately after the eruption and track it even 12 h after. In this case, the Ash RGB images every 2.5 min could clearly detect the SO2 cloud that conventional images such as infrared and split window could not detect sufficiently. Furthermore, we could estimate the height of the SO2 cloud by comparing the Ash RGB images and simulations of the JMA Global Atmospheric Transport Model with a variety of height parameters. As a result of comparison, the top and bottom height of the SO2 cloud emitted from the eruption was estimated as 7 and 13-14 km, respectively. Assuming the plume height was 13-14 km and eruption duration was 160-220 s (as estimated by seismic observation), the total emission mass of volcanic ash from the eruption was estimated as 6.1-11.8 × 108 kg, which is relatively consistent with 6.0-6.5 × 108 kg from field survey. [Figure not available: see fulltext.

Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.

A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expectedmore » clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.« less

THOR: Cloud Thickness from Off beam Lidar Returns

NASA Technical Reports Server (NTRS)

Cahalan, Robert F.; McGill, Matthew; Kolasinski, John; Varnai, Tamas; Yetzer, Ken

2004-01-01

Conventional wisdom is that lidar pulses do not significantly penetrate clouds having optical thickness exceeding about tau = 2, and that no returns are detectable from more than a shallow skin depth. Yet optically thicker clouds of tau much greater than 2 reflect a larger fraction of visible photons, and account for much of Earth s global average albedo. As cloud layer thickness grows, an increasing fraction of reflected photons are scattered multiple times within the cloud, and return from a diffuse concentric halo that grows around the incident pulse, increasing in horizontal area with layer physical thickness. The reflected halo is largely undetected by narrow field-of-view (FoV) receivers commonly used in lidar applications. THOR - Thickness from Off-beam Returns - is an airborne wide-angle detection system with multiple FoVs, capable of observing the diffuse halo, detecting wide-angle signal from which physical thickness of optically thick clouds can be retrieved. In this paper we describe the THOR system, demonstrate that the halo signal is stronger for thicker clouds, and validate physical thickness retrievals for clouds having z > 20, from NASA P-3B flights over the Department of Energy/Atmospheric Radiation Measurement/Southern Great Plains site, using the lidar, radar and other ancillary ground-based data.

Characteristics of cloud occurrence using ceilometer measurements and its relationship to precipitation over Seoul

NASA Astrophysics Data System (ADS)

Lee, Sanghee; Hwang, Seung-On; Kim, Jhoon; Ahn, Myoung-Hwan

2018-03-01

Clouds are an important component of the atmosphere that affects both climate and weather, however, their contributions can be very difficult to determine. Ceilometer measurements can provide high resolution information on atmospheric conditions such as cloud base height (CBH) and vertical frequency of cloud occurrence (CVF). This study presents the first comprehensive analysis of CBH and CVF derived using Vaisala CL51 ceilometers at two urban stations in Seoul, Korea, during a three-year period from January 2014 to December 2016. The average frequency of cloud occurrence detected by the ceilometers is 54.3%. It is found that the CL51 is better able to capture CBH as compared to another ceilometer CL31 at a nearby meteorological station because it could detect high clouds more accurately. Frequency distributions for CBH up to 13,000 m providing detailed vertical features with 500-m interval show 55% of CBHs below 2 km for aggregated CBHs. A bimodal frequency distribution was observed for three-layers CBHs. A monthly variation of CVF reveals that frequency concentration of lower clouds is found in summer and winter, and higher clouds more often detected in spring and autumn. Monthly distribution features of cloud occurrence and precipitation are depending on seasons and it might be easy to define their relationship due to higher degree of variability of precipitation than cloud occurrence. However, a fluctuation of cloud occurrence frequency in summer is similar to precipitation in trend, whereas clouds in winter are relatively frequent but precipitation is not accompanied. In addition, recent decrease of summer precipitation could be mostly explained by a decrease of cloud occurrence. Anomalous precipitation recorded sometimes is considerably related to corresponding cloud occurrence. The diurnal and daily variations of CBH and CVF from ceilometer observations and the analysis of microwave radiometer measurements for two typical cloudiness cases are also reviewed in parallel. This analysis in finer temporal scale exhibits that utilization of ground-based observations together could help to analyze the cloud behaviors.

Development of lidar sensor for cloud-based measurements during convective conditions

NASA Astrophysics Data System (ADS)

Vishnu, R.; Bhavani Kumar, Y.; Rao, T. Narayana; Nair, Anish Kumar M.; Jayaraman, A.

2016-05-01

Atmospheric convection is a natural phenomena associated with heat transport. Convection is strong during daylight periods and rigorous in summer months. Severe ground heating associated with strong winds experienced during these periods. Tropics are considered as the source regions for strong convection. Formation of thunder storm clouds is common during this period. Location of cloud base and its associated dynamics is important to understand the influence of convection on the atmosphere. Lidars are sensitive to Mie scattering and are the suitable instruments for locating clouds in the atmosphere than instruments utilizing the radio frequency spectrum. Thunder storm clouds are composed of hydrometers and strongly scatter the laser light. Recently, a lidar technique was developed at National Atmospheric Research Laboratory (NARL), a Department of Space (DOS) unit, located at Gadanki near Tirupati. The lidar technique employs slant path operation and provides high resolution measurements on cloud base location in real-time. The laser based remote sensing technique allows measurement of atmosphere for every second at 7.5 m range resolution. The high resolution data permits assessment of updrafts at the cloud base. The lidar also provides real-time convective boundary layer height using aerosols as the tracers of atmospheric dynamics. The developed lidar sensor is planned for up-gradation with scanning facility to understand the cloud dynamics in the spatial direction. In this presentation, we present the lidar sensor technology and utilization of its technology for high resolution cloud base measurements during convective conditions over lidar site, Gadanki.

Multi-wavelength dual polarisation lidar for monitoring precipitation process in the cloud seeding technique

NASA Astrophysics Data System (ADS)

Sudhakar, P.; Sheela, K. Anitha; Ramakrishna Rao, D.; Malladi, Satyanarayana

2016-05-01

In recent years weather modification activities are being pursued in many countries through cloud seeding techniques to facilitate the increased and timely precipitation from the clouds. In order to induce and accelerate the precipitation process clouds are artificially seeded with suitable materials like silver iodide, sodium chloride or other hygroscopic materials. The success of cloud seeding can be predicted with confidence if the precipitation process involving aerosol, the ice water balance, water vapor content and size of the seeding material in relation to aerosol in the cloud is monitored in real time and optimized. A project on the enhancement of rain fall through cloud seeding is being implemented jointly with Kerala State Electricity Board Ltd. Trivandrum, Kerala, India at the catchment areas of the reservoir of one of the Hydro electric projects. The dual polarization lidar is being used to monitor and measure the microphysical properties, the extinction coefficient, size distribution and related parameters of the clouds. The lidar makes use of the Mie, Rayleigh and Raman scattering techniques for the various measurement proposed. The measurements with the dual polarization lidar as above are being carried out in real time to obtain the various parameters during cloud seeding operations. In this paper we present the details of the multi-wavelength dual polarization lidar being used and the methodology to monitor the various cloud parameters involved in the precipitation process. The necessary retrieval algorithms for deriving the microphysical properties of clouds, aerosols characteristics and water vapor profiles are incorporated as a software package working under Lab-view for online and off line analysis. Details on the simulation studies and the theoretical model developed in this regard for the optimization of various parameters are discussed.

Advanced Visualization and Interactive Display Rapid Innovation and Discovery Evaluation Research (VISRIDER) Program Task 6: Point Cloud Visualization Techniques for Desktop and Web Platforms

DTIC Science & Technology

2017-04-01

ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH (VISRIDER) PROGRAM TASK 6: POINT CLOUD...To) OCT 2013 – SEP 2014 4. TITLE AND SUBTITLE ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH...various point cloud visualization techniques for viewing large scale LiDAR datasets. Evaluate their potential use for thick client desktop platforms

The Education Value of Cloud Computing

ERIC Educational Resources Information Center

Katzan, Harry, Jr.

2010-01-01

Cloud computing is a technique for supplying computer facilities and providing access to software via the Internet. Cloud computing represents a contextual shift in how computers are provisioned and accessed. One of the defining characteristics of cloud software service is the transfer of control from the client domain to the service provider.…

Precipitation Discrimination from Satellite Infrared Temperatures over the CCOPE Mesonet Region.

NASA Astrophysics Data System (ADS)

Weiss, Mitchell; Smith, Eric A.

1987-06-01

A quantitative investigation of the relationship between satellite-derived cloud-top temperature parameters and the detection of intense convective rainfall is described. The area of study is that of the Cooperative Convective Precipitation Experiment (CCOPE), which was held near Miles City, Montana during the summer of 1981. Cloud-top temperatures, derived from the GOES-West operational satellite, were used to calculate a variety of parameters for objectively quantifying the convective intensity of a storm. A dense network of rainfall provided verification of surface rainfall. The cloud-top temperature field and surface rainfall data were processed into equally sized grid domains in order to best depict the individual samples of instantaneous precipitation.The technique of statistical discriminant analysis was used to determine which combinations of cloud-top temperature parameters best classify rain versus no-rain occurrence using three different rain-rate cutoffs: 1, 4, and 10 mm h1. Time lags within the 30 min rainfall verification were tested to determine the optimum time delay associated with rainfall reaching the ground.A total of six storm cases were used to develop and test the statistical models. Discrimination of rain events was found to be most accurate when using a 10 mm h1 rain-rate cutoff. Use parameters designated as coldest cloud-top temperature, the spatial mean of coldest cloud-top temperature, and change over time of mean coldest cloud-top temperature were found to be the best classifiers of rainfall in this study. Combining both a 10-min time lag (in terms of surface verification) with a 10 mm h1 rain-rate threshold resulted in classifying over 60% of all rain and no-rain cases correctly.

A simple biota removal algorithm for 35 GHz cloud radar measurements

NASA Astrophysics Data System (ADS)

Kalapureddy, Madhu Chandra R.; Sukanya, Patra; Das, Subrata K.; Deshpande, Sachin M.; Pandithurai, Govindan; Pazamany, Andrew L.; Ambuj K., Jha; Chakravarty, Kaustav; Kalekar, Prasad; Krishna Devisetty, Hari; Annam, Sreenivas

2018-03-01

Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and non-hydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume target-based theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ˜ 4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS.

The EOS CERES Global Cloud Mask

NASA Technical Reports Server (NTRS)

Berendes, T. A.; Welch, R. M.; Trepte, Q.; Schaaf, C.; Baum, B. A.

1996-01-01

To detect long-term climate trends, it is essential to produce long-term and consistent data sets from a variety of different satellite platforms. With current global cloud climatology data sets, such as the International Satellite Cloud Climatology Experiment (ISCCP) or CLAVR (Clouds from Advanced Very High Resolution Radiometer), one of the first processing steps is to determine whether an imager pixel is obstructed between the satellite and the surface, i.e., determine a cloud 'mask.' A cloud mask is essential to studies monitoring changes over ocean, land, or snow-covered surfaces. As part of the Earth Observing System (EOS) program, a series of platforms will be flown beginning in 1997 with the Tropical Rainfall Measurement Mission (TRMM) and subsequently the EOS-AM and EOS-PM platforms in following years. The cloud imager on TRMM is the Visible/Infrared Sensor (VIRS), while the Moderate Resolution Imaging Spectroradiometer (MODIS) is the imager on the EOS platforms. To be useful for long term studies, a cloud masking algorithm should produce consistent results between existing (AVHRR) data, and future VIRS and MODIS data. The present work outlines both existing and proposed approaches to detecting cloud using multispectral narrowband radiance data. Clouds generally are characterized by higher albedos and lower temperatures than the underlying surface. However, there are numerous conditions when this characterization is inappropriate, most notably over snow and ice of the cloud types, cirrus, stratocumulus and cumulus are the most difficult to detect. Other problems arise when analyzing data from sun-glint areas over oceans or lakes over deserts or over regions containing numerous fires and smoke. The cloud mask effort builds upon operational experience of several groups that will now be discussed.

Cloud cover detection combining high dynamic range sky images and ceilometer measurements

NASA Astrophysics Data System (ADS)

Román, R.; Cazorla, A.; Toledano, C.; Olmo, F. J.; Cachorro, V. E.; de Frutos, A.; Alados-Arboledas, L.

2017-11-01

This paper presents a new algorithm for cloud detection based on high dynamic range images from a sky camera and ceilometer measurements. The algorithm is also able to detect the obstruction of the sun. This algorithm, called CPC (Camera Plus Ceilometer), is based on the assumption that under cloud-free conditions the sky field must show symmetry. The symmetry criteria are applied depending on ceilometer measurements of the cloud base height. CPC algorithm is applied in two Spanish locations (Granada and Valladolid). The performance of CPC retrieving the sun conditions (obstructed or unobstructed) is analyzed in detail using as reference pyranometer measurements at Granada. CPC retrievals are in agreement with those derived from the reference pyranometer in 85% of the cases (it seems that this agreement does not depend on aerosol size or optical depth). The agreement percentage goes down to only 48% when another algorithm, based on Red-Blue Ratio (RBR), is applied to the sky camera images. The retrieved cloud cover at Granada and Valladolid is compared with that registered by trained meteorological observers. CPC cloud cover is in agreement with the reference showing a slight overestimation and a mean absolute error around 1 okta. A major advantage of the CPC algorithm with respect to the RBR method is that the determined cloud cover is independent of aerosol properties. The RBR algorithm overestimates cloud cover for coarse aerosols and high loads. Cloud cover obtained only from ceilometer shows similar results than CPC algorithm; but the horizontal distribution cannot be obtained. In addition, it has been observed that under quick and strong changes on cloud cover ceilometers retrieve a cloud cover fitting worse with the real cloud cover.

Mapping Directly Imaged Giant Exoplanets

NASA Astrophysics Data System (ADS)

Kostov, Veselin; Apai, Dániel

2013-01-01

With the increasing number of directly imaged giant exoplanets, the current atmosphere models are often not capable of fully explaining the spectra and luminosity of the sources. A particularly challenging component of the atmosphere models is the formation and properties of condensate cloud layers, which fundamentally impact the energetics, opacity, and evolution of the planets. Here we present a suite of techniques that can be used to estimate the level of rotational modulations these planets may show. We propose that the time-resolved observations of such periodic photometric and spectroscopic variations of extrasolar planets due to their rotation can be used as a powerful tool to probe the heterogeneity of their optical surfaces. In this paper, we develop simulations to explore the capabilities of current and next-generation ground- and space-based instruments for this technique. We address and discuss the following questions: (1) what planet properties can be deduced from the light curve and/or spectra, and in particular can we determine rotation periods, spot coverage, spot colors, and spot spectra?; (2) what is the optimal configuration of instrument/wavelength/temporal sampling required for these measurements?; and (3) can principal component analysis be used to invert the light curve and deduce the surface map of the planet? Our simulations describe the expected spectral differences between homogeneous (clear or cloudy) and patchy atmospheres, outline the significance of the dominant absorption features of H2O, CH4, and CO, and provide a method to distinguish these two types of atmospheres. Assuming surfaces with and without clouds for most currently imaged planets the current models predict the largest variations in the J band. Simulated photometry from current and future instruments is used to estimate the level of detectable photometric variations. We conclude that future instruments will be able to recover not only the rotation periods, cloud cover, cloud colors, and spectra but even cloud evolution. We also show that a longitudinal map of the planet's atmosphere can be deduced from its disk-integrated light curves.

Morphological diagnostics of star formation in molecular clouds

NASA Astrophysics Data System (ADS)

Beaumont, Christopher Norris

Molecular clouds are the birth sites of all star formation in the present-day universe. They represent the initial conditions of star formation, and are the primary medium by which stars transfer energy and momentum back to parsec scales. Yet, the physical evolution of molecular clouds remains poorly understood. This is not due to a lack of observational data, nor is it due to an inability to simulate the conditions inside molecular clouds. Instead, the physics and structure of the interstellar medium are sufficiently complex that interpreting molecular cloud data is very difficult. This dissertation mitigates this problem, by developing more sophisticated ways to interpret morphological information in molecular cloud observations and simulations. In particular, I have focused on leveraging machine learning techniques to identify physically meaningful substructures in the interstellar medium, as well as techniques to inter-compare molecular cloud simulations to observations. These contributions make it easier to understand the interplay between molecular clouds and star formation. Specific contributions include: new insight about the sheet-like geometry of molecular clouds based on observations of stellar bubbles; a new algorithm to disambiguate overlapping yet morphologically distinct cloud structures; a new perspective on the relationship between molecular cloud column density distributions and the sizes of cloud substructures; a quantitative analysis of how projection effects affect measurements of cloud properties; and an automatically generated, statistically-calibrated catalog of bubbles identified from their infrared morphologies.

Ship detection from high-resolution imagery based on land masking and cloud filtering

NASA Astrophysics Data System (ADS)

Jin, Tianming; Zhang, Junping

2015-12-01

High resolution satellite images play an important role in target detection application presently. This article focuses on the ship target detection from the high resolution panchromatic images. Taking advantage of geographic information such as the coastline vector data provided by NOAA Medium Resolution Coastline program, the land region is masked which is a main noise source in ship detection process. After that, the algorithm tries to deal with the cloud noise which appears frequently in the ocean satellite images, which is another reason for false alarm. Based on the analysis of cloud noise's feature in frequency domain, we introduce a windowed noise filter to get rid of the cloud noise. With the help of morphological processing algorithms adapted to target detection, we are able to acquire ship targets in fine shapes. In addition, we display the extracted information such as length and width of ship targets in a user-friendly way i.e. a KML file interpreted by Google Earth.

Earth as an Exoplanet: Lessons in Recognizing Planetary Habitability

NASA Astrophysics Data System (ADS)

Meadows, Victoria; Robinson, Tyler; Misra, Amit; Ennico, Kimberly; Sparks, William B.; Claire, Mark; Crisp, David; Schwieterman, Edward; Bussey, D. Ben J.; Breiner, Jonathan

2015-01-01

Earth will always be our best-studied example of a habitable world. While extrasolar planets are unlikely to look exactly like Earth, they may share key characteristics, such as oceans, clouds and surface inhomogeneity. Earth's globally-averaged characteristics can therefore help us to recognize planetary habitability in data-limited exoplanet observations. One of the most straightforward ways to detect habitability will be via detection of 'glint', specular reflectance from an ocean (Robinson et al., 2010). Other methods include undertaking a census of atmospheric greenhouse gases, or attempting to measure planetary surface temperature and pressure, to determine if liquid water would be feasible on the planetary surface. Here we present recent research on detecting planetary habitability, led by the NASA Astrobiology Institute's Virtual Planetary Laboratory Team. This work includes a collaboration with the NASA Lunar Science Institute on the detection of ocean glint and ozone absorption using Lunar Crater Observation and Sensing Satellite (LCROSS) Earth observations (Robinson et al., 2014). This data/model comparison provides the first observational test of a technique that could be used to determine exoplanet habitability from disk-integrated observations at visible and near-infrared wavelengths. We find that the VPL spectral Earth model is in excellent agreement with the LCROSS Earth data, and can be used to reliably predict Earth's appearance at a range of phases relevant to exoplanet observations. Determining atmospheric surface pressure and temperature directly for a potentially habitable planet will be challenging due to the lack of spatial-resolution, presence of clouds, and difficulty in spectrally detecting many bulk constituents of terrestrial atmospheres. Additionally, Rayleigh scattering can be masked by absorbing gases and absorption from the underlying surface. However, new techniques using molecular dimers of oxygen (Misra et al., 2014) and nitrogen (Schwieterman et al., 2014) may provide an alternative means to determine terrestrial atmospheric pressure for both transit transmission and direct imaging observations.

The structure of Venus' middle atmosphere and ionosphere.

PubMed

Pätzold, M; Häusler, B; Bird, M K; Tellmann, S; Mattei, R; Asmar, S W; Dehant, V; Eidel, W; Imamura, T; Simpson, R A; Tyler, G L

2007-11-29

The atmosphere and ionosphere of Venus have been studied in the past by spacecraft with remote sensing or in situ techniques. These early missions, however, have left us with questions about, for example, the atmospheric structure in the transition region from the upper troposphere to the lower mesosphere (50-90 km) and the remarkably variable structure of the ionosphere. Observations become increasingly difficult within and below the global cloud deck (<50 km altitude), where strong absorption greatly limits the available investigative spectrum to a few infrared windows and the radio range. Here we report radio-sounding results from the first Venus Express Radio Science (VeRa) occultation season. We determine the fine structure in temperatures at upper cloud-deck altitudes, detect a distinct day-night temperature difference in the southern middle atmosphere, and track day-to-day changes in Venus' ionosphere.

Active Raman sounding of the earth's water vapor field.

PubMed

Tratt, David M; Whiteman, David N; Demoz, Belay B; Farley, Robert W; Wessel, John E

2005-08-01

The typically weak cross-sections characteristic of Raman processes has historically limited their use in atmospheric remote sensing to nighttime application. However, with advances in instrumentation and techniques, it is now possible to apply Raman lidar to the monitoring of atmospheric water vapor, aerosols and clouds throughout the diurnal cycle. Upper tropospheric and lower stratospheric measurements of water vapor using Raman lidar are also possible but are limited to nighttime and require long integration times. However, boundary layer studies of water vapor variability can now be performed with high temporal and spatial resolution. This paper will review the current state-of-the-art of Raman lidar for high-resolution measurements of the atmospheric water vapor, aerosol and cloud fields. In particular, we describe the use of Raman lidar for mapping the vertical distribution and variability of atmospheric water vapor, aerosols and clouds throughout the evolution of dynamic meteorological events. The ability of Raman lidar to detect and characterize water in the region of the tropopause and the importance of high-altitude water vapor for climate-related studies and meteorological satellite performance are discussed.

Derivation of Tropospheric Column Ozone from the EPTOMS/GOES Co-Located Data Sets using the Cloud Slicing Technique

NASA Technical Reports Server (NTRS)

Ahn, C.; Ziemke, J. R.; Chandra, S.; Bhartia, P. K.

2002-01-01

A recently developed technique called cloud slicing used for deriving upper tropospheric ozone from the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) instrument combined together with temperature-humidity and infrared radiometer (THIR) is no longer applicable to the Earth Probe TOMS (EPTOMS) because EPTOMS does not have an instrument to measure cloud top temperatures. For continuing monitoring of tropospheric ozone between 200-500hPa and testing the feasibility of this technique across spacecrafts, EPTOMS data are co-located in time and space with the Geostationary Operational Environmental Satellite (GOES)-8 infrared data for 2001 and early 2002, covering most of North and South America (45S-45N and 120W-30W). The maximum column amounts for the mid-latitudinal sites of the northern hemisphere are found in the March-May season. For the mid-latitudinal sites of the southern hemisphere, the highest column amounts are found in the September-November season, although overall seasonal variability is smaller than those of the northern hemisphere. The tropical sites show the weakest seasonal variability compared to higher latitudes. The derived results for selected sites are cross validated qualitatively with the seasonality of ozonesonde observations and the results from THIR analyses over the 1979-1984 time period due to the lack of available ozonesonde measurements to study sites for 2001. These comparisons show a reasonably good agreement among THIR, ozonesonde observations, and cloud slicing-derived column ozone. With very limited co-located EPTOMS/GOES data sets, the cloud slicing technique is still viable to derive the upper tropospheric column ozone. Two new variant approaches, High-Low (HL) cloud slicing and ozone profile derivation from cloud slicing are introduced to estimate column ozone amounts using the entire cloud information in the troposphere.

LIDAR and Millimeter-Wave Cloud RADAR (MWCR) techniques for joint observations of cirrus in Shouxian (32.56°N, 116.78°E), China

NASA Astrophysics Data System (ADS)

Bu, Lingbing; Pan, Honglin; Kumar, K. Raghavendra; Huang, Xingyou; Gao, Haiyang; Qin, Yanqiu; Liu, Xinbo; Kim, Dukhyeon

2016-10-01

Cirrus plays an important role in the regulation of the Earth-atmosphere radiation budget. The joint observation using both the LIght Detection And Ranging (LIDAR) and Millimeter-Wave Cloud RADAR (MWCR) was implemented in this study to obtain properties of cirrus at Atmospheric Radiation Measurement (ARM) mobile facility in Shouxian (32.56°N, 116.78°E, 21 m above sea level), China during May-December 2008. We chose the simultaneous measurements of LIDAR and MWCR with effective data days, and the days must with cirrus. Hence, the cirrus properties based on 37 days of data between October 18th and December 13th, 2008 were studied in the present work. By comparing the LIDAR data with the MWCR data, we analyzed the detection capabilities of both instruments quantitatively for measuring the cirrus. The LIDAR cannot penetrate through the thicker cirrus with optical depth (τ) of more than 1.5, while the MWCR cannot sense the clouds with an optical depth of less than 0.3. Statistical analysis showed that the mean cloud base height (CBH) and cloud thickness (CT) of cirrus were 6.5±0.8 km and 2.1±1.1 km, respectively. Furthermore, we investigated three existing inversion methods for deriving the ice water content (IWC) by using the separate LIDAR, MWCR, and the combination of both, respectively. Based on the comparative analysis, a novel joint method was provided to obtain more accurate IWC. In this joint method, cirrus was divided into three different categories according to the optical depth (τ≤0.3, τ≥1.5, and 0.3<τ<1.5). Based on the joint method used in this study, the mean IWC was calculated by means of the statistics, which showed that the mean IWC of cirrus was 0.011±0.008 g m-3.

Characteristics of tropical cyclones and overshooting from GPS radio occultation data

NASA Astrophysics Data System (ADS)

Biondi, Riccardo; Rieckh, Therese; Steiner, Andrea; Kirchengast, Gottfried

2014-05-01

Tropical cyclones (TCs) are extreme weather events causing every year huge damages and several deaths. In some countries they are the natural catastrophes accounting for the major economic damages. The thermal structure of TCs gives important information on the cloud top height allowing for a better understanding of the troposphere-stratosphere transport, which is still poorly understood. The measurement of atmospheric parameters (such as temperature, pressure and humidity) with high vertical resolution and accuracy in the upper troposphere and lower stratosphere (UTLS) is difficult especially during severe weather events (e.g TCs). Satellite remote sensing has improved the TC forecast and monitoring accuracy. In the last decade the Global Positioning Systems (GPS) Radio Occultation (RO) technique contributed to improve our knowledge especially at high troposphere altitudes and in remote regions of the globe thanks to the high vertical resolution, avoiding temperature smoothing issues (given by microwave and infrared instruments) in the UTLS and improving the poor temporal resolution and global coverage given by lidars and radars. We selected more than twenty-thousand GPS RO profiles co-located with TC best tracks for the period 2001 to 2012 and computed temperature anomaly profiles relative to a RO background climatology in order to detect TC cloud tops. We characterized the thermal structure for different ocean basins and for different TC intensities, distinguishing between tropical and extra-tropical cases. The analysis shows that all investigated storms have a common feature: they warm the troposphere and cool the UTLS near the cloud top. This behavior is amplified in the extra-tropical areas. Results reveal that the storms' cloud tops in the southern hemisphere basins reach higher altitudes and lower temperatures than in the northern hemisphere basins. We furthermore compared the cloud top height of each profile with the mean tropopause altitude (from the RO archive) in order to detect overshooting. We present a map of TC overshooting events indicating tropical areas which contribute most to UTLS transport and the large-scale atmospheric circulation.

Road traffic sign detection and classification from mobile LiDAR point clouds

NASA Astrophysics Data System (ADS)

Weng, Shengxia; Li, Jonathan; Chen, Yiping; Wang, Cheng

2016-03-01

Traffic signs are important roadway assets that provide valuable information of the road for drivers to make safer and easier driving behaviors. Due to the development of mobile mapping systems that can efficiently acquire dense point clouds along the road, automated detection and recognition of road assets has been an important research issue. This paper deals with the detection and classification of traffic signs in outdoor environments using mobile light detection and ranging (Li- DAR) and inertial navigation technologies. The proposed method contains two main steps. It starts with an initial detection of traffic signs based on the intensity attributes of point clouds, as the traffic signs are always painted with highly reflective materials. Then, the classification of traffic signs is achieved based on the geometric shape and the pairwise 3D shape context. Some results and performance analyses are provided to show the effectiveness and limits of the proposed method. The experimental results demonstrate the feasibility and effectiveness of the proposed method in detecting and classifying traffic signs from mobile LiDAR point clouds.

Overview of CERES Cloud Properties Derived From VIRS AND MODIS DATA

NASA Technical Reports Server (NTRS)

Minis, Patrick; Geier, Erika; Wielicki, Bruce A.; Sun-Mack, Sunny; Chen, Yan; Trepte, Qing Z.; Dong, Xiquan; Doelling, David R.; Ayers, J. Kirk; Khaiyer, Mandana M.

2006-01-01

Simultaneous measurement of radiation and cloud fields on a global basis is recognized as a key component in understanding and modeling the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. The NASA Clouds and Earth s Radiant Energy System (CERES) Project (Wielicki et al., 1998) began addressing this issue in 1998 with its first broadband shortwave and longwave scanner on the Tropical Rainfall Measuring Mission (TRMM). This was followed by the launch of two CERES scanners each on Terra and Aqua during late 1999 and early 2002, respectively. When combined, these satellites should provide the most comprehensive global characterization of clouds and radiation to date. Unfortunately, the TRMM scanner failed during late 1998. The Terra and Aqua scanners continue to operate, however, providing measurements at a minimum of 4 local times each day. CERES was designed to scan in tandem with high resolution imagers so that the cloud conditions could be evaluated for every CERES measurement. The cloud properties are essential for converting CERES radiances shortwave albedo and longwave fluxes needed to define the radiation budget (ERB). They are also needed to unravel the impact of clouds on the ERB. The 5-channel, 2-km Visible Infrared Scanner (VIRS) on the TRMM and the 36-channel 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua are analyzed to define the cloud properties for each CERES footprint. To minimize inter-satellite differences and aid the development of useful climate-scale measurements, it was necessary to ensure that each satellite imager is calibrated in a fashion consistent with its counterpart on the other CERES satellites (Minnis et al., 2006) and that the algorithms are as similar as possible for all of the imagers. Thus, a set of cloud detection and retrieval algorithms were developed that could be applied to all three imagers utilizing as few channels as possible while producing stable and accurate cloud properties. This paper discusses the algorithms and results of applying those techniques to more than 5 years of Terra MODIS, 3 years of Aqua MODIS, and 4 years of TRMM VIRS data.

Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

NASA Astrophysics Data System (ADS)

Gerhart, Torin; Sunu, Justin; Lieu, Lauren; Merkurjev, Ekaterina; Chang, Jen-Mei; Gilles, Jérôme; Bertozzi, Andrea L.

2013-05-01

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce icker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

Mobile cloud-computing-based healthcare service by noncontact ECG monitoring.

PubMed

Fong, Ee-May; Chung, Wan-Young

2013-12-02

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.

Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

PubMed Central

Fong, Ee-May; Chung, Wan-Young

2013-01-01

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service. PMID:24316562

Cloud detection algorithm comparison and validation for operational Landsat data products

USGS Publications Warehouse

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

[The application of wavelet analysis of remote detection of pollution clouds].

PubMed

Zhang, J; Jiang, F

2001-08-01

The discrete wavelet transform (DWT) is used to analyse the spectra of pollution clouds in complicated environment and extract the small-features. The DWT is a time-frequency analysis technology, which detects the subtle small changes in the target spectrum. The results show that the DWT is a quite effective method to extract features of target-cloud and improve the reliability of monitoring alarm system.

MPLNET V3 Cloud and Planetary Boundary Layer Detection

NASA Technical Reports Server (NTRS)

Lewis, Jasper R.; Welton, Ellsworth J.; Campbell, James R.; Haftings, Phillip C.

2016-01-01

The NASA Micropulse Lidar Network Version 3 algorithms for planetary boundary layer and cloud detection are described and differences relative to the previous Version 2 algorithms are highlighted. A year of data from the Goddard Space Flight Center site in Greenbelt, MD consisting of diurnal and seasonal trends is used to demonstrate the results. Both the planetary boundary layer and cloud algorithms show significant improvement of the previous version.

Object Detection using the Kinect

DTIC Science & Technology

2012-03-01

Kinect camera and point cloud data from the Kinect’s structured light stereo system (figure 1). We obtain reasonable results using a single prototype...same manner we present in this report. For example, at Willow Garage , Steder uses a 3-D feature he developed to classify objects directly from point...detecting backpacks using the data available from the Kinect sensor. 4 3.1 Point Cloud Filtering Dense point clouds derived from stereo are notoriously

Developing Dual Polarization Applications For 45th Weather Squadron's (45 WS) New Weather Radar: A Cooperative Project With The National Space Science and Technology Center (NSSTC)

NASA Technical Reports Server (NTRS)

Roeder, W.P.; Peterson, W.A.; Carey, L.D.; Deierling, W.; McNamara, T.M.

2009-01-01

A new weather radar is being acquired for use in support of America s space program at Cape Canaveral Air Force Station, NASA Kennedy Space Center, and Patrick AFB on the east coast of central Florida. This new radar includes dual polarization capability, which has not been available to 45 WS previously. The 45 WS has teamed with NSSTC with funding from NASA Marshall Spaceflight Flight Center to improve their use of this new dual polarization capability when it is implemented operationally. The project goals include developing a temperature profile adaptive scan strategy, developing training materials, and developing forecast techniques and tools using dual polarization products. The temperature profile adaptive scan strategy will provide the scan angles that provide the optimal compromise between volume scan rate, vertical resolution, phenomena detection, data quality, and reduced cone-of-silence for the 45 WS mission. The mission requirements include outstanding detection of low level boundaries for thunderstorm prediction, excellent vertical resolution in the atmosphere electrification layer between 0 C and -20 C for lightning forecasting and Lightning Launch Commit Criteria evaluation, good detection of anvil clouds for Lightning Launch Commit Criteria evaluation, reduced cone-of-silence, fast volume scans, and many samples per pulse for good data quality. The training materials will emphasize the appropriate applications most important to the 45 WS mission. These include forecasting the onset and cessation of lightning, forecasting convective winds, and hopefully the inference of electrical fields in clouds. The training materials will focus on annotated radar imagery based on products available to the 45 WS. Other examples will include time sequenced radar products without annotation to simulate radar operations. This will reinforce the forecast concepts and also allow testing of the forecasters. The new dual polarization techniques and tools will focus on the appropriate applications for the 45 WS mission. These include forecasting the onset of lightning, the cessation of lightning, convective winds, and hopefully the inference of electrical fields in clouds. This presentation will report on the results achieved so far in the project.

Intelligent Observation Strategies for Geosynchronous Remote Sensing for Natural Hazards

NASA Astrophysics Data System (ADS)

Moe, K.; Cappelaere, P. G.; Frye, S. W.; LeMoigne, J.; Mandl, D.; Flatley, T.; Geist, A.

2015-12-01

Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked "thing" with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a user's smart phone as any other smart appliance.

Intelligent Observation Strategies for Geosynchronous Remote Sensing for Natural Hazards

NASA Technical Reports Server (NTRS)

Moe, Karen; Cappleare, Patrice; Frye, Stuart; LeMoigne, Jacqueline; Mandl, Daniel; Flatley, Thomas; Geist, Alessandro

2015-01-01

Geosynchronous satellites offer a unique perspective for monitoring environmental factors important to understanding natural hazards and supporting the disasters management life cycle, namely forecast, detection, response, recovery and mitigation. In the NASA decadal survey for Earth science, the GEO-CAPE mission was proposed to address coastal and air pollution events in geosynchronous orbit, complementing similar initiatives in Asia by the South Koreans and by ESA in Europe, thereby covering the northern hemisphere. In addition to analyzing the challenges of identifying instrument capabilities to meet the science requirements, and the implications of hosting the instrument payloads on commercial geosynchronous satellites, the GEO-CAPE mission design team conducted a short study to explore strategies to optimize the science return for the coastal imaging instrument. The study focused on intelligent scheduling strategies that took into account cloud avoidance techniques as well as onboard processing methods to reduce the data storage and transmission loads. This paper expands the findings of that study to address the use of intelligent scheduling techniques and near-real time data product acquisition of both the coastal water and air pollution events. The topics include the use of onboard processing to refine and execute schedules, to detect cloud contamination in observations, and to reduce data handling operations. Analysis of state of the art flight computing capabilities will be presented, along with an assessment of cloud detection algorithms and their performance characteristics. Tools developed to illustrate operational concepts will be described, including their applicability to environmental monitoring domains with an eye to the future. In the geostationary configuration, the payload becomes a networked thing with enough connectivity to exchange data seamlessly with users. This allows the full field of view to be sensed at very high rate under the control of ground infrastructure, resulting in improved efficiencies, accuracy and science benefits. Hence a remote sensing payload and its data may become one of millions of connected objects in the emerging Internet of Things (IoT), and be as easily accessible by a users smart phone as any other smart appliance.

C+ detection of warm dark gas in diffuse clouds

NASA Astrophysics Data System (ADS)

Langer, W. D.; Velusamy, T.; Pineda, J. L.; Goldsmith, P. F.; Li, D.; Yorke, H. W.

2010-10-01

We present the first results of the Herschel open time key program, Galactic Observations of Terahertz C+ (GOT C+) survey of the [CII] 2P3/2-2P1/2 fine-structure line at 1.9 THz (158 μm) using the HIFI instrument on Herschel. We detected 146 interstellar clouds along sixteen lines-of-sight towards the inner Galaxy. We also acquired HI and CO isotopologue data along each line-of-sight for analysis of the physical conditions in these clouds. Here we analyze 29 diffuse clouds (AV < 1.3 mag) in this sample characterized by having [CII] and HI emission, but no detectable CO. We find that [CII] emission is generally stronger than expected for diffuse atomic clouds, and in a number of sources is much stronger than anticipated based on their HI column density. We show that excess [CII] emission in these clouds is best explained by the presence of a significant diffuse warm H2, dark gas, component. This first [CII] 158 μm detection of warm dark gas demonstrates the value of this tracer for mapping this gas throughout the Milky Way and in galaxies. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA.

Thermal bioaerosol cloud tracking with Bayesian classification

NASA Astrophysics Data System (ADS)

Smith, Christian W.; Dupuis, Julia R.; Schundler, Elizabeth C.; Marinelli, William J.

2017-05-01

The development of a wide area, bioaerosol early warning capability employing existing uncooled thermal imaging systems used for persistent perimeter surveillance is discussed. The capability exploits thermal imagers with other available data streams including meteorological data and employs a recursive Bayesian classifier to detect, track, and classify observed thermal objects with attributes consistent with a bioaerosol plume. Target detection is achieved based on similarity to a phenomenological model which predicts the scene-dependent thermal signature of bioaerosol plumes. Change detection in thermal sensor data is combined with local meteorological data to locate targets with the appropriate thermal characteristics. Target motion is tracked utilizing a Kalman filter and nearly constant velocity motion model for cloud state estimation. Track management is performed using a logic-based upkeep system, and data association is accomplished using a combinatorial optimization technique. Bioaerosol threat classification is determined using a recursive Bayesian classifier to quantify the threat probability of each tracked object. The classifier can accept additional inputs from visible imagers, acoustic sensors, and point biological sensors to improve classification confidence. This capability was successfully demonstrated for bioaerosol simulant releases during field testing at Dugway Proving Grounds. Standoff detection at a range of 700m was achieved for as little as 500g of anthrax simulant. Developmental test results will be reviewed for a range of simulant releases, and future development and transition plans for the bioaerosol early warning platform will be discussed.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

OT1_mputman_1: ASCII: All Sky observations of Galactic CII

NASA Astrophysics Data System (ADS)

Putman, M.

2010-07-01

The Milky Way and other galaxies require a significant source of ongoing star formation fuel to explain their star formation histories. A new ubiquitous population of discrete, cold clouds have recently been discovered at the disk-halo interface of our Galaxy that could potentially provide this source of fuel. We propose to observe a small sample of these disk-halo clouds with HIFI to determine if the level of [CII] emission detected suggests they represent the cooling of warm clouds at the interface between the star forming disk and halo. These cooling clouds are predicted by simulations of warm clouds moving into the disk-halo interface region. We target 5 clouds in this proposal for which we have high resolution HI maps and can observe the densest core of the cloud. The results of our observations will also be used to interpret the surprisingly high detections of [CII] for low HI column density clouds in the Galactic Plane by the GOT C+ Key Program by extending the clouds probed to high latitude environments.

Improving Cloud Detection in Satellite Images of Coral Reef Environments Using Space Shuttle Photographs and High-Definition Television

NASA Technical Reports Server (NTRS)

Andrefeouet, Serge; Robinson, Julie

2000-01-01

Coral reefs worldwide are suffering from severe and rapid degradation (Bryant et A, 1998; Hoegh-Guldberg, 1999). Quick, consistent, large-scale assessment is required to assess and monitor their status (e.g., USDOC/NOAA NESDIS et al., 1999). On-going systematic collection of high resolution digital satellite data will exhaustively complement the relatively small number of SPOT, Landsat 4-5, and IRS scenes acquired for coral reefs the last 20 years. The workhorse for current image acquisition is the Landsat 7 ETM+ Long Term Acquisition Plan (Gasch et al. 2000). Coral reefs are encountered in tropical areas and cloud contamination in satellite images is frequently a problem (Benner and Curry 1998), despite new automated techniques of cloud cover avoidance (Gasch and Campana 2000). Fusion of multidate acquisitions is a classical solution to solve the cloud problems. Though elegant, this solution is costly since multiple images must be purchased for one location; the cost may be prohibitive for institutions in developing countries. There are other difficulties associated with fusing multidate images as well. For example, water quality or surface state can significantly change through time in coral reef areas making the bathymetric processing of a mosaiced image strenuous. Therefore, another strategy must be selected to detect clouds and improve coral reefs mapping. Other supplemental data could be helpful and cost-effective for distinguishing clouds and generating the best possible reef maps in the shortest amount of time. Photographs taken from the 1960s to the present from the Space Shuttle and other human-occupied spacecraft are one under-used source of alternative multitemporal data (Lulla et al. 1996). Nearly 400,000 photographs have been acquired during this period, an estimated 28,000 of these taken to date are of potential value for reef remote sensing (Robinson et al. 2000a). The photographic images can be digitized into three bands (red, green and blue) and processed for various applications (e.g., Benner and Curry 1998, Nedeltchev 1999, Glasser and Lulla 2000, Robinson et al. 2000c, Webb et al, in press).

Cloud cover over the equatorial eastern Pacific derived from July 1983 International Satellite Cloud Climatology Project data using a hybrid bispectral threshold method

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Harrison, Edwin F.; Gibson, Gary G.

1987-01-01

A set of visible and IR data obtained with GOES from July 17-31, 1983 is analyzed using a modified version of the hybrid bispectral threshold method developed by Minnis and Harrison (1984). This methodology can be divided into a set of procedures or optional techniques to determine the proper contaminate clear-sky temperature or IR threshold. The various optional techniques are described; the options are: standard, low-temperature limit, high-reflectance limit, low-reflectance limit, coldest pixel and thermal adjustment limit, IR-only low-cloud temperature limit, IR clear-sky limit, and IR overcast limit. Variations in the cloud parameters and the characteristics and diurnal cycles of trade cumulus and stratocumulus clouds over the eastern equatorial Pacific are examined. It is noted that the new method produces substantial changes in about one third of the cloud amount retrieval; and low cloud retrievals are affected most by the new constraints.

Comparison Between CCCM and CloudSat Radar-Lidar (RL) Cloud and Radiation Products

NASA Technical Reports Server (NTRS)

Ham, Seung-Hee; Kato, Seiji; Rose, Fred G.; Sun-Mack, Sunny

2015-01-01

To enhance cloud properties, LaRC and CIRA developed each combination algorithm for obtained properties from passive, active and imager in A-satellite constellation. When comparing global cloud fraction each other, LaRC-produced CERES-CALIPSO-CloudSat-MODIS (CCCM) products larger low-level cloud fraction over tropic ocean, while CIRA-produced Radar-Lidar (RL) shows larger mid-level cloud fraction for high latitude region. The reason for different low-level cloud fraction is due to different filtering method of lidar-detected cloud layers. Meanwhile difference in mid-level clouds is occurred due to different priority of cloud boundaries from lidar and radar.

A new characterization of supercooled clouds below 10,000 feet AGL

NASA Technical Reports Server (NTRS)

Masters, C. O.

1985-01-01

Icing caused by supercooled clouds below 10,000 feet were characterized with a view toward a change in FAA standards for civil aircraft ice protection standards. Current techniques in cloud physics were employed.

Automated detection of Martian water ice clouds: the Valles Marineris

NASA Astrophysics Data System (ADS)

Ogohara, Kazunori; Munetomo, Takafumi; Hatanaka, Yuji; Okumura, Susumu

2016-10-01

We need to extract water ice clouds from the large number of Mars images in order to reveal spatial and temporal variations of water ice cloud occurrence and to meteorologically understand climatology of water ice clouds. However, visible images observed by Mars orbiters for several years are too many to visually inspect each of them even though the inspection was limited to one region. Therefore, an automated detection algorithm of Martian water ice clouds is necessary for collecting ice cloud images efficiently. In addition, it may visualize new aspects of spatial and temporal variations of water ice clouds that we have never been aware. We present a method for automatically evaluating the presence of Martian water ice clouds using difference images and cross-correlation distributions calculated from blue band images of the Valles Marineris obtained by the Mars Orbiter Camera onboard the Mars Global Surveyor (MGS/MOC). We derived one subtracted image and one cross-correlation distribution from two reflectance images. The difference between the maximum and the average, variance, kurtosis, and skewness of the subtracted image were calculated. Those of the cross-correlation distribution were also calculated. These eight statistics were used as feature vectors for training Support Vector Machine, and its generalization ability was tested using 10-fold cross-validation. F-measure and accuracy tended to be approximately 0.8 if the maximum in the normalized reflectance and the difference of the maximum and the average in the cross-correlation were chosen as features. In the process of the development of the detection algorithm, we found many cases where the Valles Marineris became clearly brighter than adjacent areas in the blue band. It is at present unclear whether the bright Valles Marineris means the occurrence of water ice clouds inside the Valles Marineris or not. Therefore, subtracted images showing the bright Valles Marineris were excluded from the detection of water ice clouds

A Voxel-Based Approach for Imaging Voids in Three-Dimensional Point Clouds

NASA Astrophysics Data System (ADS)

Salvaggio, Katie N.

Geographically accurate scene models have enormous potential beyond that of just simple visualizations in regard to automated scene generation. In recent years, thanks to ever increasing computational efficiencies, there has been significant growth in both the computer vision and photogrammetry communities pertaining to automatic scene reconstruction from multiple-view imagery. The result of these algorithms is a three-dimensional (3D) point cloud which can be used to derive a final model using surface reconstruction techniques. However, the fidelity of these point clouds has not been well studied, and voids often exist within the point cloud. Voids exist in texturally difficult areas, as well as areas where multiple views were not obtained during collection, constant occlusion existed due to collection angles or overlapping scene geometry, or in regions that failed to triangulate accurately. It may be possible to fill in small voids in the scene using surface reconstruction or hole-filling techniques, but this is not the case with larger more complex voids, and attempting to reconstruct them using only the knowledge of the incomplete point cloud is neither accurate nor aesthetically pleasing. A method is presented for identifying voids in point clouds by using a voxel-based approach to partition the 3D space. By using collection geometry and information derived from the point cloud, it is possible to detect unsampled voxels such that voids can be identified. This analysis takes into account the location of the camera and the 3D points themselves to capitalize on the idea of free space, such that voxels that lie on the ray between the camera and point are devoid of obstruction, as a clear line of sight is a necessary requirement for reconstruction. Using this approach, voxels are classified into three categories: occupied (contains points from the point cloud), free (rays from the camera to the point passed through the voxel), and unsampled (does not contain points and no rays passed through the area). Voids in the voxel space are manifested as unsampled voxels. A similar line-of-sight analysis can then be used to pinpoint locations at aircraft altitude at which the voids in the point clouds could theoretically be imaged. This work is based on the assumption that inclusion of more images of the void areas in the 3D reconstruction process will reduce the number of voids in the point cloud that were a result of lack of coverage. Voids resulting from texturally difficult areas will not benefit from more imagery in the reconstruction process, and thus are identified and removed prior to the determination of future potential imaging locations.

An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

NASA Astrophysics Data System (ADS)

LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

2016-12-01

Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

Remote Sensing of Clouds for Solar Forecasting Applications

NASA Astrophysics Data System (ADS)

Mejia, Felipe

A method for retrieving cloud optical depth (tauc) using a UCSD developed ground- based Sky Imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various tauc produced by a Radiative Transfer Model (RTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (SZA), tau c , solar pixel an- gle/scattering angle (SPA), and pixel zenith angle/view angle (PZA). The effects of these parameters are described and the functions for radiance, Ilambda (tau c ,SZA,SPA,PZA) , and the red-blue ratio, RBR(tauc ,SZA,SPA,PZA) , are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for tau c , where RBR increases with tauc up to about tauc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Imeaslambda (SPA,PZA) , in addition to RBRmeas (SPA,PZA ) to obtain a unique solution for tauc . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min [ MH96a ] method for overcast skies. tau c values ranged from 0-80 with values over 80 being capped and registered as 80. A tauc RMSE of 2.5 between the Min method [ MH96b ] and the USI are observed. The MWR and USI have an RMSE of 2.2 which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms. Using the RRBR tauc estimate as an input we then explore the potential of using tomographic techniques for 3-D cloud reconstruction. The Algebraic Reconstruction Technique (ART) is applied to optical depth maps from sky images to reconstruct 3-D cloud extinction coefficients. Reconstruction accuracy is explored for different products, including surface irradiance, extinction coefficients and Liquid Water Path, as a function of the number of available sky imagers (SIs) and setup distance. Increasing the number of cameras improves the accuracy of the 3-D reconstruction: For surface irradiance, the error decreases significantly up to four imagers at which point the improvements become marginal while k error continues to decrease with more cameras. The ideal distance between imagers was also explored: For a cloud height of 1 km, increasing distance up to 3 km (the domain length) improved the 3-D reconstruction for surface irradiance, while k error continued to decrease with increasing decrease. An iterative reconstruction technique was also used to improve the results of the ART by minimizing the error between input images and reconstructed simulations. For the best case of a nine imager deployment, the ART and iterative method resulted in 53.4% and 33.6% mean average error (MAE) for the extinction coefficients, respectively. The tomographic methods were then tested on real world test cases in the Uni- versity of California San Diego's (UCSD) solar testbed. Five UCSD sky imagers (USI) were installed across the testbed based on the best performing distances in simulations. Topographic obstruction is explored as a source of error by analyzing the increased error with obstruction in the field of view of the horizon. As more of the horizon is obstructed the error increases. If at least a field of view of 70° is available for the camera the accuracy is within 2% of the full field of view. Errors caused by stray light are also explored by removing the circumsolar region from images and comparing the cloud reconstruction to a full image. Removing less than 30% of the circumsolar region image and GHI errors were within 0.2% of the full image while errors in k increased 1%. Removing more than 30° around the sun resulted in inaccurate cloud reconstruction. Using four of the five USI a 3D cloud is reconstructed and compared to the fifth camera. The image of the fifth camera (excluded from the reconstruction) was then simulated and found to have a 22.9% error compared to the ground truth.

Early in-flight detection of SO2 via Differential Optical Absorption Spectroscopy: a feasible aviation safety measure to prevent potential encounters with volcanic plumes

NASA Astrophysics Data System (ADS)

Vogel, L.; Galle, B.; Kern, C.; Delgado Granados, H.; Conde, V.; Norman, P.; Arellano, S.; Landgren, O.; Lübcke, P.; Alvarez Nieves, J. M.; Cárdenas Gonzáles, L.; Platt, U.

2011-09-01

Volcanic ash constitutes a risk to aviation, mainly due to its ability to cause jet engines to fail. Other risks include the possibility of abrasion of windshields and potentially serious damage to avionic systems. These hazards have been widely recognized since the early 1980s, when volcanic ash provoked several incidents of engine failure in commercial aircraft. In addition to volcanic ash, volcanic gases also pose a threat. Prolonged and/or cumulative exposure to sulphur dioxide (SO2) or sulphuric acid (H2SO4) aerosols potentially affects e.g. windows, air frame and may cause permanent damage to engines. SO2 receives most attention among the gas species commonly found in volcanic plumes because its presence above the lower troposphere is a clear proxy for a volcanic cloud and indicates that fine ash could also be present. Up to now, remote sensing of SO2 via Differential Optical Absorption Spectroscopy (DOAS) in the ultraviolet spectral region has been used to measure volcanic clouds from ground based, airborne and satellite platforms. Attention has been given to volcanic emission strength, chemistry inside volcanic clouds and measurement procedures were adapted accordingly. Here we present a set of experimental and model results, highlighting the feasibility of DOAS to be used as an airborne early detection system of SO2 in two spatial dimensions. In order to prove our new concept, simultaneous airborne and ground-based measurements of the plume of Popocatépetl volcano, Mexico, were conducted in April 2010. The plume extended at an altitude around 5250 m above sea level and was approached and traversed at the same altitude with several forward looking DOAS systems aboard an airplane. These DOAS systems measured SO2 in the flight direction and at ±40 mrad (2.3°) angles relative to it in both, horizontal and vertical directions. The approaches started at up to 25 km distance to the plume and SO2 was measured at all times well above the detection limit. In combination with radiative transfer studies, this study indicates that an extended volcanic cloud with a concentration of 1012 molecules cm-3 at typical flight levels of 10 km can be detected unambiguously at distances of up to 80 km away. This range provides enough time (approx. 5 min) for pilots to take action to avoid entering a volcanic cloud in the flight path, suggesting that this technique can be used as an effective aid to prevent dangerous aircraft encounters with potentially ash rich volcanic clouds.

Early in-flight detection of SO2 via Differential Optical Absorption Spectroscopy: a feasible aviation safety measure to prevent potential encounters with volcanic plumes

NASA Astrophysics Data System (ADS)

Vogel, L.; Galle, B.; Kern, C.; Delgado Granados, H.; Conde, V.; Norman, P.; Arellano, S.; Landgren, O.; Lübcke, P.; Alvarez Nieves, J. M.; Cárdenas Gonzáles, L.; Platt, U.

2011-05-01

Volcanic ash constitutes a risk to aviation, mainly due to its ability to cause jet engines to fail. Other risks include the possibility of abrasion of windshields and potentially serious damage to avionic systems. These hazards have been widely recognized since the early 1980s, when volcanic ash provoked several incidents of engine failure in commercial aircraft. In addition to volcanic ash, volcanic gases also pose a threat. Prolonged and/or cumulative exposure to sulphur dioxide (SO2) or sulphuric acid (H2SO4) aerosols potentially affects e.g. windows, air frame and may cause permanent damage to engines. SO2 receives most attention among the gas species commonly found in volcanic plumes because its presence above the lower troposphere is a clear proxy for a volcanic cloud and indicates that fine ash could also be present. Up to now, remote sensing of SO2 via Differential Optical Absorption Spectroscopy (DOAS) in the ultraviolet spectral region has been used to measure volcanic clouds from ground based, airborne and satellite platforms. Attention has been given to volcanic emission strength, chemistry inside volcanic clouds and measurement procedures were adapted accordingly. Here we present a set of experimental and model results, highlighting the feasibility of DOAS to be used as an airborne early detection system of SO2 in two spatial dimensions. In order to prove our new concept, simultaneous airborne and ground-based measurements of the plume of Popocatépetl volcano, Mexico, were conducted in April 2010. The plume extended at an altitude around 5250 m above sea level and was approached and traversed at the same altitude with several forward looking DOAS systems aboard an airplane. These DOAS systems measured SO2 in the flight direction and at ± 40 mrad (2.3°) angles relative to it in both, horizontal and vertical directions. The approaches started at up to 25 km distance to the plume and SO2 was measured at all times well above the detection limit. In combination with radiative transfer studies, this study indicates that an extended volcanic cloud with a concentration of 1012 molecules cm-3 at typical flight levels of 10 km can be detected unambiguously at distances of up to 80 km away. This range provides enough time (approx. 5 min) for pilots to take action to avoid entering a volcanic cloud in the flight path, suggesting that this technique can be used as an effective aid to prevent dangerous aircraft encounters with potentially ash rich volcanic clouds.

Early in-flight detection of SO2 via Differential Optical Absorption Spectroscopy: A feasible aviation safety measure to prevent potential encounters with volcanic plumes

USGS Publications Warehouse

Vogel, L.; Galle, B.; Kern, C.; Delgado, Granados H.; Conde, V.; Norman, P.; Arellano, S.; Landgren, O.; Lubcke, P.; Alvarez, Nieves J.M.; Cardenas, Gonzales L.; Platt, U.

2011-01-01

Volcanic ash constitutes a risk to aviation, mainly due to its ability to cause jet engines to fail. Other risks include the possibility of abrasion of windshields and potentially serious damage to avionic systems. These hazards have been widely recognized 5 since the early 1980s, when volcanic ash provoked several incidents of engine failure in commercial aircraft. In addition to volcanic ash, volcanic gases also pose a threat. Prolonged and/or cumulative exposure to sulphur dioxide (SO2) or sulphuric acid (H2SO4) aerosols potentially affects e.g. windows, air frame and may cause permanent damage to engines. SO2 receives most attention among the gas species commonly found in 10 volcanic plumes because its presence above the lower troposphere is a clear proxy for a volcanic cloud and indicates that fine ash could also be present. Up to now, remote sensing of SO2 via Differential Optical Absorption Spectroscopy (DOAS) in the ultraviolet spectral region has been used to measure volcanic clouds from ground based, airborne and satellite platforms. Attention has been given to vol- 15 canic emission strength, chemistry inside volcanic clouds and measurement procedures were adapted accordingly. Here we present a set of experimental and model results, highlighting the feasibility of DOAS to be used as an airborne early detection system of SO2 in two spatial dimensions. In order to prove our new concept, simultaneous airborne and ground-based measurements of the plume of Popocatepetl volcano, Mexico, were conducted in April 2010. The plume extended at an altitude around 5250 m above sea level and was approached and traversed at the same altitude with several forward looking DOAS systems aboard an airplane. These DOAS systems measured SO2 in the flight direction and at ±40 mrad (2.3◦) angles relative to it in both, horizontal and vertical directions. The approaches started at up to 25 km distance to 25 the plume and SO2 was measured at all times well above the detection limit. In combination with radiative transfer studies, this study indicates that an extended volcanic cloud with a concentration of 1012 molecules cm−3 at typical flight levels of 10 km can be detected unambiguously at distances of up to 80 km away. This range provides enough time (approx. 5 min) for pilots to take action to avoid entering a volcanic cloud in the flight path, suggesting that this technique can be used as an effective aid to prevent dangerous aircraft encounters with potentially ash rich volcanic clouds.

Characterizing a New Surface-Based Shortwave Cloud Retrieval Technique, Based on Transmitted Radiance for Soil and Vegetated Surface Types

NASA Technical Reports Server (NTRS)

Coddington, Odele; Pilewskie, Peter; Schmidt, K. Sebastian; McBride, Patrick J.; Vukicevic, Tomislava

2013-01-01

This paper presents an approach using the GEneralized Nonlinear Retrieval Analysis (GENRA) tool and general inverse theory diagnostics including the maximum likelihood solution and the Shannon information content to investigate the performance of a new spectral technique for the retrieval of cloud optical properties from surface based transmittance measurements. The cumulative retrieval information over broad ranges in cloud optical thickness (tau), droplet effective radius (r(sub e)), and overhead sun angles is quantified under two conditions known to impact transmitted radiation; the variability in land surface albedo and atmospheric water vapor content. Our conclusions are: (1) the retrieved cloud properties are more sensitive to the natural variability in land surface albedo than to water vapor content; (2) the new spectral technique is more accurate (but still imprecise) than a standard approach, in particular for tau between 5 and 60 and r(sub e) less than approximately 20 nm; and (3) the retrieved cloud properties are dependent on sun angle for clouds of tau from 5 to 10 and r(sub e) less than 10 nm, with maximum sensitivity obtained for an overhead sun.

A New Heuristic Anonymization Technique for Privacy Preserved Datasets Publication on Cloud Computing

NASA Astrophysics Data System (ADS)

Aldeen Yousra, S.; Mazleena, Salleh

2018-05-01

Recent advancement in Information and Communication Technologies (ICT) demanded much of cloud services to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data information involves medical, census, voter registration, social network, and customer services. Primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfill the confidentiality concerns is Privacy Preserving Data Mining (PPDM). The PPDM service in Cloud Computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to accomplish the privacy requirements. However, the well-known privacy preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, I propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing on cloud. The advantages of K-anonymity, L-diversity and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results revealed the superiority and outperformance of the developed technique than K-anonymity, L-diversity, and (α, k)-anonymity measure.

COLLABORATIVE RESEARCH:USING ARM OBSERVATIONS & ADVANCED STATISTICAL TECHNIQUES TO EVALUATE CAM3 CLOUDS FOR DEVELOPMENT OF STOCHASTIC CLOUD-RADIATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Somerville, Richard

2013-08-22

The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key stepmore » in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been a collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen).« less

[Application of single-band brightness variance ratio to the interference dissociation of cloud for satellite data].

PubMed

Qu, Wei-ping; Liu, Wen-qing; Liu, Jian-guo; Lu, Yi-huai; Zhu, Jun; Qin, Min; Liu, Cheng

2006-11-01

In satellite remote-sensing detection, cloud as an interference plays a negative role in data retrieval. How to discern the cloud fields with high fidelity thus comes as a need to the following research. A new method rooting in atmospheric radiation characteristics of cloud layer, in the present paper, presents a sort of solution where single-band brightness variance ratio is used to detect the relative intensity of cloud clutter so as to delineate cloud field rapidly and exactly, and the formulae of brightness variance ratio of satellite image, image reflectance variance ratio, and brightness temperature variance ratio of thermal infrared image are also given to enable cloud elimination to produce data free from cloud interference. According to the variance of the penetrating capability for different spectra bands, an objective evaluation is done on cloud penetration of them with the factors that influence penetration effect. Finally, a multi-band data fusion task is completed using the image data of infrared penetration from cirrus nothus. Image data reconstruction is of good quality and exactitude to show the real data of visible band covered by cloud fields. Statistics indicates the consistency of waveband relativity with image data after the data fusion.

Progress in the Development of Practical Remote Detection of Icing Conditions

NASA Technical Reports Server (NTRS)

Reehorst, Andrew; Politovich, Marcia K.; Zednik, Stephan; Isaac, George A.; Cober, Stewart

2006-01-01

The NASA Icing Remote Sensing System (NIRSS) has been under definition and development at NASA Glenn Research Center since 1997. The goal of this development activity is to produce and demonstrate the required sensing and data processing technologies required to accurately remotely detect and measure icing conditions aloft. As part of that effort NASA has teamed with NCAR to develop software to fuse data from multiple instruments into a single detected icing condition product. The multiple instrument approach utilizes a X-band vertical staring radar, a multifrequency microwave, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data is post-processed with a LabVIEW program with a resultant supercooled liquid water profile and aircraft hazard depiction. Ground-based, remotely-sensed measurements and in-situ measurements from research aircraft were gathered during the international 2003-2004 Alliance Icing Research Study (AIRS II). Comparisons between the remote sensing system s fused icing product and the aircraft measurements are reviewed here. While there are areas where improvement can be made, the cases examined suggest that the fused sensor remote sensing technique appears to be a valid approach.

Comparison of Monthly Mean Cloud Fraction and Cloud Optical depth Determined from Surface Cloud Radar, TOVS, AVHRR, and MODIS over Barrow, Alaska

NASA Technical Reports Server (NTRS)

Uttal, Taneil; Frisch, Shelby; Wang, Xuan-Ji; Key, Jeff; Schweiger, Axel; Sun-Mack, Sunny; Minnis, Patrick

2005-01-01

A one year comparison is made of mean monthly values of cloud fraction and cloud optical depth over Barrow, Alaska (71 deg 19.378 min North, 156 deg 36.934 min West) between 35 GHz radar-based retrievals, the TOVS Pathfinder Path-P product, the AVHRR APP-X product, and a MODIS based cloud retrieval product from the CERES-Team. The data sets represent largely disparate spatial and temporal scales, however, in this paper, the focus is to provide a preliminary analysis of how the mean monthly values derived from these different data sets compare, and determine how they can best be used separately, and in combination to provide reliable estimates of long-term trends of changing cloud properties. The radar and satellite data sets described here incorporate Arctic specific modifications that account for cloud detection challenges specific to the Arctic environment. The year 2000 was chosen for this initial comparison because the cloud radar data was particularly continuous and reliable that year, and all of the satellite retrievals of interest were also available for the year 2000. Cloud fraction was chosen as a comparison variable as accurate detection of cloud is the primary product that is necessary for any other cloud property retrievals. Cloud optical depth was additionally selected as it is likely the single cloud property that is most closely correlated to cloud influences on surface radiation budgets.

Observations of temporal change of nighttime cloud cover from Himawari 8 and ground-based sky camera over Chiba, Japan

NASA Astrophysics Data System (ADS)

Lagrosas, N.; Gacal, G. F. B.; Kuze, H.

2017-12-01

Detection of nighttime cloud from Himawari 8 is implemented using the difference of digital numbers from bands 13 (10.4µm) and 7 (3.9µm). The digital number difference of -1.39x104 can be used as a threshold to separate clouds from clear sky conditions. To look at observations from the ground over Chiba, a digital camera (Canon Powershot A2300) is used to take images of the sky every 5 minutes at an exposure time of 5s at the Center for Environmental Remote Sensing, Chiba University. From these images, cloud cover values are obtained using threshold algorithm (Gacal, et al, 2016). Ten minute nighttime cloud cover values from these two datasets are compared and analyzed from 29 May to 05 June 2017 (20:00-03:00 JST). When compared with lidar data, the camera can detect thick high level clouds up to 10km. The results show that during clear sky conditions (02-03 June), both camera and satellite cloud cover values show 0% cloud cover. During cloudy conditions (05-06 June), the camera shows almost 100% cloud cover while satellite cloud cover values range from 60 to 100%. These low values can be attributed to the presence of low-level thin clouds ( 2km above the ground) as observed from National Institute for Environmental Studies lidar located inside Chiba University. This difference of cloud cover values shows that the camera can produce accurate cloud cover values of low level clouds that are sometimes not detected by satellites. The opposite occurs when high level clouds are present (01-02 June). Derived satellite cloud cover shows almost 100% during the whole night while ground-based camera shows cloud cover values that range from 10 to 100% during the same time interval. The fluctuating values can be attributed to the presence of thin clouds located at around 6km from the ground and the presence of low level clouds ( 1km). Since the camera relies on the reflected city lights, it is possible that the high level thin clouds are not observed by the camera but is observed by the satellite. Also, this condition constitutes layers of clouds that are not observed by each camera. The results of this study show that one instrument can be used to correct each other to provide better cloud cover values. These corrections is dependent on the height and thickness of the clouds. No correction is necessary when the sky is clear.

Non-destructive evaluation of laboratory scale hydraulic fracturing using acoustic emission

NASA Astrophysics Data System (ADS)

Hampton, Jesse Clay

The primary objective of this research is to develop techniques to characterize hydraulic fractures and fracturing processes using acoustic emission monitoring based on laboratory scale hydraulic fracturing experiments. Individual microcrack AE source characterization is performed to understand the failure mechanisms associated with small failures along pre-existing discontinuities and grain boundaries. Individual microcrack analysis methods include moment tensor inversion techniques to elucidate the mode of failure, crack slip and crack normal direction vectors, and relative volumetric deformation of an individual microcrack. Differentiation between individual microcrack analysis and AE cloud based techniques is studied in efforts to refine discrete fracture network (DFN) creation and regional damage quantification of densely fractured media. Regional damage estimations from combinations of individual microcrack analyses and AE cloud density plotting are used to investigate the usefulness of weighting cloud based AE analysis techniques with microcrack source data. Two granite types were used in several sample configurations including multi-block systems. Laboratory hydraulic fracturing was performed with sample sizes ranging from 15 x 15 x 25 cm3 to 30 x 30 x 25 cm 3 in both unconfined and true-triaxially confined stress states using different types of materials. Hydraulic fracture testing in rock block systems containing a large natural fracture was investigated in terms of AE response throughout fracture interactions. Investigations of differing scale analyses showed the usefulness of individual microcrack characterization as well as DFN and cloud based techniques. Individual microcrack characterization weighting cloud based techniques correlated well with post-test damage evaluations.

Introduction and analysis of several FY3C-MWHTS cloud/rain screening methods

NASA Astrophysics Data System (ADS)

Li, Xiaoqing

2017-04-01

Data assimilation of satellite microwave sounders are very important for numerical weather prediction. Fengyun-3C (FY-3C),launched in September, 2013, has two such sounders: MWTS (MicroWave Temperature Sounder) and MWHTS (MicroWave Humidity and Temperature Sounder). These data should be quality-controlled before assimilation and cloud/rain detection is one of the crucial steps. This paper introduced different cloud/rain detection methods based on MWHTS, VIRR (Visible and InfraRed Radiometer) and MWRI (Microwave Radiation Imager) observations. We designed 6 cloud/rain detection combinations and then analyzed the application effect of these schemes. The difference between observations and model simulations for FY-3C MWHTS channels were calculated as a parameter for analysis. Both RTTOV and CRTM were used to fast simulate radiances of MWHTS channels.

Volcanic eruption detection with TOMS

NASA Technical Reports Server (NTRS)

Krueger, Arlin J.

1987-01-01

The Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) is designed for mapping of the atmospheric ozone distribution. Absorption by sulfur dioxide at the same ultraviolet spectral wavelengths makes it possible to observe and resolve the size of volcanic clouds. The sulfur dioxide absorption is discriminated from ozone and water clouds in the data processing by their spectral signatures. Thus, the sulfur dioxide can serve as a tracer which appears in volcanic eruption clouds because it is not present in other clouds. The detection limit with TOMS is close to the theoretical limit due to telemetry signal quantization of 1000 metric tons (5-sigma threshold) within the instrument field of view (50 by 50 km near the nadir). Requirements concerning the use of TOMS in detection of eruptions, geochemical cycles, and volcanic climatic effects are discussed.

Comparison between SAGE II and ISCCP high-level clouds. 1: Global and zonal mean cloud amounts

NASA Technical Reports Server (NTRS)

Liao, Xiaohan; Rossow, William B.; Rind, David

1995-01-01

Global high-level clouds identified in Stratospheric Aerosol and Gas Experiment II (SAGE II) occultation measurements for January and July in the period 1985 to 1990 are compared with near-nadir-looking observations from the International Satellite Cloud Climatology Project (ISCCP). Global and zonal mean high-level cloud amounts from the two data sets agree very well, if clouds with layer extinction coefficients of less than 0.008/km at 1.02 micrometers wavelength are removed from the SAGE II results and all detected clouds are interpreted to have an average horizontal size of about 75 km along the 200 km transimission path length of the SAGE II observations. The SAGE II results are much more sensitive to variations of assumed cloud size than to variations of detection threshold. The geographical distribution of cloud fractions shows good agreement, but systematic regional differences also indicate that the average cloud size varies somewhat among different climate regimes. The more sensitive SAGE II results show that about one third of all high-level clouds are missed by ISCCP but that these clouds have very low optical thicknesses (less than 0.1 at 0.6 micrometers wavelength). SAGE II sampling error in monthly zonal cloud fraction is shown to produce no bias, to be less than the intraseasonal natural variability, but to be comparable with the natural variability at longer time scales.

Study to determine cloud motion from meteorological satellite data

NASA Technical Reports Server (NTRS)

Clark, B. B.

1972-01-01

Processing techniques were tested for deducing cloud motion vectors from overlapped portions of pairs of pictures made from meteorological satellites. This was accomplished by programming and testing techniques for estimating pattern motion by means of cross correlation analysis with emphasis placed upon identifying and reducing errors resulting from various factors. Techniques were then selected and incorporated into a cloud motion determination program which included a routine which would select and prepare sample array pairs from the preprocessed test data. The program was then subjected to limited testing with data samples selected from the Nimbus 4 THIR data provided by the 11.5 micron channel.

Low-Frequency Carbon Recombination Lines in the Orion Molecular Cloud Complex

NASA Astrophysics Data System (ADS)

Tremblay, Chenoa D.; Jordan, Christopher H.; Cunningham, Maria; Jones, Paul A.; Hurley-Walker, Natasha

2018-05-01

We detail tentative detections of low-frequency carbon radio recombination lines from within the Orion molecular cloud complex observed at 99-129 MHz. These tentative detections include one alpha transition and one beta transition over three locations and are located within the diffuse regions of dust observed in the infrared at 100 μm, the Hα emission detected in the optical, and the synchrotron radiation observed in the radio. With these observations, we are able to study the radiation mechanism transition from collisionally pumped to radiatively pumped within the H ii regions within the Orion molecular cloud complex.

Newly detected molecules in dense interstellar clouds

NASA Astrophysics Data System (ADS)

Irvine, William M.; Avery, L. W.; Friberg, P.; Matthews, H. E.; Ziurys, L. M.

Several new interstellar molecules have been identified including C2S, C3S, C5H, C6H and (probably) HC2CHO in the cold, dark cloud TMC-1; and the discovery of the first interstellar phosphorus-containing molecule, PN, in the Orion "plateau" source. Further results include the observations of 13C3H2 and C3HD, and the first detection of HCOOH (formic acid) in a cold cloud.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data.

PubMed

Shang, Huazhe; Letu, Husi; Nakajima, Takashi Y; Wang, Ziming; Ma, Run; Wang, Tianxing; Lei, Yonghui; Ji, Dabin; Li, Shenshen; Shi, Jiancheng

2018-01-18

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

A composite large-scale CO survey at high galactic latitudes in the second quadrant

NASA Technical Reports Server (NTRS)

Heithausen, A.; Stacy, J. G.; De Vries, H. W.; Mebold, U.; Thaddeus, P.

1993-01-01

Surveys undertaken in the 2nd quadrant of the Galaxy with the CfA 1.2 m telescope have been combined to produce a map covering about 620 sq deg in the 2.6 mm CO(J = 1 - 0) line at high galactic latitudes. There is CO emission from molecular 'cirrus' clouds in about 13 percent of the region surveyed. The CO clouds are grouped together into three major cloud complexes with 29 individual members. All clouds are associated with infrared emission at 100 micron, although there is no one-to-one correlation between the corresponding intensities. CO emission is detected in all bright and dark Lynds' nebulae cataloged in that region; however not all CO clouds are visible on optical photographs as reflection or absorption features. The clouds are probably local. At an adopted distance of 240 pc cloud sizes range from O.1 to 30 pc and cloud masses from 1 to 1600 solar masses. The molecular cirrus clouds contribute between 0.4 and 0.8 M solar mass/sq pc to the surface density of molecular gas in the galactic plane. Only 26 percent of the 'infrared-excess clouds' in the area surveyed actually show CO and about 2/3 of the clouds detected in CO do not show an infrared excess.

Real-time Microseismic Processing for Induced Seismicity Hazard Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzel, Eric M.

Induced seismicity is inherently associated with underground fluid injections. If fluids are injected in proximity to a pre-existing fault or fracture system, the resulting elevated pressures can trigger dynamic earthquake slip, which could both damage surface structures and create new migration pathways. The goal of this research is to develop a fundamentally better approach to geological site characterization and early hazard detection. We combine innovative techniques for analyzing microseismic data with a physics-based inversion model to forecast microseismic cloud evolution. The key challenge is that faults at risk of slipping are often too small to detect during the site characterizationmore » phase. Our objective is to devise fast-running methodologies that will allow field operators to respond quickly to changing subsurface conditions.« less

A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

NASA Astrophysics Data System (ADS)

Russell, A. M.; Alliss, R. J.; Felton, B. D.

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources. This study seeks to revolutionize our understanding of and our ability to predict such atmospheric events through the mining of data from a high-resolution Numerical Weather Prediction (NWP) model. Specifically, output from the Weather Research and Forecasting (WRF) model is mined using a Random Forest (RF) ensemble classification and regression approach in order to improve the prediction of low cloud cover over the Haleakala summit of the Hawaiian island of Maui. RF techniques have a number of advantages including the ability to capture non-linear associations between the predictors (in this case physical variables from WRF such as temperature, relative humidity, wind speed and pressure) and the predictand (clouds), which becomes critical when dealing with the complex non-linear occurrence of clouds. In addition, RF techniques are capable of representing complex spatial-temporal dynamics to some extent. Input predictors to the WRF-based RF model are strategically selected based on expert knowledge and a series of sensitivity tests. Ultimately, three types of WRF predictors are chosen: local surface predictors, regional 3D moisture predictors and regional inversion predictors. A suite of RF experiments is performed using these predictors in order to evaluate the performance of the hybrid RF-WRF technique. The RF model is trained and tuned on approximately half of the input dataset and evaluated on the other half. The RF approach is validated using in-situ observations of clouds. All of the hybrid RF-WRF experiments demonstrated here significantly outperform the base WRF local low cloud cover forecasts in terms of the probability of detection and the overall bias. In particular, RF experiments that use only regional three-dimensional moisture predictors from the WRF model produce the highest accuracy when compared to RF experiments that use local surface predictors only or regional inversion predictors only. Furthermore, adding multiple types of WRF predictors and additional WRF predictors to the RF algorithm does not necessarily add more value in the resulting forecasts, indicating that it is better to have a small set of meaningful predictors than to have a vast set of indiscriminately-chosen predictors. This work also reveals that the WRF-based RF approach is highly sensitive to the time period over which the algorithm is trained and evaluated. Future work will focus on developing a similar WRF-based RF model for high cloud prediction and expanding the algorithm to two-dimensions horizontally.

Radiative transfer model for aerosols at infrared wavelengths for passive remote sensing applications: revisited.

PubMed

Ben-David, Avishai; Davidson, Charles E; Embury, Janon F

2008-11-01

We introduced a two-dimensional radiative transfer model for aerosols in the thermal infrared [Appl. Opt.45, 6860-6875 (2006)APOPAI0003-693510.1364/AO.45.006860]. In that paper we superimposed two orthogonal plane-parallel layers to compute the radiance due to a two-dimensional (2D) rectangular aerosol cloud. In this paper we revisit the model and correct an error in the interaction of the two layers. We derive new expressions relating to the signal content of the radiance from an aerosol cloud based on the concept of five directional thermal contrasts: four for the 2D diffuse radiance and one for direct radiance along the line of sight. The new expressions give additional insight on the radiative transfer processes within the cloud. Simulations for Bacillus subtilis var. niger (BG) bioaerosol and dustlike kaolin aerosol clouds are compared and contrasted for two geometries: an airborne sensor looking down and a ground-based sensor looking up. Simulation results suggest that aerosol cloud detection from an airborne platform may be more challenging than for a ground-based sensor and that the detection of an aerosol cloud in emission mode (negative direct thermal contrast) is not the same as the detection of an aerosol cloud in absorption mode (positive direct thermal contrast).

Probabilistic detection of volcanic ash using a Bayesian approach

PubMed Central

Mackie, Shona; Watson, Matthew

2014-01-01

Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into “ash” and “ash free” classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes “ash” and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection. Key Points Presentation of a probabilistic volcanic ash detection scheme Method for calculation of probability density function for ash observations Demonstration of a remote sensing technique for monitoring volcanic ash hazards PMID:25844278

Probabilistic detection of volcanic ash using a Bayesian approach.

PubMed

Mackie, Shona; Watson, Matthew

2014-03-16

Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection. Presentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards.

Cloud Point Extraction for Electroanalysis: Anodic Stripping Voltammetry of Cadmium.

PubMed

Rusinek, Cory A; Bange, Adam; Papautsky, Ian; Heineman, William R

2015-06-16

Cloud point extraction (CPE) is a well-established technique for the preconcentration of hydrophobic species from water without the use of organic solvents. Subsequent analysis is then typically performed via atomic absorption spectroscopy (AAS), UV-vis spectroscopy, or high performance liquid chromatography (HPLC). However, the suitability of CPE for electroanalytical methods such as stripping voltammetry has not been reported. We demonstrate the use of CPE for electroanalysis using the determination of cadmium (Cd(2+)) by anodic stripping voltammetry (ASV). Rather than using the chelating agents which are commonly used in CPE to form a hydrophobic, extractable metal complex, we used iodide and sulfuric acid to neutralize the charge on Cd(2+) to form an extractable ion pair. This offers good selectivity for Cd(2+) as no interferences were observed from other heavy metal ions. Triton X-114 was chosen as the surfactant for the extraction because its cloud point temperature is near room temperature (22-25 °C). Bare glassy carbon (GC), bismuth-coated glassy carbon (Bi-GC), and mercury-coated glassy carbon (Hg-GC) electrodes were compared for the CPE-ASV. A detection limit for Cd(2+) of 1.7 nM (0.2 ppb) was obtained with the Hg-GC electrode. ASV with CPE gave a 20x decrease (4.0 ppb) in the detection limit compared to ASV without CPE. The suitability of this procedure for the analysis of tap and river water samples was demonstrated. This simple, versatile, environmentally friendly, and cost-effective extraction method is potentially applicable to a wide variety of transition metals and organic compounds that are amenable to detection by electroanalytical methods.

Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

NASA Technical Reports Server (NTRS)

Herzfeld, Ute Christina; McDonald, Brian W.; Neumann, Thomas Allen; Wallin, Bruce F.; Neumann, Thomas A.; Markus, Thorsten; Brenner, Anita; Field, Christopher

2014-01-01

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection.

Operational processing and cloud boundary detection from micro pulse lidar data

NASA Technical Reports Server (NTRS)

Campbell, James R.; Hlavka, Dennis L.; Spinhirne, James D.; Scott, V. Stanley., III; Turner, David D.

1998-01-01

Micro Pulse Lidar (MPL) was developed at NASA Goddard Space Flight Center (GSFC) as the result of research on space-borne lidar techniques. It was designed to provide continuous, unattended observations of all significant atmospheric cloud and aerosol structure with a rugged, compact system design and the benefit of eye safety (Spinhirne 1993). The significant eye safety feature is achieved by using low pulse energies and high pulse repetition rates compared to standard lidar systems. MPL systems use a diode pumped 10 microj, 2500 Hz doubled Nd:YLF laser. In addition, a solid state Geiger mode avalanche photo diode (GAPD) photon counting detector is used allowing for quantum efficiencies approaching 70%. Other design features have previously been noted by Spinhirne (1995). Though a commercially available instrument, with nearly 20 systems operating around the world, the most extensive MPL work has come from those operated by the Atmospheric Radiation Measurement (ARM) (Stokes and Schwartz 1994) program. The diverse ability of the instrument relating to the measurement of basic cloud macrophysical structure and both cloud and aerosol radiative properties well suits the ARM research philosophy. MPL data can be used to yield many parameters including cloud boundary heights to the limit of signal attenuation, cloud scattering cross sections and optical thicknesses, planetary boundary layer heights and aerosol scattering profiles, including those into the stratosphere in nighttime cases (Hlavka et al 1996). System vertical resolution ranges from 30 m to 300 m (i.e. high and low resolution respectively) depending on system design. The lidar research group at GSFC plays an advisory role in the operation, calibration and maintenance of NASA and ARM owned MPL systems. Over the past three years, processing software and system correction techniques have been developed in anticipation of the increasing population of systems amongst the community. Datasets produced by three ARM-owned systems have served as the basis for this development. With two operating at the southern Great Plains Cloud and Radiation Testbed Site (SGP CART) since December 1993 and another at the Manus Island Atmospheric Radiation and Cloud Station (TWP ARCS) location in the tropical western Pacific since February 1997, the ARM archive contains over 4 years of observations. In addition, high resolution systems planning to come on-line at the North Slope, AK CART shortly with another scheduled to follow at the TWP ARCS-II will diversify this archive with more extensive observations.

Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

NASA Astrophysics Data System (ADS)

Stengel, Martin; Stapelberg, Stefan; Sus, Oliver; Schlundt, Cornelia; Poulsen, Caroline; Thomas, Gareth; Christensen, Matthew; Carbajal Henken, Cintia; Preusker, Rene; Fischer, Jürgen; Devasthale, Abhay; Willén, Ulrika; Karlsson, Karl-Göran; McGarragh, Gregory R.; Proud, Simon; Povey, Adam C.; Grainger, Roy G.; Fokke Meirink, Jan; Feofilov, Artem; Bennartz, Ralf; Bojanowski, Jedrzej S.; Hollmann, Rainer

2017-11-01

New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.

For each dataset a digital object identifier has been issued:

Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002

Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002

Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002

Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002

Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002

Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

GNSS Polarimetric Radio Occultations: Thermodynamical Structure of pecipitating clouds

NASA Astrophysics Data System (ADS)

De La Torre Juarez, M.; Padulles, R.; Cardellach, E.; Turk, F. J.; Tomás, S.; Ao, C. O.

2016-12-01

Recent analysis of changes in the hydrological sensitivity during a recent weakening of transient warming show that the representation of the processes linking the condensation of water vapor and the growth and invigoration of convective precipitation produce the greatest disparities between cloud resolving models and current observations of convective cloud systems. The temperature and moisture structure of a cloud environment is the main control on the thermodynamical processes leading to the development of precipitation. The surrounding environmental state acts as the broader sink and source for moisture exchange between clouds and their surroundings. As precipitation develops, water vapor condensation leads to an evolving 3D temperature and moisture structure in and near clouds different from the larger scale structure or the clear-sky environment. Yet there is a gap in existing space-based observations since conventional IR and microwave sounding data are degraded in the presence of clouds and precipitation. GNSS radio occultations (RO) are a low-cost approach to sounding the global atmosphere with high precision, accuracy and vertical resolution inside clouds and across land-ocean boundaries. GNSS provides reliable, sustained signal sources. While current RO provide no direct information on the associated precipitation state, a recently studied concept of Polarimetric RO (PRO) can characterize the moist thermodynamics within precipitating systems. Since precipitation-sized hydrometeors are non-spherically shaped, precipitation induces a cross-polarized component during propagation through clouds, recorded by a dual-channel RO receiver as a differential phase shift. Theoretical analysis performed using coincident TRMM Precipitation Radar and COSMIC observations shows that the polarimetric phase shift is sensitive to the path-integrated rain rate. Based on the expected signal-to-noise ratio (SNR) of simulated PRO measurements, the precision of the differential phase signal averaged over 1-sec has been estimated greater than 1.5 mm, with rain rates exceeding 5 mm hr-1 detectable above the instrument noise level 90% of the time. We present the technique and show analyses that prove its potential to characterize the lapse rate inside precipitating vs. non-precipitating clouds.

DSCOVR/EPIC observations of SO2 reveal dynamics of young volcanic eruption clouds

NASA Astrophysics Data System (ADS)

Carn, S. A.; Krotkov, N. A.; Taylor, S.; Fisher, B. L.; Li, C.; Bhartia, P. K.; Prata, F. J.

2017-12-01

Volcanic emissions of sulfur dioxide (SO2) and ash have been measured by ultraviolet (UV) and infrared (IR) sensors on US and European polar-orbiting satellites since the late 1970s. Although successful, the main limitation of these observations from low Earth orbit (LEO) is poor temporal resolution (once per day at low latitudes). Furthermore, most currently operational geostationary satellites cannot detect SO2, a key tracer of volcanic plumes, limiting our ability to elucidate processes in fresh, rapidly evolving volcanic eruption clouds. In 2015, the launch of the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) provided the first opportunity to observe volcanic clouds from the L1 Lagrange point. EPIC is a 10-band spectroradiometer spanning UV to near-IR wavelengths with two UV channels sensitive to SO2, and a ground resolution of 25 km. The unique L1 vantage point provides continuous observations of the sunlit Earth disk, from sunrise to sunset, offering multiple daily observations of volcanic SO2 and ash clouds in the EPIC field of view. When coupled with complementary retrievals from polar-orbiting UV and IR sensors such as the Ozone Monitoring Instrument (OMI), the Ozone Mapping and Profiler Suite (OMPS), and the Atmospheric Infrared Sounder (AIRS), we demonstrate how the increased observation frequency afforded by DSCOVR/EPIC permits more timely volcanic eruption detection and novel analyses of the temporal evolution of volcanic clouds. Although EPIC has detected several mid- to high-latitude volcanic eruptions since launch, we focus on recent eruptions of Bogoslof volcano (Aleutian Islands, AK, USA). A series of EPIC exposures from May 28-29, 2017, uniquely captures the evolution of SO2 mass in a young Bogoslof eruption cloud, showing separation of SO2- and ice-rich regions of the cloud. We show how analyses of these sequences of EPIC SO2 data can elucidate poorly understood processes in transient eruption clouds, such as the relative roles of H2S oxidation and ice scavenging in modifying volcanic SO2 emissions. Detection of these relatively small events also proves EPIC's ability to provide timely detection of volcanic clouds in the upper troposphere and lower stratosphere.

Harmonic regression based multi-temporal cloud filtering algorithm for Landsat 8

NASA Astrophysics Data System (ADS)

Joshi, P.

2015-12-01

Landsat data archive though rich is seen to have missing dates and periods owing to the weather irregularities and inconsistent coverage. The satellite images are further subject to cloud cover effects resulting in erroneous analysis and observations of ground features. In earlier studies the change detection algorithm using statistical control charts on harmonic residuals of multi-temporal Landsat 5 data have been shown to detect few prominent remnant clouds [Brooks, Evan B., et al, 2014]. So, in this work we build on this harmonic regression approach to detect and filter clouds using a multi-temporal series of Landsat 8 images. Firstly, we compute the harmonic coefficients using the fitting models on annual training data. This time series of residuals is further subjected to Shewhart X-bar control charts which signal the deviations of cloud points from the fitted multi-temporal fourier curve. For the process with standard deviation σ we found the second and third order harmonic regression with a x-bar chart control limit [Lσ] ranging between [0.5σ < Lσ < σ] as most efficient in detecting clouds. By implementing second order harmonic regression with successive x-bar chart control limits of L and 0.5 L on the NDVI, NDSI and haze optimized transformation (HOT), and utilizing the seasonal physical properties of these parameters, we have designed a novel multi-temporal algorithm for filtering clouds from Landsat 8 images. The method is applied to Virginia and Alabama in Landsat8 UTM zones 17 and 16 respectively. Our algorithm efficiently filters all types of cloud cover with an overall accuracy greater than 90%. As a result of the multi-temporal operation and the ability to recreate the multi-temporal database of images using only the coefficients of the fourier regression, our algorithm is largely storage and time efficient. The results show a good potential for this multi-temporal approach for cloud detection as a timely and targeted solution for the Landsat 8 research community, catering to the need for innovative processing solutions in the infant stage of the satellite.

Speeding Clouds May Reveal Invisible Black Holes

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2017-07-01

Several small, speeding clouds have been discovered at the center of our galaxy. A new study suggests that these unusual objects may reveal the lurking presence of inactive black holes.Peculiar Cloudsa) Velocity-integrated intensity map showing the location of the two high-velocity compact clouds, HCN0.0090.044 and HCN0.0850.094, in the context of larger molecular clouds. b) and c) Latitude-velocity and longitude-velocity maps for HCN0.0090.044 and HCN0.0850.094, respectively. d) and e) spectra for the two compacts clouds, respectively. Click for a closer look. [Takekawa et al. 2017]Sgr A*, the supermassive black hole marking the center of our galaxy, is surrounded by a region roughly 650 light-years across known as the Central Molecular Zone. This area at the heart of our galaxy is filled with large amounts of warm, dense molecular gas that has a complex distribution and turbulent kinematics.Several peculiar gas clouds have been discovered within the Central Molecular Zone within the past two decades. These clouds, dubbed high-velocity compact clouds, are characterized by their compact sizes and extremely broad velocity widths.What created this mysterious population of energetic clouds? The recent discovery of two new high-velocity compact clouds, reported on in a paper led by Shunya Takekawa (Keio University, Japan), may help us to answer this question.Two More to the CountUsing the James Clerk Maxwell Telescope in Hawaii, Takekawa and collaborators detected the small clouds near the circumnuclear disk at the centermost part of our galaxy. These two clouds have velocity spreads of -80 to -20 km/s and -80 to 0 km/s and compact sizes of just over 1 light-year. The clouds similar appearances and physical properties suggest that they may both have been formed by the same process.Takekawa and collaborators explore and discard several possible origins for these clouds, such as outflows from massive protostars (no massive, luminous stars have been detected affiliated with these clouds), interaction with supernova remnants (no supernova remnants have been detected toward the clouds), and cloudcloud collisions (such collisions leave other signs, like cavities in the parent cloud, which are not detected here).Masses and velocities of black holes that could create the two high-velocity compact clouds fall above the red and blue lines here. [Takekawa et al. 2017]Revealed on the PlungeAs an alternative explanation, Takekawa and collaborators propose that these two small,speeding cloudswere each created when a massive compact object plunged into a nearby molecular cloud. Since we dont seeany luminous stellar counterparts to the high-velocity compact clouds, this suggests that the responsibleobjects were invisible black holes. As each black hole tore through a molecular cloud, it dragged some of the clouds gas along behind it to form the high-velocity compact cloud.Does this explanation make sense statistically? The authors point out that the number of black holes predicted to silently lurk in the central 30 light-years of the Milky Way is around 10,000. This makes it entirely plausible that we could have caught sight of two of them as they revealed their presence while plunging through molecular clouds.If the authors interpretation is correct, then high-velocity compact clouds provide an excellent opportunity: we can search for these speeding bodiesto potentially discover inactive black holes that would otherwise go undetected.CitationShunya Takekawa et al 2017 ApJL 843 L11. doi:10.3847/2041-8213/aa79ee

Geomorphological activity at a rock glacier front detected with a 3D density-based clustering algorithm

NASA Astrophysics Data System (ADS)

Micheletti, Natan; Tonini, Marj; Lane, Stuart N.

2017-02-01

Acquisition of high density point clouds using terrestrial laser scanners (TLSs) has become commonplace in geomorphic science. The derived point clouds are often interpolated onto regular grids and the grids compared to detect change (i.e. erosion and deposition/advancement movements). This procedure is necessary for some applications (e.g. digital terrain analysis), but it inevitably leads to a certain loss of potentially valuable information contained within the point clouds. In the present study, an alternative methodology for geomorphological analysis and feature detection from point clouds is proposed. It rests on the use of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), applied to TLS data for a rock glacier front slope in the Swiss Alps. The proposed methods allowed the detection and isolation of movements directly from point clouds which yield to accuracies in the following computation of volumes that depend only on the actual registered distance between points. We demonstrated that these values are more conservative than volumes computed with the traditional DEM comparison. The results are illustrated for the summer of 2015, a season of enhanced geomorphic activity associated with exceptionally high temperatures.

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

NASA Technical Reports Server (NTRS)

Platnick, Steven; King, Michael D.; Wind, Galina; Hubanks, Paul; Arnold, G. Thomas; Amarasinghe, Nandana

2010-01-01

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Probabilistic detection of volcanic ash using a Bayesian approach

NASA Astrophysics Data System (ADS)

Mackie, Shona; Watson, Matthew

2014-03-01

Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into "ash" and "ash free" classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes "ash" and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.

Global Analysis of Aerosol Properties Above Clouds

NASA Technical Reports Server (NTRS)

Waquet, F.; Peers, F.; Ducos, F.; Goloub, P.; Platnick, S. E.; Riedi, J.; Tanre, D.; Thieuleux, F.

2013-01-01

The seasonal and spatial varability of Aerosol Above Cloud (AAC) properties are derived from passive satellite data for the year 2008. A significant amount of aerosols are transported above liquid water clouds on the global scale. For particles in the fine mode (i.e., radius smaller than 0.3 m), including both clear sky and AAC retrievals increases the global mean aerosol optical thickness by 25(+/- 6%). The two main regions with man-made AAC are the tropical Southeast Atlantic, for biomass burning aerosols, and the North Pacific, mainly for pollutants. Man-made AAC are also detected over the Arctic during the spring. Mineral dust particles are detected above clouds within the so-called dust belt region (5-40 N). AAC may cause a warming effect and bias the retrieval of the cloud properties. This study will then help to better quantify the impacts of aerosols on clouds and climate.

Remote sensing observing systems of the Meteorological Service of Catalonia (SMC): application to thunderstorm surveillance

NASA Astrophysics Data System (ADS)

Argemí, O.; Bech, J.; Pineda, N.; Rigo, T.

2009-09-01

Remote sensing observing systems of the Meteorological Service of Catalonia (SMC) have been upgraded during the last years with newer technologies and enhancements. Recent changes on the weather radar network have been motivated to improve precipitation estimates by radar as well as meteorological surveillance in the area of Catalonia. This region has approximately 32,000 square kilometres and is located in the NE of Spain, limited by the Pyrenees to the North (with mountains exceeding 3000 m) and by the Mediterranean Sea to the East and South. In the case of the total lightning (intra-cloud and cloud-to-ground lightning) detection system, the current upgrades will assure a better lightning detection efficiency and location accuracy. Both upgraded systems help to enhance the tracking and the study of thunderstorm events. Initially, the weather radar network was designed to cover the complex topography of Catalonia and surrounding areas to support the regional administration, which includes civil protection and water authorities. The weather radar network was upgraded in 2008 with the addition of a new C-band Doppler radar system, which is located in the top of La Miranda Mountain (Tivissa) in the southern part of Catalonia enhancing the coverage, particularly to the South and South-West. Technically the new radar is very similar to the last one installed in 2003 (Creu del Vent radar), using a 4 m antenna (i.e., 1 degree beam width), a Vaisala-Sigmet RVP-8 digital receiver and processor and a low power transmitter using a Travelling Wave Tube (TWT) amplifier. This design allows using pulse-compression techniques to enhance radial resolution and sensitivity. Currently, the SMC is upgrading its total lightning detection system, operational since 2003. While a fourth sensor (Amposta) was added last year to enlarge the system coverage, all sensors and central processor will be upgraded this year to the new Vaisala’s total lightning location technology. The new LS8000 sensor configuration integrates two lightning detection technologies: VHF interferometry technology provides high performance in detection of cloud lightning, while LF combined magnetic direction finding and time-of-arrival technology offers a highest detection efficiency and accurate location for cloud-to-ground lightning strokes. The presentation describes in some detail all this innovation in remote sensing observing networks and also reports some examples over Catalonia which is frequently affected by different types of convective events, including severe weather (large hail, tornadic events, etc.) and heavy rainfall episodes.

Snow cover detection algorithm using dynamic time warping method and reflectances of MODIS solar spectrum channels

NASA Astrophysics Data System (ADS)

Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo

2016-10-01

Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria. The result of our study detect as snow cover in the several regions which are did not detected as snow in MOD10 L2 and detected as snow cover in MODIS RGB image. The result of our study can improve accuracy of other surface product such as land surface reflectance and land surface emissivity. Also it can use input data of hydrological modeling.

Spectral signatures of polar stratospheric clouds and sulfate aerosol

NASA Technical Reports Server (NTRS)

Massie, S. T.; Bailey, P. L.; Gille, J. C.; Lee, E. C.; Mergenthaler, J. L.; Roche, A. E.; Kumer, J. B.; Fishbein, E. F.; Waters, J. W.; Lahoz, W. A.

1994-01-01

Multiwavelength observations of Antarctic and midlatitude aerosol by the Cryogenic Limb Array Etalon Spectrometer (CLAES) experiment on the Upper Atmosphere Research Satellite (UARS) are used to demonstrate a technique that identifies the location of polar stratospheric clouds. The technique discussed uses the normalized area of the triangle formed by the aerosol extinctions at 925, 1257, and 1605/cm (10.8, 8.0, and 6.2 micrometers) to derive a spectral aerosol measure M of the aerosol spectrum. Mie calculations for spherical particles and T-matrix calculations for spheriodal particles are used to generate theoretical spectral extinction curves for sulfate and polar stratospheric cloud particles. The values of the spectral aerosol measure M for the sulfate and polar stratospheric cloud particles are shown to be different. Aerosol extinction data, corresponding to temperatures between 180 and 220 K at a pressure of 46 hPa (near 21-km altitude) for 18 August 1992, are used to demonstrate the technique. Thermodynamic calculations, based upon frost-point calculations and laboratory phase-equilibrium studies of nitric acid trihydrate, are used to predict the location of nitric acid trihydrate cloud particles.

A robust threshold-based cloud mask for the HRV channel of MSG SEVIRI

NASA Astrophysics Data System (ADS)

Bley, S.; Deneke, H.

2013-03-01

A robust threshold-based cloud mask for the high-resolution visible (HRV) channel (1 × 1 km2) of the METEOSAT SEVIRI instrument is introduced and evaluated. It is based on operational EUMETSAT cloud mask for the low resolution channels of SEVIRI (3 × 3 km2), which is used for the selection of suitable thresholds to ensure consistency with its results. The aim of using the HRV channel is to resolve small-scale cloud structures which cannot be detected by the low resolution channels. We find that it is of advantage to apply thresholds relative to clear-sky reflectance composites, and to adapt the threshold regionally. Furthermore, the accuracy of the different spectral channels for thresholding and the suitability of the HRV channel are investigated for cloud detection. The case studies show different situations to demonstrate the behaviour for various surface and cloud conditions. Overall, between 4 and 24% of cloudy low-resolution SEVIRI pixels are found to contain broken clouds in our test dataset depending on considered region. Most of these broken pixels are classified as cloudy by EUMETSAT's cloud mask, which will likely result in an overestimate if the mask is used as estimate of cloud fraction.

Cloud cover and solar disk state estimation using all-sky images: deep neural networks approach compared to routine methods

NASA Astrophysics Data System (ADS)

Krinitskiy, Mikhail; Sinitsyn, Alexey

2017-04-01

Shortwave radiation is an important component of surface heat budget over sea and land. To estimate them accurate observations of cloud conditions are needed including total cloud cover, spatial and temporal cloud structure. While massively observed visually, for building accurate SW radiation parameterizations cloud structure needs also to be quantified using precise instrumental measurements. While there already exist several state of the art land-based cloud-cameras that satisfy researchers needs, their major disadvantages are associated with inaccuracy of all-sky images processing algorithms which typically result in the uncertainties of 2-4 octa of cloud cover estimates with the resulting true-scoring cloud cover accuracy of about 7%. Moreover, none of these algorithms determine cloud types. We developed an approach for cloud cover and structure estimating, which provides much more accurate estimates and also allows for measuring additional characteristics. This method is based on the synthetic controlling index, namely the "grayness rate index", that we introduced in 2014. Since then this index has already demonstrated high efficiency being used along with the technique namely the "background sunburn effect suppression", to detect thin clouds. This made it possible to significantly increase the accuracy of total cloud cover estimation in various sky image states using this extension of routine algorithm type. Errors for the cloud cover estimates significantly decreased down resulting the mean squared error of about 1.5 octa. Resulting true-scoring accuracy is more than 38%. The main source of this approach uncertainties is the solar disk state determination errors. While the deep neural networks approach lets us to estimate solar disk state with 94% accuracy, the final result of total cloud estimation still isn`t satisfying. To solve this problem completely we applied the set of machine learning algorithms to the problem of total cloud cover estimation directly. The accuracy of this approach varies depending on algorithm choice. Deep neural networks demonstrated the best accuracy of more than 96%. We will demonstrate some approaches and the most influential statistical features of all-sky images that lets the algorithm reach that high accuracy. With the use of our new optical package a set of over 480`000 samples has been collected in several sea missions in 2014-2016 along with concurrent standard human observed and instrumentally recorded meteorological parameters. We will demonstrate the results of the field measurements and will discuss some still remaining problems and the potential of the further developments of machine learning approach.

Inhomogeneous models of the Venus clouds containing sulfur

NASA Technical Reports Server (NTRS)

Smith, S. M.; Pollack, J. B.; Giver, L. P.; Cuzzi, J. N.; Podolak, M.

1979-01-01

Based on the suggestion that elemental sulfur is responsible for the yellow color of Venus, calculations are compared at 3.4 microns of the reflectivity phase function of two sulfur containing inhomogeneous cloud models with that of a homogeneous model. Assuming reflectivity observations with 25% or less total error, comparison of the model calculations leads to a minimum detectable mass of sulfur equal to 7% of the mass of sulfuric acid for the inhomogeneous drop model. For the inhomogeneous cloud model the comparison leads to a minimum detectable mass of sulfur between 17% and 38% of the mass of the acid drops, depending upon the actual size of the large particles. It is concluded that moderately accurate 3.4 microns reflectivity observations are capable of detecting quite small amounts of elemental sulfur at the top of the Venus clouds.

The role of global cloud climatologies in validating numerical models

NASA Technical Reports Server (NTRS)

HARSHVARDHAN

1991-01-01

The net upward longwave surface radiation is exceedingly difficult to measure from space. A hybrid method using General Circulation Model (GCM) simulations and satellite data from the Earth Radiation Budget Experiment (ERBE) and the International Satellite Cloud Climatology Project (ISCCP) was used to produce global maps of this quantity over oceanic areas. An advantage of this technique is that no independent knowledge or assumptions regarding cloud cover for a particular month are required. The only information required is a relationship between the cloud radiation forcing (CRF) at the top of the atmosphere and that at the surface, which is obtained from the GCM simulation. A flow diagram of the technique and results are given.

Investigating the Accuracy of Point Clouds Generated for Rock Surfaces

NASA Astrophysics Data System (ADS)

Seker, D. Z.; Incekara, A. H.

2016-12-01

Point clouds which are produced by means of different techniques are widely used to model the rocks and obtain the properties of rock surfaces like roughness, volume and area. These point clouds can be generated by applying laser scanning and close range photogrammetry techniques. Laser scanning is the most common method to produce point cloud. In this method, laser scanner device produces 3D point cloud at regular intervals. In close range photogrammetry, point cloud can be produced with the help of photographs taken in appropriate conditions depending on developing hardware and software technology. Many photogrammetric software which is open source or not currently provide the generation of point cloud support. Both methods are close to each other in terms of accuracy. Sufficient accuracy in the mm and cm range can be obtained with the help of a qualified digital camera and laser scanner. In both methods, field work is completed in less time than conventional techniques. In close range photogrammetry, any part of rock surfaces can be completely represented owing to overlapping oblique photographs. In contrast to the proximity of the data, these two methods are quite different in terms of cost. In this study, whether or not point cloud produced by photographs can be used instead of point cloud produced by laser scanner device is investigated. In accordance with this purpose, rock surfaces which have complex and irregular shape located in İstanbul Technical University Ayazaga Campus were selected as study object. Selected object is mixture of different rock types and consists of both partly weathered and fresh parts. Study was performed on a part of 30m x 10m rock surface. 2D and 3D analysis were performed for several regions selected from the point clouds of the surface models. 2D analysis is area-based and 3D analysis is volume-based. Analysis conclusions showed that point clouds in both are similar and can be used as alternative to each other. This proved that point cloud produced using photographs which are both economical and enables to produce data in less time can be used in several studies instead of point cloud produced by laser scanner.

Cirrus cloud retrieval from MSG/SEVIRI during day and night using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca

2017-04-01

By covering a large part of the Earth, cirrus clouds play an important role in climate as they reflect incoming solar radiation and absorb outgoing thermal radiation. Nevertheless, the cirrus clouds remain one of the largest uncertainties in atmospheric research and the understanding of the physical processes that govern their life cycle is still poorly understood, as is their representation in climate models. To monitor and better understand the properties and physical processes of cirrus clouds, it's essential that those tenuous clouds can be observed from geostationary spaceborne imagers like SEVIRI (Spinning Enhanced Visible and InfraRed Imager), that possess a high temporal resolution together with a large field of view and play an important role besides in-situ observations for the investigation of cirrus cloud processes. CiPS (Cirrus Properties from Seviri) is a new algorithm targeting thin cirrus clouds. CiPS is an artificial neural network trained with coincident SEVIRI and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations in order to retrieve a cirrus cloud mask along with the cloud top height (CTH), ice optical thickness (IOT) and ice water path (IWP) from SEVIRI. By utilizing only the thermal/IR channels of SEVIRI, CiPS can be used during day and night making it a powerful tool for the cirrus life cycle analysis. Despite the great challenge of detecting thin cirrus clouds and retrieving their properties from a geostationary imager using only the thermal/IR wavelengths, CiPS performs well. Among the cirrus clouds detected by CALIOP, CiPS detects 70 and 95 % of the clouds with an optical thickness of 0.1 and 1.0 respectively. Among the cirrus free pixels, CiPS classify 96 % correctly. For the CTH retrieval, CiPS has a mean absolute percentage error of 10 % or less with respect to CALIOP for cirrus clouds with a CTH greater than 8 km. For the IOT retrieval, CiPS has a mean absolute percentage error of 100 % or less with respect to CALIOP for cirrus clouds with an optical thickness down to 0.07. For such thin cirrus clouds an error of 100 % should be regarded as low from a geostationary imager like SEVIRI. The IWP retrieved by CiPS shows a similar performance, but has larger deviations for the thinner cirrus clouds.

Comparative Study of Aerosol and Cloud Detected by CALIPSO and OMI

NASA Technical Reports Server (NTRS)

Chen, Zhong; Torres, Omar; McCormick, M. Patrick; Smith, William; Ahn, Changwoo

2012-01-01

The Ozone Monitoring Instrument (OMI) on the Aura Satellite detects the presence of desert dust and smoke particles (also known as aerosols) in terms of a parameter known as the UV Aerosol Index (UV AI). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission measures the vertical distribution of aerosols and clouds. Aerosols and clouds play important roles in the atmosphere and climate system. Accurately detecting their presence, altitude, and properties using satellite radiance measurements is a very important task. This paper presents a comparative analysis of the CALIPSO Version 2 Vertical Feature Mask (VFM) product with the (OMI) UV Aerosol Index (UV AI) and reflectivity datasets for a full year of 2007. The comparison is done at regional and global scales. Based on CALIPSO arid OMI observations, the vertical and horizontal extent of clouds and aerosols are determined and the effects of aerosol type selection, load, cloud fraction on aerosol identification are discussed. It was found that the spatial-temporal correlation found between CALIPSO and OMI observations, is strongly dependent on aerosol types and cloud contamination. CALIPSO is more sensitivity to cloud and often misidentifies desert dust aerosols as cloud, while some small scale aerosol layers as well as some pollution aerosols are unidentified by OMI UV AI. Large differences in aerosol distribution patterns between CALIPSO and OMI are observed, especially for the smoke and pollution aerosol dominated areas. In addition, the results found a significant correlation between CALIPSO lidar 1064 nm backscatter and the OMI UV AI over the study regions.

A Bispectral Composite Threshold Approach for Automatic Cloud Detection in VIIRS Imagery

NASA Technical Reports Server (NTRS)

LaFontaine Frank J.; Jedlovec, Gary J.

2015-01-01

The detection of clouds in satellite imagery has a number of important applications in weather and climate studies. The presence of clouds can alter the energy budget of the Earth-atmosphere system through scattering and absorption of shortwave radiation and the absorption and re-emission of infrared radiation at longer wavelengths. The scattering and absorption characteristics of clouds vary with the microphysical properties of clouds, hence the cloud type. Thus, detecting the presence of clouds over a region in satellite imagery is important in order to derive atmospheric or surface parameters that give insight into weather and climate processes. For many applications however, clouds are a contaminant whose presence interferes with retrieving atmosphere or surface information. In these cases, is important to isolate cloud-free pixels, used to retrieve atmospheric thermodynamic information or surface geophysical parameters, from cloudy ones. This abstract describes an application of a two-channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels from VIIRS in two spectral tests; a straight-forward infrared threshold test with the longwave channel and a shortwave - longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The paper and subsequent presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.

Applications of 3D-EDGE Detection for ALS Point Cloud

NASA Astrophysics Data System (ADS)

Ni, H.; Lin, X. G.; Zhang, J. X.

2017-09-01

Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point's neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

NASA Astrophysics Data System (ADS)

Lolli, Simone; Madonna, Fabio; Rosoldi, Marco; Campbell, James R.; Welton, Ellsworth J.; Lewis, Jasper R.; Gu, Yu; Pappalardo, Gelsomina

2018-03-01

In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m-2 at surface and 0.007 W m-2 at top of the atmosphere) and dust aerosol layers (0.7 W m-2 at surface and 0.85 W m-2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m-2 at surface and 2.7 W m-2 at top of the atmosphere) and opaque (7.7 W m-2 at surface and 11.8 W m-2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20-150 sr) than for clouds (20-35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

A low-frequency near-field interferometric-TOA 3-D Lightning Mapping Array

NASA Astrophysics Data System (ADS)

Lyu, Fanchao; Cummer, Steven A.; Solanki, Rahulkumar; Weinert, Joel; McTague, Lindsay; Katko, Alex; Barrett, John; Zigoneanu, Lucian; Xie, Yangbo; Wang, Wenqi

2014-11-01

We report on the development of an easily deployable LF near-field interferometric-time of arrival (TOA) 3-D Lightning Mapping Array applied to imaging of entire lightning flashes. An interferometric cross-correlation technique is applied in our system to compute windowed two-sensor time differences with submicrosecond time resolution before TOA is used for source location. Compared to previously reported LF lightning location systems, our system captures many more LF sources. This is due mainly to the improved mapping of continuous lightning processes by using this type of hybrid interferometry/TOA processing method. We show with five station measurements that the array detects and maps different lightning processes, such as stepped and dart leaders, during both in-cloud and cloud-to-ground flashes. Lightning images mapped by our LF system are remarkably similar to those created by VHF mapping systems, which may suggest some special links between LF and VHF emission during lightning processes.

Decomposition Techniques for Icesat/glas Full-Waveform Data

NASA Astrophysics Data System (ADS)

Liu, Z.; Gao, X.; Li, G.; Chen, J.

2018-04-01

The geoscience laser altimeter system (GLAS) on the board Ice, Cloud, and land Elevation Satellite (ICESat), is the first long-duration space borne full-waveform LiDAR for measuring the topography of the ice shelf and temporal variation, cloud and atmospheric characteristics. In order to extract the characteristic parameters of the waveform, the key step is to process the full waveform data. In this paper, the modified waveform decomposition method is proposed to extract the echo components from full-waveform. First, the initial parameter estimation is implemented through data preprocessing and waveform detection. Next, the waveform fitting is demonstrated using the Levenberg-Marquard (LM) optimization method. The results show that the modified waveform decomposition method can effectively extract the overlapped echo components and missing echo components compared with the results from GLA14 product. The echo components can also be extracted from the complex waveforms.

Automatic extraction of blocks from 3D point clouds of fractured rock

NASA Astrophysics Data System (ADS)

Chen, Na; Kemeny, John; Jiang, Qinghui; Pan, Zhiwen

2017-12-01

This paper presents a new method for extracting blocks and calculating block size automatically from rock surface 3D point clouds. Block size is an important rock mass characteristic and forms the basis for several rock mass classification schemes. The proposed method consists of four steps: 1) the automatic extraction of discontinuities using an improved Ransac Shape Detection method, 2) the calculation of discontinuity intersections based on plane geometry, 3) the extraction of block candidates based on three discontinuities intersecting one another to form corners, and 4) the identification of "true" blocks using an improved Floodfill algorithm. The calculated block sizes were compared with manual measurements in two case studies, one with fabricated cardboard blocks and the other from an actual rock mass outcrop. The results demonstrate that the proposed method is accurate and overcomes the inaccuracies, safety hazards, and biases of traditional techniques.

CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

NASA Technical Reports Server (NTRS)

Nowottnick, Ed; Yorks, John; McGill, Matt; Scott, Stan; Palm, Stephen; Hlavka, Dennis; Hart, William; Selmer, Patrick; Kupchock, Andrew; Pauly, Rebecca

2017-01-01

Using GEOS-5, we are developing a 1D ENS approach for assimilating CATS near real time observations of total attenuated backscatter at 1064 nm: a) After performing a 1-ENS assimilation of a cloud-free profile, the GEOS-5 analysis closely followed observed total attenuated backscatter. b) Vertical localization length scales were varied for the well-mixed PBL and the free troposphere After assimilating a cloud free segment of a CATS granule, the fine detail of a dust event was obtained in the GEOS-5 analysis for both total attenuated backscatter and extinction. Future Work: a) Explore horizontal localization and test within a cloudy aerosol layer. b) Address noisy analysis increments in the free troposphere where both CATS and GEOS-5 aerosol loadings are low. c) Develop a technique to screen CATS ground return from profiles. d) "Dynamic" lidar ratio that will evolve in conjunction with simulated aerosol mixtures.

A comparison of the static and flow methods for the detection of ice nuclei

NASA Astrophysics Data System (ADS)

Hussain, K.; Kayani, S. A.

The use of the membrane-filter processing chamber to study ice nuclei concentrations has become wide-spread since its introduction by Bigg et al. in 1961. The technique is convenient because of the simplicity of its operation and because it could be run remote from the place of field study. It has however been found to suffer from a number of drawbacks, namely, the volume effect, the chamber height effect, the vapour depletion effect, etc. Comparison of the results obtained by running a traditional filter processor and a continuous flow chamber under identical temperature and humidity conditions for polluted Manchester air has shown that the latter technique detects more ice nuclei than the former one by a factor of about 14±4. These results confirm that the filter technique suffers from the vapour depletion effect. The present results are in agreement with Bigg et al., Mossop and Thorndike, and King. In the light of our findings the filter technique does not appear to be a standard method. Therefore the ice nuclei data obtained with the filter method should not be extended to clouds in order to study their microphysical properties.

Indoor Modelling from Slam-Based Laser Scanner: Door Detection to Envelope Reconstruction

NASA Astrophysics Data System (ADS)

Díaz-Vilariño, L.; Verbree, E.; Zlatanova, S.; Diakité, A.

2017-09-01

Updated and detailed indoor models are being increasingly demanded for various applications such as emergency management or navigational assistance. The consolidation of new portable and mobile acquisition systems has led to a higher availability of 3D point cloud data from indoors. In this work, we explore the combined use of point clouds and trajectories from SLAM-based laser scanner to automate the reconstruction of building indoors. The methodology starts by door detection, since doors represent transitions from one indoor space to other, which constitutes an initial approach about the global configuration of the point cloud into building rooms. For this purpose, the trajectory is used to create a vertical point cloud profile in which doors are detected as local minimum of vertical distances. As point cloud and trajectory are related by time stamp, this feature is used to subdivide the point cloud into subspaces according to the location of the doors. The correspondence between subspaces and building rooms is not unambiguous. One subspace always corresponds to one room, but one room is not necessarily depicted by just one subspace, for example, in case of a room containing several doors and in which the acquisition is performed in a discontinue way. The labelling problem is formulated as combinatorial approach solved as a minimum energy optimization. Once the point cloud is subdivided into building rooms, envelop (conformed by walls, ceilings and floors) is reconstructed for each space. The connectivity between spaces is included by adding the previously detected doors to the reconstructed model. The methodology is tested in a real case study.

Cloud Detection Using Measured and Modeled State Parameters

NASA Technical Reports Server (NTRS)

Yi, Y.; Minnis, P.; Huang, J.; Ayers, J. K.; Doelling, D. R.; Khaiyer, M. M.; Nordeen, M. L.

2004-01-01

In this study, hourly RUC analyses were used to examine the differences between RH and temperature values from RUC reanalysis data and from radiosonde atmospheric profiles obtained at the ARM SCF. The results show that the temperature observations from the SONDE and RUC are highly correlated. The RHs are also well-correlated, but the SONDE values generally exceed those from RUC. Inside cloud layers, the RH from RUC is 2-14% lower than the RH from SONDE for all RUC layers. Although the layer mean RH within clouds is much greater than the layer mean RH outside cloud or in the clear-sky, RH thresholds chosen as a function of temperature can more accurately diagnose cloud occurrence for either dataset. For overcast clouds, it was found that the 50% probability RH threshold for diagnosing a cloud, within a given upper tropospheric layer is roughly 90% for the Vaisala RS80-15LH radisonde and 80% for RUC data. While for the partial cloud (cloud amount is less than 90%), the RH thresholds of SONDE are close to RUC for a given probability in upper tropospheric layers. The probabilities of detecting clouds at a given RH and temperature should be useful for a variety of application such as the development of new cloud parameterizations or for estimating the vertical profile of cloudiness underneath a given cloud observed from the satellite to construct a 3-D cloud data set for computing atmospheric radiative heating profiles or determining potential aircraft icing conditions.

Outer satellite atmospheres: Their nature and planetary interactions. [atmospheric models for Amalthea, Ganymede, Callisto, and Titan are presented

NASA Technical Reports Server (NTRS)

Smyth, W. H.

1978-01-01

Results show that Amalthea is likely to form a tightly-bound partial toroidal-shaped hydrogen cloud about its planet, while Ganymede, Callisto and Titan may have rather large, complete and nearly symmetric toroidal-shaped clouds. The toroidal cloud for Amalthea compares favorably with spacecraft data of Pioneer 10 for a satellite escape flux of order 10 to the 11th power atoms/sq cm/sec. Model results for Ganymede, Callisto and Titan suggest that these extended hydrogen atmospheres are likely to be detected by the Voyager spacecrafts and that Titan's cloud might also be detected by the Pioneer 11 spacecraft. Ions created because of atoms lost through ionization processes from these four extended hydrogen atmospheres and from the sodium cloud of Io are discussed.

Ground-based LiDAR application to characterize sea cliff instability processes along a densely populated coastline in Southern Italy

NASA Astrophysics Data System (ADS)

Esposito, Giuseppe; Semaan, Fouad; Salvini, Riccardo; Troise, Claudia; Somma, Renato; Matano, Fabio; Sacchi, Marco

2017-04-01

Sea cliff retreatment along the coastline of the Campi Flegrei volcanic area (Southern Italy) is becoming a threat for public and private structures due to the massive urbanization occurred in the last few decades. In this area, geological features of the outcropping rocks represent one of the most important factors conditioning the sea cliff retreatment. In fact, pyroclastic deposits formed by pumices, scoria, ashes and lapilli are arranged in weakly to moderately welded layers of variable thicknesses, resulting very erodible and prone to landslide processes. Available methods to evaluate topographic changes and retreat rates of sea cliffs include a variety of geomatic techniques, like terrestrial and aerial photogrammetry and LiDAR (Light Detection And Ranging). By means of such techniques, it is in fact possible to obtain high resolution topography of sea cliffs and perform multi-temporal change detection analysis. In this contribution, we present an application of Terrestrial Laser Scanning (TLS or ground-based LiDAR) aimed to identify and quantify instability processes acting along the Torrefumo coastal cliff, in the Campi Flegrei area. Specifically, we acquired a series of 3D point clouds on the years 2013 and 2016, and compared them through a cloud-to-cloud distance computation. Furthermore, a statistical analysis was applied to the change detection results. In this way, an inventory of the cliff failures occurred along the Torrefumo cliff in the 2013-2016 time span was created, as well as the spatial and volumetric distribution of these failures was evaluated. The volumetric analysis shows that large collapses occurred rarely, whereas the spatial analysis shows that the majority of failures occurred in the middle and upper parts of the cliff face. Results also show that both rock fall and surficial erosion processes contribute to the cliff retreatment, acting in turn according to the geological properties of the involved pyroclastic deposits. The presented TLS approach proves to be a cost and time efficient method for characterizing the geomorphic changes involving the sea cliff surfaces over a short-time period (i.e. monthly or yearly). The accuracy of the acquired data allows the characterization of a full range of failures to be located and quantified with a level of detail not reachable using traditional techniques. Results obtained in this research will be used in future applications to assess hazard conditions affecting the anthropic structures built close to the cliff top.

Determination of parabens using two microextraction methods coupled with capillary liquid chromatography-UV detection.

PubMed

Chen, Chen-Wen; Hsu, Wen-Chan; Lu, Ya-Chen; Weng, Jing-Ru; Feng, Chia-Hsien

2018-02-15

Parabens are common preservatives and environmental hormones. As such, possible detrimental health effects could be amplified through their widespread use in foods, cosmetics, and pharmaceutical products. Thus, the determination of parabens in such products is of particular importance. This study explored vortex-assisted dispersive liquid-liquid microextraction techniques based on the solidification of a floating organic drop (VA-DLLME-SFO) and salt-assisted cloud point extraction (SA-CPE) for paraben extraction. Microanalysis was performed using a capillary liquid chromatography-ultraviolet detection system. These techniques were modified successfully to determine four parabens in 19 commercial products. The regression equations of these parabens exhibited good linearity (r 2 =0.998, 0.1-10μg/mL), good precision (RSD<5%) and accuracy (RE<5%), reduced reagent consumption and reaction times (<6min), and excellent sample versatility. VA-DLLME-SFO was also particularly convenient due to the use of a solidified extract. Thus, the VA-DLLME-SFO technique was better suited to the extraction of parabens from complex matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

AP-Cloud: Adaptive particle-in-cloud method for optimal solutions to Vlasov–Poisson equation

DOE PAGES

Wang, Xingyu; Samulyak, Roman; Jiao, Xiangmin; ...

2016-04-19

We propose a new adaptive Particle-in-Cloud (AP-Cloud) method for obtaining optimal numerical solutions to the Vlasov–Poisson equation. Unlike the traditional particle-in-cell (PIC) method, which is commonly used for solving this problem, the AP-Cloud adaptively selects computational nodes or particles to deliver higher accuracy and efficiency when the particle distribution is highly non-uniform. Unlike other adaptive techniques for PIC, our method balances the errors in PDE discretization and Monte Carlo integration, and discretizes the differential operators using a generalized finite difference (GFD) method based on a weighted least square formulation. As a result, AP-Cloud is independent of the geometric shapes ofmore » computational domains and is free of artificial parameters. Efficient and robust implementation is achieved through an octree data structure with 2:1 balance. We analyze the accuracy and convergence order of AP-Cloud theoretically, and verify the method using an electrostatic problem of a particle beam with halo. Here, simulation results show that the AP-Cloud method is substantially more accurate and faster than the traditional PIC, and it is free of artificial forces that are typical for some adaptive PIC techniques.« less

AP-Cloud: Adaptive Particle-in-Cloud method for optimal solutions to Vlasov–Poisson equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Xingyu; Samulyak, Roman, E-mail: roman.samulyak@stonybrook.edu; Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973

We propose a new adaptive Particle-in-Cloud (AP-Cloud) method for obtaining optimal numerical solutions to the Vlasov–Poisson equation. Unlike the traditional particle-in-cell (PIC) method, which is commonly used for solving this problem, the AP-Cloud adaptively selects computational nodes or particles to deliver higher accuracy and efficiency when the particle distribution is highly non-uniform. Unlike other adaptive techniques for PIC, our method balances the errors in PDE discretization and Monte Carlo integration, and discretizes the differential operators using a generalized finite difference (GFD) method based on a weighted least square formulation. As a result, AP-Cloud is independent of the geometric shapes ofmore » computational domains and is free of artificial parameters. Efficient and robust implementation is achieved through an octree data structure with 2:1 balance. We analyze the accuracy and convergence order of AP-Cloud theoretically, and verify the method using an electrostatic problem of a particle beam with halo. Simulation results show that the AP-Cloud method is substantially more accurate and faster than the traditional PIC, and it is free of artificial forces that are typical for some adaptive PIC techniques.« less

AP-Cloud: Adaptive particle-in-cloud method for optimal solutions to Vlasov–Poisson equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Xingyu; Samulyak, Roman; Jiao, Xiangmin

We propose a new adaptive Particle-in-Cloud (AP-Cloud) method for obtaining optimal numerical solutions to the Vlasov–Poisson equation. Unlike the traditional particle-in-cell (PIC) method, which is commonly used for solving this problem, the AP-Cloud adaptively selects computational nodes or particles to deliver higher accuracy and efficiency when the particle distribution is highly non-uniform. Unlike other adaptive techniques for PIC, our method balances the errors in PDE discretization and Monte Carlo integration, and discretizes the differential operators using a generalized finite difference (GFD) method based on a weighted least square formulation. As a result, AP-Cloud is independent of the geometric shapes ofmore » computational domains and is free of artificial parameters. Efficient and robust implementation is achieved through an octree data structure with 2:1 balance. We analyze the accuracy and convergence order of AP-Cloud theoretically, and verify the method using an electrostatic problem of a particle beam with halo. Here, simulation results show that the AP-Cloud method is substantially more accurate and faster than the traditional PIC, and it is free of artificial forces that are typical for some adaptive PIC techniques.« less

Assessment of Cloud Screening with Apparent Surface Reflectance in Support of the ICESat-2 Mission

NASA Technical Reports Server (NTRS)

Yang, Yuekui; Marshak, Alexander; Palm, Stephen P.; Wang, Zhuosen; Schaaf, Crystal

2011-01-01

The separation of cloud and clear scenes is usually one of the first steps in satellite data analysis. Before deriving a geophysical product, almost every satellite mission requires a cloud mask to label a scene as either clear or cloudy through a cloud detection procedure. For clear scenes, products such as surface properties may be retrieved; for cloudy scenes, scientist can focus on studying the cloud properties. Hence the quality of cloud detection directly affects the quality of most satellite operational and research products. This is certainly true for the Ice, Cloud, and land Elevation Satellite-2 (lCESat-2), which is the successor to the ICESat-l. As a top priority mission, ICESat-2 will continue to provide measurements of ice sheets and sea ice elevation on a global scale. Studies have shown that clouds can significantly affect the accuracy of the retrieved results. For example, some of the photons (a photon is a basic unit of light) in the laser beam will be scattered by cloud particles on its way. So instead of traveling in a straight line, these photons are scattered sideways and have traveled a longer path. This will result in biases in ice sheet elevation measurements. Hence cloud screening must be done and be done accurately before the retrievals.

Mash-up of techniques between data crawling/transfer, data preservation/stewardship and data processing/visualization technologies on a science cloud system designed for Earth and space science: a report of successful operation and science projects of the NICT Science Cloud

NASA Astrophysics Data System (ADS)

Murata, K. T.

2014-12-01

Data-intensive or data-centric science is 4th paradigm after observational and/or experimental science (1st paradigm), theoretical science (2nd paradigm) and numerical science (3rd paradigm). Science cloud is an infrastructure for 4th science methodology. The NICT science cloud is designed for big data sciences of Earth, space and other sciences based on modern informatics and information technologies [1]. Data flow on the cloud is through the following three techniques; (1) data crawling and transfer, (2) data preservation and stewardship, and (3) data processing and visualization. Original tools and applications of these techniques have been designed and implemented. We mash up these tools and applications on the NICT Science Cloud to build up customized systems for each project. In this paper, we discuss science data processing through these three steps. For big data science, data file deployment on a distributed storage system should be well designed in order to save storage cost and transfer time. We developed a high-bandwidth virtual remote storage system (HbVRS) and data crawling tool, NICTY/DLA and Wide-area Observation Network Monitoring (WONM) system, respectively. Data files are saved on the cloud storage system according to both data preservation policy and data processing plan. The storage system is developed via distributed file system middle-ware (Gfarm: GRID datafarm). It is effective since disaster recovery (DR) and parallel data processing are carried out simultaneously without moving these big data from storage to storage. Data files are managed on our Web application, WSDBank (World Science Data Bank). The big-data on the cloud are processed via Pwrake, which is a workflow tool with high-bandwidth of I/O. There are several visualization tools on the cloud; VirtualAurora for magnetosphere and ionosphere, VDVGE for google Earth, STICKER for urban environment data and STARStouch for multi-disciplinary data. There are 30 projects running on the NICT Science Cloud for Earth and space science. In 2003 56 refereed papers were published. At the end, we introduce a couple of successful results of Earth and space sciences using these three techniques carried out on the NICT Sciences Cloud. [1] http://sc-web.nict.go.jp

Analysis of Co-Located MODIS and CALIPSO Observations Near Clouds

NASA Technical Reports Server (NTRS)

Varnai, Tamas; Marshak, Alexander

2011-01-01

The purpose of this paper is to help researchers combine data from different satellites and thus gain new insights into two critical yet poorly understood aspects of anthropogenic climate change, aerosol-cloud interactions and aerosol radiative effects, For this, the paper explores whether cloud information from the Aqua satellite's MODIS instrument can help characterize systematic aerosol changes near clouds by refining earlier perceptions of these changes that were based on the CALIPSO satellite's CALIOP instrument. Similar to a radar but using visible and ncar-infrared light, CALIOP sends out laser pulses and provides aerosol and cloud information along a single line that tracks the satellite orbit by measuring the reflection of its pulses. In contrast, MODIS takes images of reflected sunlight and emitted infrared radiation at several wavelengths, and covers wide areas around the satellite track. This paper analyzes a year-long global dataset covering all ice-free oceans, and finds that MODIS can greatly help the interpretation of CALIOP observations, especially by detecting clouds that lie outside the line observed by CALlPSO. The paper also finds that complications such as differences in view direction or clouds drifting in the 72 seconds that elapse between MODIS and CALIOP observations have only a minor impact. The study also finds that MODIS data helps refine but does not qualitatively alter perceptions of the systematic aerosol changes that were detected in earlier studies using only CALIOP data. It then proposes a statistical approach to account for clouds lying outside the CALIOP track even when MODIS cannot as reliably detect low clouds, for example at night or over ice. Finally, the paper finds that, because of variations in cloud amount and type, the typical distance to clouds in maritime clear areas varies with season and location. The overall median distance to clouds in maritime clear areas around 4-5 km. The fact that half of all clear areas is closer than 5 km to clouds implies that pronounced near-cloud changes in aerosol properties have significant implications for overall clear-sky characteristics, including the radiative impact of aerosols.

Above-Cloud Precipitable Water Retrievals using the MODIS 0.94 micron Band with Applications for Multi-Layer Cloud Detection

NASA Technical Reports Server (NTRS)

Platnick, S.; Wind, G.

2004-01-01

In order to perform satellite retrievals of cloud properties, it is important to account for the effect of the above-cloud atmosphere on the observations. The solar bands used in the operational MODIS Terra and Aqua cloud optical and microphysical algorithms (visible, NIR, and SWIR spectral windows) are primarily affected by water vapor, and to a lesser extent by well-mixed gases. For water vapor, the above-cloud column amount, or precipitable water, provides adequate information for an atmospheric correction; details of the vertical vapor distribution are not typically necessary for the level of correction required. Cloud-top pressure has a secondary effect due to pressure broadening influences. For well- mixed gases, cloud-top pressure is also required for estimates of above-cloud abundances. We present a method for obtaining above-cloud precipitable water over dark Ocean surfaces using the MODIS 0.94 pm vapor absorption band. The retrieval includes an iterative procedure for establishing cloud-top temperature and pressure, and is useful for both single layer water and ice clouds. Knowledge of cloud thermodynamic phase is fundamental in retrieving cloud optical and microphysical properties. However, in cases of optically thin cirrus overlapping lower water clouds, the concept of a single unique phase is ill- defined and depends, at least, on the spectral region of interest. We will present a method for multi-layer and multi-phase cloud detection which uses above-cloud precipitable water retrievals along with several existing MODIS operational cloud products (cloud-top pressure derived from a C02 slicing algorithm, IR and SWIR phase retrievals). Results are catagorized by whether the radiative signature in the MODIS solar bands is primarily that of a water cloud with ice cloud contamination, or visa-versa. Examples in polar and mid-latitude regions will be shown.

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

NASA Technical Reports Server (NTRS)

Hilker, Thomas; Lyapustin, Alexei I.; Tucker, Compton J.; Sellers, Piers J.; Hall, Forrest G.; Wang, Yujie

2012-01-01

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

Efficient operating system level virtualization techniques for cloud resources

NASA Astrophysics Data System (ADS)

Ansu, R.; Samiksha; Anju, S.; Singh, K. John

2017-11-01

Cloud computing is an advancing technology which provides the servcies of Infrastructure, Platform and Software. Virtualization and Computer utility are the keys of Cloud computing. The numbers of cloud users are increasing day by day. So it is the need of the hour to make resources available on demand to satisfy user requirements. The technique in which resources namely storage, processing power, memory and network or I/O are abstracted is known as Virtualization. For executing the operating systems various virtualization techniques are available. They are: Full System Virtualization and Para Virtualization. In Full Virtualization, the whole architecture of hardware is duplicated virtually. No modifications are required in Guest OS as the OS deals with the VM hypervisor directly. In Para Virtualization, modifications of OS is required to run in parallel with other OS. For the Guest OS to access the hardware, the host OS must provide a Virtual Machine Interface. OS virtualization has many advantages such as migrating applications transparently, consolidation of server, online maintenance of OS and providing security. This paper briefs both the virtualization techniques and discusses the issues in OS level virtualization.

Water Ice Clouds in the Martian Atmosphere: A View from MGS TES

NASA Technical Reports Server (NTRS)

Hale, A. S.; Tamppari, L. K.; Christensen, P. R.; Smith, M. D.; Bass, Deborah; Qu, Zheng; Pearl, J. C.

2005-01-01

We use the method of Tamppari et al. to map water ice clouds in the Martian atmosphere. This technique was originally developed to analyze the broadband Viking IRTM channels and we have now applied it to the TES data. To do this, the TES spectra are convolved to the IRTM bandshapes and spatial resolutions, enabling use of the same processing techniques as were used in Tamppari et al.. This retrieval technique relies on using the temperature difference recorded in the 20 micron and 11 micron IRTM bands (or IRTM convolved TES bands) to map cold water ice clouds above the warmer Martian surface. Careful removal of surface contributions to the observed radiance is therefore necessary, and we have used both older Viking-derived basemaps of the surface emissivity and albedo, and new MGS derived basemaps in order the explore any possible differences on cloud retrieval due to differences in surface contribution removal. These results will be presented in our poster. Our previous work has concentrated primarily on comparing MGS TES to Viking data; that work saw that large-scale cloud features, such as the aphelion cloud belt, are quite repeatable from year to year, though small scale behavior shows some variation. Comparison of Viking and MGS era cloud maps will be presented in our poster. In the current stage of our study, we have concentrated our efforts on close analysis of water ice cloud behavior in the northern summer of the three MGS mapping years on relatively small spatial scales, and present our results below. Additional information is included in the original extended abstract.

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction

PubMed Central

Berveglieri, Adilson; Liang, Xinlian; Honkavaara, Eija

2017-01-01

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras. PMID:29207468

Vertical Optical Scanning with Panoramic Vision for Tree Trunk Reconstruction.

PubMed

Berveglieri, Adilson; Tommaselli, Antonio M G; Liang, Xinlian; Honkavaara, Eija

2017-12-02

This paper presents a practical application of a technique that uses a vertical optical flow with a fisheye camera to generate dense point clouds from a single planimetric station. Accurate data can be extracted to enable the measurement of tree trunks or branches. The images that are collected with this technique can be oriented in photogrammetric software (using fisheye models) and used to generate dense point clouds, provided that some constraints on the camera positions are adopted. A set of images was captured in a forest plot in the experiments. Weighted geometric constraints were imposed in the photogrammetric software to calculate the image orientation, perform dense image matching, and accurately generate a 3D point cloud. The tree trunks in the scenes were reconstructed and mapped in a local reference system. The accuracy assessment was based on differences between measured and estimated trunk diameters at different heights. Trunk sections from an image-based point cloud were also compared to the corresponding sections that were extracted from a dense terrestrial laser scanning (TLS) point cloud. Cylindrical fitting of the trunk sections allowed the assessment of the accuracies of the trunk geometric shapes in both clouds. The average difference between the cylinders that were fitted to the photogrammetric cloud and those to the TLS cloud was less than 1 cm, which indicates the potential of the proposed technique. The point densities that were obtained with vertical optical scanning were 1/3 less than those that were obtained with TLS. However, the point density can be improved by using higher resolution cameras.

Examining the Impact of Overlying Aerosols on the Retrieval of Cloud Optical Properties from Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Coddington, O. M.; Pilewskie, P.; Redemann, J.; Platnick, S.; Russell, P. B.; Schmidt, K. S.; Gore, W. J.; Livingston, J.; Wind, G.; Vukicevic, T.

2010-01-01

Haywood et al. (2004) show that an aerosol layer above a cloud can cause a bias in the retrieved cloud optical thickness and effective radius. Monitoring for this potential bias is difficult because space ]based passive remote sensing cannot unambiguously detect or characterize aerosol above cloud. We show that cloud retrievals from aircraft measurements above cloud and below an overlying aerosol layer are a means to test this bias. The data were collected during the Intercontinental Chemical Transport Experiment (INTEX-A) study based out of Portsmouth, New Hampshire, United States, above extensive, marine stratus cloud banks affected by industrial outflow. Solar Spectral Flux Radiometer (SSFR) irradiance measurements taken along a lower level flight leg above cloud and below aerosol were unaffected by the overlying aerosol. Along upper level flight legs, the irradiance reflected from cloud top was transmitted through an aerosol layer. We compare SSFR cloud retrievals from below ]aerosol legs to satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to detect an aerosol ]induced bias. In regions of small variation in cloud properties, we find that SSFR and MODIS-retrieved cloud optical thickness compares within the uncertainty range for each instrument while SSFR effective radius tend to be smaller than MODIS values (by 1-2 microns) and at the low end of MODIS uncertainty estimates. In regions of large variation in cloud properties, differences in SSFR and MODIS ]retrieved cloud optical thickness and effective radius can reach values of 10 and 10 microns, respectively. We include aerosols in forward modeling to test the sensitivity of SSFR cloud retrievals to overlying aerosol layers. We find an overlying absorbing aerosol layer biases SSFR cloud retrievals to smaller effective radii and optical thickness while nonabsorbing aerosols had no impact.

Detecting tree-like multicellular life on extrasolar planets.

PubMed

Doughty, Christopher E; Wolf, Adam

2010-11-01

Over the next two decades, NASA and ESA are planning a series of space-based observatories to find Earth-like planets and determine whether life exists on these planets. Previous studies have assessed the likelihood of detecting life through signs of biogenic gases in the atmosphere or a red edge. Biogenic gases and the red edge could be signs of either single-celled or multicellular life. In this study, we propose a technique with which to determine whether tree-like multicellular life exists on extrasolar planets. For multicellular photosynthetic organisms on Earth, competition for light and the need to transport water and nutrients has led to a tree-like body plan characterized by hierarchical branching networks. This design results in a distinct bidirectional reflectance distribution function (BRDF) that causes differing reflectance at different sun/view geometries. BRDF arises from the changing visibility of the shadows cast by objects, and the presence of tree-like structures is clearly distinguishable from flat ground with the same reflectance spectrum. We examined whether the BRDF could detect the existence of tree-like structures on an extrasolar planet by using changes in planetary albedo as a planet orbits its star. We used a semi-empirical BRDF model to simulate vegetation reflectance at different planetary phase angles and both simulated and real cloud cover to calculate disk and rotation-averaged planetary albedo for a vegetated and non-vegetated planet with abundant liquid water. We found that even if the entire planetary albedo were rendered to a single pixel, the rate of increase of albedo as a planet approaches full illumination would be comparatively greater on a vegetated planet than on a non-vegetated planet. Depending on how accurately planetary cloud cover can be resolved and the capabilities of the coronagraph to resolve exoplanets, this technique could theoretically detect tree-like multicellular life on exoplanets in 50 stellar systems.

Strategies for cloud-top phase determination: differentiation between thin cirrus clouds and snow in manual (ground truth) analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Etherton, Brian J.; Topping, Phillip C.

1996-12-01

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create ground truth analyses for the evaluation of cloud detection algorithms is relatively straightforward. However, when focus shifts toward quantifying the performance of automated cloud classification algorithms, the task of creating ground truth images becomes much more complicated since these CNC analyses must differentiate between water and ice cloud tops while ensuring that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. The process of creating these ground truth CNC analyses may become particularly difficult when little or no spectral signature is evident between a cloud and its background, as appears to be the case when thin cirrus is present over snow-covered surfaces. In this paper, procedures are described that enhance the researcher's ability to manually interpret and differentiate between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery. The methodology uses data in up to six AVHRR spectral bands, including an additional band derived from the daytime 3.7 micron channel, which has proven invaluable for the manual discrimination between thin cirrus clouds and snow. It is concluded that while the 1.6 micron channel remains essential to differentiate between thin ice clouds and snow. However, this capability that may be lost if the 3.7 micron data switches to a nighttime-only transmission with the launch of future NOAA satellites.

Nanobubbles at Hydrophilic Particle-Water Interfaces.

PubMed

Pan, Gang; He, Guangzhi; Zhang, Meiyi; Zhou, Qin; Tyliszczak, Tolek; Tai, Renzhong; Guo, Jinghua; Bi, Lei; Wang, Lei; Zhang, Honggang

2016-11-01

The puzzling persistence of nanobubbles breaks Laplace's law for bubbles, which is of great interest for promising applications in surface processing, H 2 and CO 2 storage, water treatment, and drug delivery. So far, nanobubbles have mostly been reported on hydrophobic planar substrates with atomic flatness. It remains a challenge to quantify nanobubbles on rough and irregular surfaces because of the lack of a characterization technique that can detect both the nanobubble morphology and chemical composition inside individual nanobubble-like objects. Here, by using synchrotron-based scanning transmission soft X-ray microscopy (STXM) with nanometer resolution, we discern nanoscopic gas bubbles of >25 nm with direct in situ proof of O 2 inside the nanobubbles at a hydrophilic particle-water interface under ambient conditions. We find a stable cloud of O 2 nanobubbles at the diatomite particle-water interface hours after oxygen aeration and temperature variation. The in situ technique may be useful for many surface nanobubble-related studies such as material preparation and property manipulation, phase equilibrium, nucleation kinetics, and relationships with chemical composition within the confined nanoscale space. The oxygen nanobubble clouds may be important in modifying particle-water interfaces and offering breakthrough technologies for oxygen delivery in sediment and/or deep water environments.

Photogrammetric Analysis of Rotor Clouds Observed during T-REX

NASA Astrophysics Data System (ADS)

Romatschke, U.; Grubišić, V.

2017-12-01

Stereo photogrammetric analysis is a rarely utilized but highly valuable tool for studying smaller, highly ephemeral clouds. In this study, we make use of data that was collected during the Terrain-induced Rotor Experiment (T-REX), which took place in Owens Valley, eastern California, in the spring of 2006. The data set consists of matched digital stereo photographs obtained at high temporal (on the order of seconds) and spatial resolution (limited by the pixel size of the cameras). Using computer vision techniques we have been able to develop algorithms for camera calibration, automatic feature matching, and ultimately reconstruction of 3D cloud scenes. Applying these techniques to images from different T-REX IOPs we capture the motion of clouds in several distinct mountain wave scenarios ranging from short lived lee wave clouds on an otherwise clear sky day to rotor clouds formed in an extreme turbulence environment with strong winds and high cloud coverage. Tracking the clouds in 3D space and time allows us to quantify phenomena such as vertical and horizontal movement of clouds, turbulent motion at the upstream edge of rotor clouds, the structure of the lifting condensation level, extreme wind shear, and the life cycle of clouds in lee waves. When placed into context with the existing literature that originated from the T-REX field campaign, our results complement and expand our understanding of the complex dynamics observed in a variety of different lee wave settings.

Self-Similar Spin Images for Point Cloud Matching

NASA Astrophysics Data System (ADS)

Pulido, Daniel

The rapid growth of Light Detection And Ranging (Lidar) technologies that collect, process, and disseminate 3D point clouds have allowed for increasingly accurate spatial modeling and analysis of the real world. Lidar sensors can generate massive 3D point clouds of a collection area that provide highly detailed spatial and radiometric information. However, a Lidar collection can be expensive and time consuming. Simultaneously, the growth of crowdsourced Web 2.0 data (e.g., Flickr, OpenStreetMap) have provided researchers with a wealth of freely available data sources that cover a variety of geographic areas. Crowdsourced data can be of varying quality and density. In addition, since it is typically not collected as part of a dedicated experiment but rather volunteered, when and where the data is collected is arbitrary. The integration of these two sources of geoinformation can provide researchers the ability to generate products and derive intelligence that mitigate their respective disadvantages and combine their advantages. Therefore, this research will address the problem of fusing two point clouds from potentially different sources. Specifically, we will consider two problems: scale matching and feature matching. Scale matching consists of computing feature metrics of each point cloud and analyzing their distributions to determine scale differences. Feature matching consists of defining local descriptors that are invariant to common dataset distortions (e.g., rotation and translation). Additionally, after matching the point clouds they can be registered and processed further (e.g., change detection). The objective of this research is to develop novel methods to fuse and enhance two point clouds from potentially disparate sources (e.g., Lidar and crowdsourced Web 2.0 datasets). The scope of this research is to investigate both scale and feature matching between two point clouds. The specific focus of this research will be in developing a novel local descriptor based on the concept of self-similarity to aid in the scale and feature matching steps. An open problem in fusion is how best to extract features from two point clouds and then perform feature-based matching. The proposed approach for this matching step is the use of local self-similarity as an invariant measure to match features. In particular, the proposed approach is to combine the concept of local self-similarity with a well-known feature descriptor, Spin Images, and thereby define "Self-Similar Spin Images". This approach is then extended to the case of matching two points clouds in very different coordinate systems (e.g., a geo-referenced Lidar point cloud and stereo-image derived point cloud without geo-referencing). The use of Self-Similar Spin Images is again applied to address this problem by introducing a "Self-Similar Keyscale" that matches the spatial scales of two point clouds. Another open problem is how best to detect changes in content between two point clouds. A method is proposed to find changes between two point clouds by analyzing the order statistics of the nearest neighbors between the two clouds, and thereby define the "Nearest Neighbor Order Statistic" method. Note that the well-known Hausdorff distance is a special case as being just the maximum order statistic. Therefore, by studying the entire histogram of these nearest neighbors it is expected to yield a more robust method to detect points that are present in one cloud but not the other. This approach is applied at multiple resolutions. Therefore, changes detected at the coarsest level will yield large missing targets and at finer levels will yield smaller targets.

An AVHRR Cloud Classification Database Typed by Experts

DTIC Science & Technology

1993-10-01

analysis. Naval Research Laboratory, Monterey, CA. 110 pp. Gallaudet , Timothy C. and James J. Simpson, 1991: Automated cloud screening of AVHRR imagery...1987) and Saunders and Kriebel (1988a,b) have used threshold techniques to classify clouds. Gallaudet and Simpson (1991) have used split-and-merge

Measurements of the Ice Water Content of Cirrus in the Tropics and Subtropics. I; Instrument Details and Validation

NASA Technical Reports Server (NTRS)

Weinstock, E. M.; Smith, J. B.; Sayres, D.; Pittman, J. V.; Allen, N.; Demusz, J.; Greenberg, M.; Rivero, M.; Anderson, J. G.

2003-01-01

We describe an instrument mounted in a pallet on the NASA WB-57 aircraft that is designed to measure the sum of gas phase and solid phase water, or total water, in cirrus clouds. Using an isokinetic inlet, a 600-watt heater mounted directly in the flow, and Lyman-alpha photofragment fluorescence technique for detection, accurate measurements of total water have been made over almost three orders of magnitude. Isokinetic flow is achieved with an actively controlled roots pump by referencing aircraft pressure, temperature, and true air speed, together with instrument flow velocity, temperature, and pressure. During CRYSTAL FACE, the instrument operated at duct temperatures sufficiently warm to completely evaporate particles up to 150 microns diameter. In flight diagnostics, intercomparison with water measured by absorption in flight, as well as intercomparisons in clear air with water vapor measured by the Harvard water vapor instrument and the JPL infrared tunable diode laser hygrometer validate the detection sensitivity of the instrument and illustrate minimal hysteresis from instrument surfaces. The simultaneous measurement of total water and water vapor in cirrus clouds yields their ice water content.

Detection of carbon monoxide pollution from cities and wildfires on regional and urban scales: the benefit of CO column retrievals from SCIAMACHY 2.3 µm measurements under cloudy conditions

NASA Astrophysics Data System (ADS)

Borsdorff, Tobias; Andrasec, Josip; aan de Brugh, Joost; Hu, Haili; Aben, Ilse; Landgraf, Jochen

2018-05-01

In the perspective of the upcoming TROPOMI Sentinel-5 Precursor carbon monoxide data product, we discuss the benefit of using CO total column retrievals from cloud-contaminated SCIAMACHY 2.3 µm shortwave infrared spectra to detect atmospheric CO enhancements on regional and urban scales due to emissions from cities and wildfires. The study uses the operational Sentinel-5 Precursor algorithm SICOR, which infers the vertically integrated CO column together with effective cloud parameters. We investigate its capability to detect localized CO enhancements distinguishing between clear-sky observations and observations with low (< 1.5 km) and medium-high clouds (1.5-5 km). As an example, we analyse CO enhancements over the cities Paris, Los Angeles and Tehran as well as the wildfire events in Mexico-Guatemala 2005 and Alaska-Canada 2004. The CO average of the SCIAMACHY full-mission data set of clear-sky observations can detect weak CO enhancements of less than 10 ppb due to air pollution in these cities. For low-cloud conditions, the CO data product performs similarly well. For medium-high clouds, the observations show a reduced CO signal both over Tehran and Los Angeles, while for Paris no significant CO enhancement can be detected. This indicates that information about the vertical distribution of CO can be obtained from the SCIAMACHY measurements. Moreover, for the Mexico-Guatemala fires, the low-cloud CO data captures a strong outflow of CO over the Gulf of Mexico and the Pacific Ocean and so provides complementary information to clear-sky retrievals, which can only be obtained over land. For both burning events, enhanced CO values are even detectable with medium-high-cloud retrievals, confirming a distinct vertical extension of the pollution. The larger number of additional measurements, and hence the better spatial coverage, significantly improve the detection of wildfire pollution using both the clear-sky and cloudy CO retrievals. Due to the improved instrument performance of the TROPOMI instrument with respect to its precursor SCIAMACHY, the upcoming Sentinel-5 Precursor CO data product will allow improved detection of CO emissions and their vertical extension over cities and fires, making new research applications possible.

Sound Is Sound: Film Sound Techniques and Infrasound Data Array Processing

NASA Astrophysics Data System (ADS)

Perttu, A. B.; Williams, R.; Taisne, B.; Tailpied, D.

2017-12-01

A multidisciplinary collaboration between earth scientists and a sound designer/composer was established to explore the possibilities of audification analysis of infrasound array data. Through the process of audification of the infrasound we began to experiment with techniques and processes borrowed from cinema to manipulate the noise content of the signal. The results of this posed the question: "Would the accuracy of infrasound data array processing be enhanced by employing these techniques?". So a new area of research was born from this collaboration and highlights the value of these interactions and the unintended paths that can occur from them. Using a reference event database, infrasound data were processed using these new techniques and the results were compared with existing techniques to asses if there was any improvement to detection capability for the array. With just under one thousand volcanoes, and a high probability of eruption, Southeast Asia offers a unique opportunity to develop and test techniques for regional monitoring of volcanoes with different technologies. While these volcanoes are monitored locally (e.g. seismometer, infrasound, geodetic and geochemistry networks) and remotely (e.g. satellite and infrasound), there are challenges and limitations to the current monitoring capability. Not only is there a high fraction of cloud cover in the region, making plume observation more difficult via satellite, there have been examples of local monitoring networks and telemetry being destroyed early in the eruptive sequence. The success of local infrasound studies to identify explosions at volcanoes, and calculate plume heights from these signals, has led to an interest in retrieving source parameters for the purpose of ash modeling with a regional network independent of cloud cover.

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

Position and volume estimation of atmospheric nuclear detonations from video reconstruction

NASA Astrophysics Data System (ADS)

Schmitt, Daniel T.

Recent work in digitizing films of foundational atmospheric nuclear detonations from the 1950s provides an opportunity to perform deeper analysis on these historical tests. This work leverages multi-view geometry and computer vision techniques to provide an automated means to perform three-dimensional analysis of the blasts for several points in time. The accomplishment of this requires careful alignment of the films in time, detection of features in the images, matching of features, and multi-view reconstruction. Sub-explosion features can be detected with a 67% hit rate and 22% false alarm rate. Hotspot features can be detected with a 71.95% hit rate, 86.03% precision and a 0.015% false positive rate. Detected hotspots are matched across 57-109 degree viewpoints with 76.63% average correct matching by defining their location relative to the center of the explosion, rotating them to the alternative viewpoint, and matching them collectively. When 3D reconstruction is applied to the hotspot matching it completes an automated process that has been used to create 168 3D point clouds with 31.6 points per reconstruction with each point having an accuracy of 0.62 meters with 0.35, 0.24, and 0.34 meters of accuracy in the x-, y- and z-direction respectively. As a demonstration of using the point clouds for analysis, volumes are estimated and shown to be consistent with radius-based models and in some cases improve on the level of uncertainty in the yield calculation.

Intelligent cloud computing security using genetic algorithm as a computational tools

NASA Astrophysics Data System (ADS)

Razuky AL-Shaikhly, Mazin H.

2018-05-01

An essential change had occurred in the field of Information Technology which represented with cloud computing, cloud giving virtual assets by means of web yet awesome difficulties in the field of information security and security assurance. Currently main problem with cloud computing is how to improve privacy and security for cloud “cloud is critical security”. This paper attempts to solve cloud security by using intelligent system with genetic algorithm as wall to provide cloud data secure, all services provided by cloud must detect who receive and register it to create list of users (trusted or un-trusted) depend on behavior. The execution of present proposal has shown great outcome.

Evaluation of terrestrial photogrammetric point clouds derived from thermal imagery

NASA Astrophysics Data System (ADS)

Metcalf, Jeremy P.; Olsen, Richard C.

2016-05-01

Computer vision and photogrammetric techniques have been widely applied to digital imagery producing high density 3D point clouds. Using thermal imagery as input, the same techniques can be applied to infrared data to produce point clouds in 3D space, providing surface temperature information. The work presented here is an evaluation of the accuracy of 3D reconstruction of point clouds produced using thermal imagery. An urban scene was imaged over an area at the Naval Postgraduate School, Monterey, CA, viewing from above as with an airborne system. Terrestrial thermal and RGB imagery were collected from a rooftop overlooking the site using a FLIR SC8200 MWIR camera and a Canon T1i DSLR. In order to spatially align each dataset, ground control points were placed throughout the study area using Trimble R10 GNSS receivers operating in RTK mode. Each image dataset is processed to produce a dense point cloud for 3D evaluation.

Lidar Cloud Detection with Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Cromwell, E.; Flynn, D.

2017-12-01

The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

Optical remote sensing of properties and concentrations of atmospheric trace constituents

NASA Astrophysics Data System (ADS)

Vladutescu, Daniela Viviana

The effect of human activities on the global climate may lead to large disturbances of the economic, social and political circumstances in the middle and long term. Understanding the dynamics of the Earth's climate is therefore of high importance and one of the major scientific challenges of our time. The estimation of the contribution of the Earth's climate system components needs observation and continuous monitoring of various atmospheric physical and chemical parameters. Temperature, water vapor and greenhouse gases concentration, aerosol and clouds loads, and atmospheric dynamics are parameters of particular importance in this respect. The quantification of the anthropogenic influence on the dynamics of these above-mentioned parameters is of crucial importance nowadays but still affected by significant uncertainties. In the present context of these huge uncertainties in our understanding of how these different atmospheric compounds contribute to the radiative forcing, a significant part of my research interest is related to the following topics: (1) Development of lidar (Light Detection and Ranging)-based remote sensing techniques for monitoring atmospheric compounds and processes; (2) Aerosols hygroscopic properties and atmospheric modeling; (3) Water vapor mixing ratio and relative humidity estimation in the troposphere; (4) Characterization of the long-range transported aerosols; (5) Ambient gases detection using Fourier Transform Interferometers (FTIR); (6) Design of inexpensive Fabry Perot Interferometer for visible and near infrared for land and ocean surface remote sensing applications. The lidar-based remote sensing measurement techniques for the monitoring of climate change parameters where implemented at the City College of the City University of New York (CCNY/CUNY) LIDAR station and are presented in the second section of the paper. The geographical location of the CCNY lidar station is 40.86N, -73.86W. Among the lidar retrievals one important application is the detection of water vapor in the atmosphere. Water vapor is an important greenhouse gas due to its high concentration in the atmosphere (parts per thousand), among the trace constituents, and its interaction with tropospheric aerosols particles. The upward convection of water vapor and aerosols due to intense heating of the ground lead to aggregation of water particles or ice on aerosols in the air forming different types of clouds at various altitudes. In this regard a reliable method of retrieving atmospheric water vapor profiles is presented in the third part of the paper. The proposed technique here is the Raman lidar procedure that is calibrated afterwards. The accuracy of the water vapor measurements is obtained by calibration techniques based on different techniques that where compared and validated. The calibration method is based on data fusion from different sources like: GPS (global positioning system) sunphotometer, radiosonde. The condensation of water vapor on aerosols is affecting their size, shape, refractive index and chemical composition. The warming or cooling effect of the clouds hence formed are both possible depending on the cloud location, cover, composition and structure. The effect of these clouds on radiative global forcing and therefore on the short and long term global climate is of high interest in the scientific world. In an effort to understand the hygroscopic properties of aerosols, a major interest is manifested in obtaining accurate vertical water vapor profiles simultaneously with aerosol extinction and backscatter profiles. A reliable method of retrieving atmospheric water vapor profiles and aerosols backscatter and extinction in the same atmospheric volume is presented in the fourth chapter of the paper. As mentioned above the determination of greenhouse gases and other molecular pollutants is important in process control as well as environmental monitoring. Since many molecular vibrational modes are in the infrared, molecules can absorb light from an infrared source (such as the sun or an artificial source such as a glow rod) and therefore, if the source spectrum is known, the absorption spectra of the sample can be measured. Therefore, any spectroscopy method needs a well characterized infrared source as well as an accurate high resolution spectrometer. In the fifth chapter of the paper is presented a standard technique for open-path detection of greenhouse gases which is based on Fourier Transform Infrared Spectroscopy (FTIR). A MIDAC open path FTIR instrument is presented along with measurements and analyses. In the group of spectrometers with a high spatial spectral resolution is found as well the Fabry Perot Interferometer that is presented in chapter 6. A visible-near infrared (VIS-NIR) scanning Fabry Perot Imager design is proposed based on combinations of Fabry Perot etalons and/or broadband interference filters that can in principle be used as a hyperspectral sensors from geostationary spaceborne platforms. Keywords. Lidar, Raman, Mie, water vapor mixing ratio, backscatter, extinction, relative humidity, aerosol hygroscopic properties, atmospheric model, FTIR, FPI, green house gases

A Simple Technique for Securing Data at Rest Stored in a Computing Cloud

NASA Astrophysics Data System (ADS)

Sedayao, Jeff; Su, Steven; Ma, Xiaohao; Jiang, Minghao; Miao, Kai

"Cloud Computing" offers many potential benefits, including cost savings, the ability to deploy applications and services quickly, and the ease of scaling those application and services once they are deployed. A key barrier for enterprise adoption is the confidentiality of data stored on Cloud Computing Infrastructure. Our simple technique implemented with Open Source software solves this problem by using public key encryption to render stored data at rest unreadable by unauthorized personnel, including system administrators of the cloud computing service on which the data is stored. We validate our approach on a network measurement system implemented on PlanetLab. We then use it on a service where confidentiality is critical - a scanning application that validates external firewall implementations.

Radiotherapy Monte Carlo simulation using cloud computing technology.

PubMed

Poole, C M; Cornelius, I; Trapp, J V; Langton, C M

2012-12-01

Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1/n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal.

O2 A Band Studies for Cloud Detection and Algorithm Improvement

NASA Technical Reports Server (NTRS)

Chance, K. V.

1996-01-01

Detection of cloud parameters from space-based spectrometers can employ the vibrational bands of O2 in the (sup b1)Sigma(sub +)(sub g) yields X(sub 3) Sigma(sup -)(sub g) spin-forbidden electronic transition manifold, particularly the Delta nu = 0 A band. The GOME instrument uses the A band in the Initial Cloud Fitting Algorithm (ICFA). The work reported here consists of making substantial improvements in the line-by-line spectral database for the A band, testing whether an additional correction to the line shape function is necessary in order to correctly model the atmospheric transmission in this band, and calculating prototype cloud and ground template spectra for comparison with satellite measurements.

Application of cellular automata approach for cloud simulation and rendering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christopher Immanuel, W.; Paul Mary Deborrah, S.; Samuel Selvaraj, R.

Current techniques for creating clouds in games and other real time applications produce static, homogenous clouds. These clouds, while viable for real time applications, do not exhibit an organic feel that clouds in nature exhibit. These clouds, when viewed over a time period, were able to deform their initial shape and move in a more organic and dynamic way. With cloud shape technology we should be able in the future to extend to create even more cloud shapes in real time with more forces. Clouds are an essential part of any computer model of a landscape or an animation ofmore » an outdoor scene. A realistic animation of clouds is also important for creating scenes for flight simulators, movies, games, and other. Our goal was to create a realistic animation of clouds.« less

A Time-Frequency Analysis of the Effects of Solar Activities on Tropospheric Thermodynamics

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Kyle, H. Lee; Wharton, Stephen W. (Technical Monitor)

2001-01-01

Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth System under the external and internal forcings give out very complex signals.The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is a very useful technique for detecting, understanding and monitoring climate changes.

The NASA Icing Remote Sensing System

NASA Technical Reports Server (NTRS)

Reehorst, Andrew L.; Brinker, David J.; Ratvasky, Thomas P.; Ryerson, Charles C.; Koenig, George G.

2005-01-01

NASA and the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) have an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. A multiple instrument approach is the current emphasis of this activity. Utilizing radar, radiometry, and lidar, a region of supercooled liquid is identified. If the liquid water content (LWC) is sufficiently high, then the region of supercooled liquid cloud is flagged as being an aviation hazard. The instruments utilized for the current effort are an X-band vertical staring radar, a radiometer that measures twelve frequencies between 22 and 59 GHz, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data are post-processed with a LabVIEW program with a resultant supercooled LWC profile and aircraft hazard identification. Remotely sensed measurements gathered during the 2003-2004 Alliance Icing Research Study (AIRS II) were compared to aircraft in-situ measurements. Although the comparison data set is quite small, the cases examined indicate that the remote sensing technique appears to be an acceptable approach.

Discovery of a Bright Equatorial Storm on Neptune

NASA Astrophysics Data System (ADS)

Molter, E. M.; De Pater, I.; Alvarez, C.; Tollefson, J.; Luszcz-Cook, S.

2017-12-01

Images of Neptune, taken with the NIRC2 instrument during testing of the new Twilight Zone observing program at Keck Observatory, revealed an extremely large bright storm system near Neptune's equator. The storm complex is ≈9,000 km across and brightened considerably between June 26 and July 2. Historically, very bright clouds have occasionally been seen on Neptune, but always in the midlatitude regions between ≈15° and ≈60° North or South. Voyager and HST observations have shown that cloud features large enough to dominate near-IR photometry are often "companion" clouds of dark anti-cyclonic vortices similar to Jupiter's Great Red Spot, interpreted as orographic clouds. In the past such clouds and their coincident dark vortices often persisted for one up to several years. However, the cloud complex we detect is unique: never before has a bright cloud been seen at, or so close to, the equator. The discovery points to a drastic departure in the dynamics of Neptune's atmosphere from what has been observed for the past several decades. Detections of the complex in multiple NIRC2 filters allows radiative transfer modeling to constrain the cloud's altitude and vertical extent.

Aviation response to a widely dispersed volcanic ash and gas cloud from the August 2008 eruption of Kasatochi, Alaska, USA

USGS Publications Warehouse

Guffanti, Marianne; Schneider, David J.; Wallace, Kristi L.; Hall, Tony; Bensimon, Dov R.; Salinas, Leonard J.

2010-01-01

The extensive volcanic cloud from Kasatochi's 2008 eruption caused widespread disruptions to aviation operations along Pacific oceanic, Canadian, and U.S. air routes. Based on aviation hazard warnings issued by the National Oceanic and Atmospheric Administration, U.S. Geological Survey, the Federal Aviation Administration, and Meteorological Service of Canada, air carriers largely avoided the volcanic cloud over a 5 day period by route modifications and flight cancellations. Comparison of time coincident GOES thermal infrared (TIR) data for ash detection with Ozone Monitoring Instrument (OMI) ultraviolet data for SO2 detection shows congruent areas of ash and gas in the volcanic cloud in the 2 days following onset of ash production. After about 2.5 days, the area of SO2 detected by OMI was more extensive than the area of ash indicated by TIR data, indicating significant ash depletion by fall out had occurred. Pilot reports of visible haze at cruise altitudes over Canada and the northern United States suggested that SO2 gas had converted to sulfate aerosols. Uncertain about the hazard potential of the aging cloud, airlines coped by flying over, under, or around the observed haze layer. Samples from a nondamaging aircraft encounter with Kasatochi's nearly 3 day old cloud contained volcanic silicate particles, confirming that some fine ash is present in predominantly gas clouds. The aircraft's exposure to ash was insufficient to cause engine damage; however, slightly damaging encounters with volcanic clouds from eruptions of Reventador in 2002 and Hekla in 2000 indicate the possibility of lingering hazards associated with old and/or diffuse volcanic clouds.

Cloud Detection with the Earth Polychromatic Imaging Camera (EPIC)

NASA Technical Reports Server (NTRS)

Meyer, Kerry; Marshak, Alexander; Lyapustin, Alexei; Torres, Omar; Wang, Yugie

2011-01-01

The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) would provide a unique opportunity for Earth and atmospheric research due not only to its Lagrange point sun-synchronous orbit, but also to the potential for synergistic use of spectral channels in both the UV and visible spectrum. As a prerequisite for most applications, the ability to detect the presence of clouds in a given field of view, known as cloud masking, is of utmost importance. It serves to determine both the potential for cloud contamination in clear-sky applications (e.g., land surface products and aerosol retrievals) and clear-sky contamination in cloud applications (e.g., cloud height and property retrievals). To this end, a preliminary cloud mask algorithm has been developed for EPIC that applies thresholds to reflected UV and visible radiances, as well as to reflected radiance ratios. This algorithm has been tested with simulated EPIC radiances over both land and ocean scenes, with satisfactory results. These test results, as well as algorithm sensitivity to potential instrument uncertainties, will be presented.

Simulations of the observation of clouds and aerosols with the Experimental Lidar in Space Equipment system.

PubMed

Liu, Z; Voelger, P; Sugimoto, N

2000-06-20

We carried out a simulation study for the observation of clouds and aerosols with the Japanese Experimental Lidar in Space Equipment (ELISE), which is a two-wavelength backscatter lidar with three detection channels. The National Space Development Agency of Japan plans to launch the ELISE on the Mission Demonstrate Satellite 2 (MDS-2). In the simulations, the lidar return signals for the ELISE are calculated for an artificial, two-dimensional atmospheric model including different types of clouds and aerosols. The signal detection processes are simulated realistically by inclusion of various sources of noise. The lidar signals that are generated are then used as input for simulations of data analysis with inversion algorithms to investigate retrieval of the optical properties of clouds and aerosols. The results demonstrate that the ELISE can provide global data on the structures and optical properties of clouds and aerosols. We also conducted an analysis of the effects of cloud inhomogeneity on retrievals from averaged lidar profiles. We show that the effects are significant for space lidar observations of optically thick broken clouds.

Parameterizing Size Distribution in Ice Clouds

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeSlover, Daniel; Mitchell, David L.

2009-09-25

PARAMETERIZING SIZE DISTRIBUTIONS IN ICE CLOUDS David L. Mitchell and Daniel H. DeSlover ABSTRACT An outstanding problem that contributes considerable uncertainty to Global Climate Model (GCM) predictions of future climate is the characterization of ice particle sizes in cirrus clouds. Recent parameterizations of ice cloud effective diameter differ by a factor of three, which, for overcast conditions, often translate to changes in outgoing longwave radiation (OLR) of 55 W m-2 or more. Much of this uncertainty in cirrus particle sizes is related to the problem of ice particle shattering during in situ sampling of the ice particle size distribution (PSD).more » Ice particles often shatter into many smaller ice fragments upon collision with the rim of the probe inlet tube. These small ice artifacts are counted as real ice crystals, resulting in anomalously high concentrations of small ice crystals (D < 100 µm) and underestimates of the mean and effective size of the PSD. Half of the cirrus cloud optical depth calculated from these in situ measurements can be due to this shattering phenomenon. Another challenge is the determination of ice and liquid water amounts in mixed phase clouds. Mixed phase clouds in the Arctic contain mostly liquid water, and the presence of ice is important for determining their lifecycle. Colder high clouds between -20 and -36 oC may also be mixed phase but in this case their condensate is mostly ice with low levels of liquid water. Rather than affecting their lifecycle, the presence of liquid dramatically affects the cloud optical properties, which affects cloud-climate feedback processes in GCMs. This project has made advancements in solving both of these problems. Regarding the first problem, PSD in ice clouds are uncertain due to the inability to reliably measure the concentrations of the smallest crystals (D < 100 µm), known as the “small mode”. Rather than using in situ probe measurements aboard aircraft, we employed a treatment of ice cloud optical properties formulated in terms of PSD parameters in combination with remote measurements of thermal radiances to characterize the small mode. This is possible since the absorption efficiency (Qabs) of small mode crystals is larger at 12 µm wavelength relative to 11 µm wavelength due to the process of wave resonance or photon tunneling more active at 12 µm. This makes the 12/11 µm absorption optical depth ratio (or equivalently the 12/11 µm Qabs ratio) a means for detecting the relative concentration of small ice particles in cirrus. Using this principle, this project tested and developed PSD schemes that can help characterize cirrus clouds at each of the three ARM sites: SGP, NSA and TWP. This was the main effort of this project. These PSD schemes and ice sedimentation velocities predicted from them have been used to test the new cirrus microphysics parameterization in the GCM known as the Community Climate Systems Model (CCSM) as part of an ongoing collaboration with NCAR. Regarding the second problem, we developed and did preliminary testing on a passive thermal method for retrieving the total water path (TWP) of Arctic mixed phase clouds where TWPs are often in the range of 20 to 130 g m-2 (difficult for microwave radiometers to accurately measure). We also developed a new radar method for retrieving the cloud ice water content (IWC), which can be vertically integrated to yield the ice water path (IWP). These techniques were combined to determine the IWP and liquid water path (LWP) in Arctic clouds, and hence the fraction of ice and liquid water. We have tested this approach using a case study from the ARM field campaign called M-PACE (Mixed-Phase Arctic Cloud Experiment). This research led to a new satellite remote sensing method that appears promising for detecting low levels of liquid water in high clouds typically between -20 and -36 oC. We hope to develop this method in future research.« less

Snow precipitation on Mars driven by cloud-induced night-time convection

NASA Astrophysics Data System (ADS)

Spiga, Aymeric; Hinson, David P.; Madeleine, Jean-Baptiste; Navarro, Thomas; Millour, Ehouarn; Forget, François; Montmessin, Franck

2017-09-01

Although it contains less water vapour than Earth's atmosphere, the Martian atmosphere hosts clouds. These clouds, composed of water-ice particles, influence the global transport of water vapour and the seasonal variations of ice deposits. However, the influence of water-ice clouds on local weather is unclear: it is thought that Martian clouds are devoid of moist convective motions, and snow precipitation occurs only by the slow sedimentation of individual particles. Here we present numerical simulations of the meteorology in Martian cloudy regions that demonstrate that localized convective snowstorms can occur on Mars. We show that such snowstorms--or ice microbursts--can explain deep night-time mixing layers detected from orbit and precipitation signatures detected below water-ice clouds by the Phoenix lander. In our simulations, convective snowstorms occur only during the Martian night, and result from atmospheric instability due to radiative cooling of water-ice cloud particles. This triggers strong convective plumes within and below clouds, with fast snow precipitation resulting from the vigorous descending currents. Night-time convection in Martian water-ice clouds and the associated snow precipitation lead to transport of water both above and below the mixing layers, and thus would affect Mars' water cycle past and present, especially under the high-obliquity conditions associated with a more intense water cycle.

Estimation of radiative forcing and chore length of shallow convective clouds (SCC) based on broadband pyranometer measurement network

NASA Astrophysics Data System (ADS)

Shi, H.

2017-12-01

We presented a method to identify and calculate cloud radiative forcing (CRF) and horizontal chore length (L) of shallow convective clouds (SCC) using a network of 9 broadband pyranometers. The analyzing data was collected from the SCC campaign during two years summers (2015 2016) at Baiqi site over Inner Mongolia grassland. The network of pyranometers was operated across a spatial domain covering 42.16-42.30° N and 114.83-114.98° E. The SCC detection method was verified by observer reports and cameras, which showed that the detection method and human observations were in agreement about 75 %. The differences between the SCC detection method and human observations can be responsible for following factors: 1) small or dissipating clouds can be neglected for the value of 1 min of temporal resolution of pyranometer; 2) human observation recorded weather conditions four times every day; 3) SCC was indistinguishable from coexistence of SCC and Cirrus (Ci); 4) the SCC detection method is weighted toward clouds crossing the sun's path, while the human observer can view clouds over the entire sky. The deviation of L can be attributed to two factors: 1) the accuracy of wind speed at height of SCC and the ratio of horizontal and vertical length play a key role in determine values of L; 2) the effect of variance of solar zenith angle can be negligible. The downwelling shortwave CRF of SCC was -134.1 Wm-2. The average value of L of SCC was 1129 m. Besides, the distribution of normalized cloud chore length agreed well with power-law fit.

Mesospheric CO2 ice clouds on Mars observed by Planetary Fourier Spectrometer onboard Mars Express

NASA Astrophysics Data System (ADS)

Aoki, S.; Sato, Y.; Giuranna, M.; Wolkenberg, P.; Sato, T. M.; Nakagawa, H.; Kasaba, Y.

2018-03-01

We have investigated mesospheric CO2 ice clouds on Mars through analysis of near-infrared spectra acquired by Planetary Fourier Spectrometer (PFS) onboard the Mars Express (MEx) from MY 27 to MY 32. With the highest spectral resolution achieved thus far in the relevant spectral range among remote-sensing experiments orbiting Mars, PFS enables precise identification of the scattering peak of CO2 ice at the bottom of the 4.3 μm CO2 band. A total of 111 occurrences of CO2 ice cloud features have been detected over the period investigated. Data from the OMEGA imaging spectrometer onboard MEx confirm all of PFS detections from times when OMEGA operated simultaneously with PFS. The spatial and seasonal distributions of the CO2 ice clouds detected by PFS are consistent with previous observations by other instruments. We find CO2 ice clouds between Ls = 0° and 140° in distinct longitudinal corridors around the equatorial region (± 20°N). Moreover, CO2 ice clouds were preferentially detected at the observational LT range between 15-16 h in MY 29. However, observational biases prevent from distinguishing local time dependency from inter-annual variation. PFS also enables us to investigate the shape of mesospheric CO2 ice cloud spectral features in detail. In all cases, peaks were found between 4.240 and 4.265 μm. Relatively small secondary peaks were occasionally observed around 4.28 μm (8 occurrences). These spectral features cannot be reproduced using our radiative transfer model, which may be because the available CO2 ice refractive indices are inappropriate for the mesospheric temperatures of Mars, or because of the assumption in our model that the CO2 ice crystals are spherical and composed by pure CO2 ice.

Point Source Polarimetry with the Gemini Planet Imager: Sensitivity Characterization with T5.5 Dwarf Companion HD 19467 B

NASA Technical Reports Server (NTRS)

Jensen-Clem, Rebecca; Millar-Blanchaer, Max; Mawet, Dimitri; Graham, James R.; Wallace, J. Kent; Macintosh, Bruce; Hinkley, Sasha; Wiktorowicz, Sloane J.; Perrin, Marshall D.; Marley, Mark S.;

Method and apparatus for measuring purity of noble gases

DOEpatents

Austin, Robert

2008-04-01

A device for detecting impurities in a noble gas includes a detection chamber and a source of pulsed ultraviolet light. The pulse of the ultraviolet light is transferred into the detection chamber and onto a photocathode, thereby emitting a cloud of free electrons into the noble gas within the detection chamber. The cloud of electrons is attracted to the opposite end of the detection chamber by a high positive voltage potential at that end and focused onto a sensing anode. If there are impurities in the noble gas, some or all of the electrons within the cloud will bond with the impurity molecules and not reach the sensing anode. Therefore, measuring a lower signal at the sensing anode indicates a higher level of impurities while sensing a higher signal indicates fewer impurities. Impurities in the range of one part per billion can be measured by this device.

Point source polarimetry with the Gemini planet imager: Sensitivity characterization with T5.5 dwarf companion HD 19467 B

DOE PAGES

Jensen-Clem, Rebecca; Millar-Blanchaer, Max; Mawet, Dimitri; ...

2016-03-29

Detecting polarized light from self-luminous exoplanets has the potential to provide key information about rotation, surface gravity, cloud grain size, and cloud coverage. While field brown dwarfs with detected polarized emission are common, no exoplanet or substellar companion has yet been detected in polarized light. With the advent of high contrast imaging spectro-polarimeters such as GPI and SPHERE, such a detection may now be possible with careful treatment of instrumental polarization. In this paper, we present 28 minutes of H-band GPI polarimetric observations of the benchmark T5.5 companion HD 19467 B. We detect no polarization signal from the target, and place an upper limit on the degree of linear polarization ofmore » $${p}_{\\mathrm{CL}99.73\\%}\\leqslant 2.4\\%$$. In conclusion, we discuss our results in the context of T dwarf cloud models and photometric variability.« less

Screening of cloud microorganisms isolated at the Puy de Dôme (France) station for the production of biosurfactants

NASA Astrophysics Data System (ADS)

Renard, Pascal; Canet, Isabelle; Sancelme, Martine; Wirgot, Nolwenn; Deguillaume, Laurent; Delort, Anne-Marie

2016-09-01

A total of 480 microorganisms collected from 39 clouds sampled at the Puy de Dôme station (alt. 1465 m; 45°46'19'' N, 2°57'52'' E; Massif Central, France) were isolated and identified. This unique collection was screened for biosurfactant (surfactants of microbial origin) production by measuring the surface tension (σ) of the crude extracts, comprising the supernatants of the pure cultures, using the pendant drop technique. The results showed that 41 % of the tested strains were active producers (σ < 55 mN m-1), with 7 % being extremely active (σ < 30 mN m-1). The most efficient biosurfactant producers (σ < 45 mN m-1) belong to a few bacterial genera (Pseudomonas and Xanthomonas) from the Υ-Proteobacteria class (78 %) and a yeast genus (Udeniomyces) from the Basidiomycota phylum (11 %). Some Bacillus strains from the Firmicutes phylum were also active but represented a small fraction of the collected population. Strains from the Actinobacteria phylum in the collection examined in the present study showed moderate biosurfactant production (45<σ < 55 mN m-1). Pseudomonas (Υ-Proteobacteria), the most frequently detected genus in clouds, with some species issued from the phyllosphere, was the dominant group for the production of biosurfactants. We observed some correlations between the chemical composition of cloud water and the presence of biosurfactant-producing microorganisms, suggesting the "biogeography" of this production. Moreover, the potential impact of the production of biosurfactants by cloud microorganisms on atmospheric processes is discussed.

A compressive sensing based secure watermark detection and privacy preserving storage framework.

PubMed

Qia Wang; Wenjun Zeng; Jun Tian

2014-03-01

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

Measured electric field intensities near electric cloud discharges detected by the Kennedy Space Center's Lightning Detection and Ranging System, LDAR

NASA Technical Reports Server (NTRS)

Poehler, H. A.

1977-01-01

For a summer thunderstorm, for which simultaneous, airborne electric field measurements and Lightning Detection and Ranging (LDAR) System data was available, measurements were coordinated to present a picture of the electric field intensity near cloud electrical discharges detected by the LDAR System. Radar precipitation echos from NOAA's 10 cm weather radar and measured airborne electric field intensities were superimposed on LDAR PPI plots to present a coordinated data picture of thunderstorm activity.

A laboratory investigation of the reflective properties of simulated, optically thick clouds

NASA Technical Reports Server (NTRS)

Mckee, T. B.; Cox, S. K.

1982-01-01

The Cloud Field Optical Simulator project includes work in the following areas: (1) improvement in the shape of the desired (visible) spectral response of the measurement, (2) selection of two usable materials for cloud simulation, (3) a means of assigning a visible optical depth to the simulated clouds, and (4) confirmation that the apparatus is capable of detecting basic finite cloud characteristics. A brief description of the accomplishments in each of these areas is presented.

Validation of a radiosonde-based cloud layer detection method against a ground-based remote sensing method at multiple ARM sites

NASA Astrophysics Data System (ADS)

Zhang, Jinqiang; Li, Zhanqing; Chen, Hongbin; Cribb, Maureen

2013-01-01

Cloud vertical structure is a key quantity in meteorological and climate studies, but it is also among the most difficult quantities to observe. In this study, we develop a long-term (10 years) radiosonde-based cloud profile product for the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP), Tropical Western Pacific (TWP), and North Slope of Alaska (NSA) sites and a shorter-term product for the ARM Mobile Facility (AMF) deployed in Shouxian, Anhui Province, China (AMF-China). The AMF-China site was in operation from 14 May to 28 December 2008; the ARM sites have been collecting data for over 15 years. The Active Remote Sensing of Cloud (ARSCL) value-added product (VAP), which combines data from the 95-GHz W-band ARM Cloud Radar (WACR) and/or the 35-GHz Millimeter Microwave Cloud Radar (MMCR), is used in this study to validate the radiosonde-based cloud layer retrieval method. The performance of the radiosonde-based cloud layer retrieval method applied to data from different climate regimes is evaluated. Overall, cloud layers derived from the ARSCL VAP and radiosonde data agree very well at the SGP and AMF-China sites. At the TWP and NSA sites, the radiosonde tends to detect more cloud layers in the upper troposphere.

Bayesian cloud detection for MERIS, AATSR, and their combination

NASA Astrophysics Data System (ADS)

Hollstein, A.; Fischer, J.; Carbajal Henken, C.; Preusker, R.

2014-11-01

A broad range of different of Bayesian cloud detection schemes is applied to measurements from the Medium Resolution Imaging Spectrometer (MERIS), the Advanced Along-Track Scanning Radiometer (AATSR), and their combination. The cloud masks were designed to be numerically efficient and suited for the processing of large amounts of data. Results from the classical and naive approach to Bayesian cloud masking are discussed for MERIS and AATSR as well as for their combination. A sensitivity study on the resolution of multidimensional histograms, which were post-processed by Gaussian smoothing, shows how theoretically insufficient amounts of truth data can be used to set up accurate classical Bayesian cloud masks. Sets of exploited features from single and derived channels are numerically optimized and results for naive and classical Bayesian cloud masks are presented. The application of the Bayesian approach is discussed in terms of reproducing existing algorithms, enhancing existing algorithms, increasing the robustness of existing algorithms, and on setting up new classification schemes based on manually classified scenes.

A cloud-based system for automatic glaucoma screening.

PubMed

Fengshou Yin; Damon Wing Kee Wong; Ying Quan; Ai Ping Yow; Ngan Meng Tan; Gopalakrishnan, Kavitha; Beng Hai Lee; Yanwu Xu; Zhuo Zhang; Jun Cheng; Jiang Liu

2015-08-01

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases including glaucoma. However, these systems are usually standalone software with basic functions only, limiting their usage in a large scale. In this paper, we introduce an online cloud-based system for automatic glaucoma screening through the use of medical image-based pattern classification technologies. It is designed in a hybrid cloud pattern to offer both accessibility and enhanced security. Raw data including patient's medical condition and fundus image, and resultant medical reports are collected and distributed through the public cloud tier. In the private cloud tier, automatic analysis and assessment of colour retinal fundus images are performed. The ubiquitous anywhere access nature of the system through the cloud platform facilitates a more efficient and cost-effective means of glaucoma screening, allowing the disease to be detected earlier and enabling early intervention for more efficient intervention and disease management.

Detection of single and multilayer clouds in an artificial neural network approach

NASA Astrophysics Data System (ADS)

Sun-Mack, Sunny; Minnis, Patrick; Smith, William L.; Hong, Gang; Chen, Yan

2017-10-01

Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output truth for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.

New Perspectives of Point Clouds Color Management - the Development of Tool in Matlab for Applications in Cultural Heritage

NASA Astrophysics Data System (ADS)

Pepe, M.; Ackermann, S.; Fregonese, L.; Achille, C.

2017-02-01

The paper describes a method for Point Clouds Color management and Integration obtained from Terrestrial Laser Scanner (TLS) and Image Based (IB) survey techniques. Especially in the Cultural Heritage (CH) environment, methods and techniques to improve the color quality of Point Clouds have a key role because a homogenous texture brings to a more accurate reconstruction of the investigated object and to a more pleasant perception of the color object as well. A color management method for point clouds can be useful in case of single data set acquired by TLS or IB technique as well as in case of chromatic heterogeneity resulting by merging different datasets. The latter condition can occur when the scans are acquired in different moments of the same day or when scans of the same object are performed in a period of weeks or months, and consequently with a different environment/lighting condition. In this paper, a procedure to balance the point cloud color in order to uniform the different data sets, to improve the chromatic quality and to highlight further details will be presented and discussed.

Estimating GATE rainfall with geosynchronous satellite images

NASA Technical Reports Server (NTRS)

Stout, J. E.; Martin, D. W.; Sikdar, D. N.

1979-01-01

A method of estimating GATE rainfall from either visible or infrared images of geosynchronous satellites is described. Rain is estimated from cumulonimbus cloud area by the equation R = a sub 0 A + a sub 1 dA/dt, where R is volumetric rainfall, A cloud area, t time, and a sub 0 and a sub 1 are constants. Rainfall, calculated from 5.3 cm ship radar, and cloud area are measured from clouds in the tropical North Atlantic. The constants a sub 0 and a sub 1 are fit to these measurements by the least-squares method. Hourly estimates by the infrared version of this technique correlate well (correlation coefficient of 0.84) with rain totals derived from composited radar for an area of 100,000 sq km. The accuracy of this method is described and compared to that of another technique using geosynchronous satellite images. It is concluded that this technique provides useful estimates of tropical oceanic rainfall on a convective scale.

Lidar

NASA Technical Reports Server (NTRS)

Collis, R. T. H.

1969-01-01

Lidar is an optical radar technique employing laser energy. Variations in signal intensity as a function of range provide information on atmospheric constituents, even when these are too tenuous to be normally visible. The theoretical and technical basis of the technique is described and typical values of the atmospheric optical parameters given. The significance of these parameters to atmospheric and meteorological problems is discussed. While the basic technique can provide valuable information about clouds and other material in the atmosphere, it is not possible to determine particle size and number concentrations precisely. There are also inherent difficulties in evaluating lidar observations. Nevertheless, lidar can provide much useful information as is shown by illustrations. These include lidar observations of: cirrus cloud, showing mountain wave motions; stratification in clear air due to the thermal profile near the ground; determinations of low cloud and visibility along an air-field approach path; and finally the motion and internal structure of clouds of tracer materials (insecticide spray and explosion-caused dust) which demonstrate the use of lidar for studying transport and diffusion processes.