The application of time series models to cloud field morphology analysis

Science.gov (United States)

Chin, Roland T.; Jau, Jack Y. C.; Weinman, James A.

1987-01-01

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters - Part 2: Aerosols

Science.gov (United States)

Wind, Galina; da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-07-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a "simulated radiance" product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land-ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters . Part 2; Aerosols

Science.gov (United States)

Wind, Galina; Da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-01-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to

Macrophysical properties of continental cumulus clouds from active and passive remote sensing

Energy Technology Data Exchange (ETDEWEB)

Kassianov, Evgueni I.; Riley, Erin A.; Kleiss, Jessica; Long, Charles N.; Riihimaki, Laura D.; Flynn, Donna M.; Flynn, Connor J M.; Berg, Larry K.

2017-10-06

Cloud amount is an essential and extensively used macrophysical parameter of cumulus clouds. It is commonly defined as a cloud fraction (CF) from zenith-pointing ground-based active and passive remote sensing. However, conventional retrievals of CF from the remote sensing data with very narrow field-of-view (FOV) may not be representative of the surrounding area. Here we assess its representativeness using an integrated dataset collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site in Oklahoma, USA. For our assessment with focus on selected days with single-layer cumulus clouds (2005-2016), we include the narrow-FOV ARM Active Remotely Sensed Clouds Locations (ARSCL) and large-FOV Total Sky Imager (TSI) cloud products, the 915-MHz Radar Wind Profiler (RWP) measurements of wind speed and direction, and also high-resolution satellite images from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS). We demonstrate that a root-mean-square difference (RMSD) between the 15-min averaged ARSCL cloud fraction (CF) and the 15-min averaged TSI fractional sky cover (FSC) is large (up to 0.3). We also discuss how the horizontal distribution of clouds can modify the obtained large RMSD using a new uniformity metric. The latter utilizes the spatial distribution of the FSC over the 100° FOV TSI images obtained with high temporal resolution (30 sec sampling). We demonstrate that cases with more uniform spatial distribution of FSC show better agreement between the narrow-FOV CF and large-FOV FSC, reducing the RMSD by up to a factor of 2.

Cloud Computing, Tieto Cloud Server Model

OpenAIRE

Suikkanen, Saara

2013-01-01

The purpose of this study is to find out what is cloud computing. To be able to make wise decisions when moving to cloud or considering it, companies need to understand what cloud is consists of. Which model suits best to they company, what should be taken into account before moving to cloud, what is the cloud broker role and also SWOT analysis of cloud? To be able to answer customer requirements and business demands, IT companies should develop and produce new service models. IT house T...

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

Science.gov (United States)

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

Optical fibre multi-parameter sensing with secure cloud based signal capture and processing

Science.gov (United States)

Newe, Thomas; O'Connell, Eoin; Meere, Damien; Yuan, Hongwei; Leen, Gabriel; O'Keeffe, Sinead; Lewis, Elfed

2016-05-01

Recent advancements in cloud computing technologies in the context of optical and optical fibre based systems are reported. The proliferation of real time and multi-channel based sensor systems represents significant growth in data volume. This coupled with a growing need for security presents many challenges and presents a huge opportunity for an evolutionary step in the widespread application of these sensing technologies. A tiered infrastructural system approach is adopted that is designed to facilitate the delivery of Optical Fibre-based "SENsing as a Service- SENaaS". Within this infrastructure, novel optical sensing platforms, deployed within different environments, are interfaced with a Cloud-based backbone infrastructure which facilitates the secure collection, storage and analysis of real-time data. Feedback systems, which harness this data to affect a change within the monitored location/environment/condition, are also discussed. The cloud based system presented here can also be used with chemical and physical sensors that require real-time data analysis, processing and feedback.

Optimizing cloud removal from satellite remotely sensed data for monitoring vegetation dynamics in humid tropical climate

International Nuclear Information System (INIS)

Hashim, M; Pour, A B; Onn, C H

2014-01-01

Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper + (ETM + ) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM + dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate

VMware private cloud computing with vCloud director

CERN Document Server

Gallagher, Simon

2013-01-01

It's All About Delivering Service with vCloud Director Empowered by virtualization, companies are not just moving into the cloud, they're moving into private clouds for greater security, flexibility, and cost savings. However, this move involves more than just infrastructure. It also represents a different business model and a new way to provide services. In this detailed book, VMware vExpert Simon Gallagher makes sense of private cloud computing for IT administrators. From basic cloud theory and strategies for adoption to practical implementation, he covers all the issues. You'll lea

A New Method of Cloud Detection Based on Cascaded AdaBoost

International Nuclear Information System (INIS)

Ma, C; Chen, F; Liu, J; Duan, J

2014-01-01

Cloud detection of remote sensing image is a critical step in the processing of the remote sensing images. How to quickly, accurately and effectively detect cloud on remote sensing images, is still a challenging issue in this area. In order to avoid disadvantages of the current algorithms, the cascaded AdaBoost classifier algorithm is successfully applied to the cloud detection. A new algorithm combined cascaded AdaBoost classifier and multi-features, is proposed in this paper. First, multi-features based on the color, texture and spectral features are extracted from the remote sensing image. Second, the automatic cloud detection model is obtained based on the cascaded AdaBoost algorithm. In this paper, the results show that the new algorithm can determine cloud detection model and threshold values adaptively for different resolution remote sensing training data. The accuracy of cloud detection is improved. So it is a new effective algorithm for the cloud detection of remote sensing images

Evaluating statistical cloud schemes: What can we gain from ground-based remote sensing?

Science.gov (United States)

Grützun, V.; Quaas, J.; Morcrette, C. J.; Ament, F.

2013-09-01

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the "perfect model approach." This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme.

Added Value of Far-Infrared Radiometry for Ice Cloud Remote Sensing

Science.gov (United States)

Libois, Q.; Blanchet, J. P.; Ivanescu, L.; S Pelletier, L.; Laurence, C.

2017-12-01

Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, most of these observations only cover the midinfrared (MIR, λ technology, though, now make it possible to consider spaceborne remote sensing in the FIR. Here we show that adding a few FIR channels with realistic radiometric performances to existing spaceborne narrowband radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere above clouds is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. This would somehow extend the range of applicability of current infrared retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection-based algorithms would thus be reached in nighttime conditions. Using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes, which is highly relevant for cirrus clouds and convective towers, and for investigating the water cycle in the driest regions of the atmosphere.

Remote sensing the susceptibility of cloud albedo to changes in drop concentration

International Nuclear Information System (INIS)

Platnick, S.E.

1991-01-01

The role of clouds in reflecting solar radiation to space and thereby reducing surface heating is of critical importance to climate. Combustion processes that produce greenhouse gases also increase cloud condensation nuclei (CCN) concentrations which in turn increase cloud drop concentrations and thereby cloud albedo. A calculation of cloud susceptibility, defined in this work as the increase in albedo resulting from the addition of one cloud drop per cubic centimeter (as cloud liquid water content remains constant), is made through satellite remote sensing of cloud drop radius and optical thickness. The remote technique uses spectral channels of the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA polar orbiting satellites. Radiative transfer calculations of reflectance and effective surface and cloud emissivities are made for applicable sun and satellite viewing angles, including azimuth, at various radii and optical thicknesses for each AVHRR channel. Emission in channel 3 (at 3.75 microns) is removed to give the reflected solar component. These calculations are used to infer the radius and optical thickness giving the best match to the satellite measurements. The effect of the atmosphere on the signal received by the satellite is included in the analysis

Satellite remote sensing and cloud modeling of St. Anthony, Minnesota storm clouds and dew point depression

Science.gov (United States)

Hung, R. J.; Tsao, Y. D.

1988-01-01

Rawinsonde data and geosynchronous satellite imagery were used to investigate the life cycles of St. Anthony, Minnesota's severe convective storms. It is found that the fully developed storm clouds, with overshooting cloud tops penetrating above the tropopause, collapsed about three minutes before the touchdown of the tornadoes. Results indicate that the probability of producing an outbreak of tornadoes causing greater damage increases when there are higher values of potential energy storage per unit area for overshooting cloud tops penetrating the tropopause. It is also found that there is less chance for clouds with a lower moisture content to be outgrown as a storm cloud than clouds with a higher moisture content.

Exploring the Effects of Cloud Vertical Structure on Cloud Microphysical Retrievals based on Polarized Reflectances

Science.gov (United States)

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

2013-12-01

A polarized cloud reflectance simulator was developed by coupling an LES cloud model with a polarized radiative transfer model to assess the capabilities of polarimetric cloud retrievals. With future remote sensing campaigns like NASA's Aerosols/Clouds/Ecosystems (ACE) planning to feature advanced polarimetric instruments it is important for the cloud remote sensing community to understand the retrievable information available and the related systematic/methodical limitations. The cloud retrieval simulator we have developed allows us to probe these important questions in a realistically relevant test bed. Our simulator utilizes a polarized adding-doubling radiative transfer model and an LES cloud field from a DHARMA simulation (Ackerman et al. 2004) with cloud properties based on the stratocumulus clouds observed during the DYCOMS-II field campaign. In this study we will focus on how the vertical structure of cloud microphysics can influence polarized cloud effective radius retrievals. Numerous previous studies have explored how retrievals based on total reflectance are affected by cloud vertical structure (Platnick 2000, Chang and Li 2002) but no such studies about the effects of vertical structure on polarized retrievals exist. Unlike the total cloud reflectance, which is predominantly multiply scattered light, the polarized reflectance is primarily the result of singly scattered photons. Thus the polarized reflectance is sensitive to only the uppermost region of the cloud (tau~influencer on the microphysical development of cloud droplets, can be potentially studied with polarimetric retrievals.

A physically based algorithm for non-blackbody correction of the cloud top temperature for the convective clouds

Science.gov (United States)

Wang, C.; Luo, Z. J.; Chen, X.; Zeng, X.; Tao, W.; Huang, X.

2012-12-01

Cloud top temperature is a key parameter to retrieval in the remote sensing of convective clouds. Passive remote sensing cannot directly measure the temperature at the cloud tops. Here we explore a synergistic way of estimating cloud top temperature by making use of the simultaneous passive and active remote sensing of clouds (in this case, CloudSat and MODIS). Weighting function of the MODIS 11μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat retrievals and temperature and humidity profiles based on ECMWF ERA-interim reanalysis into a radiation transfer model. Among 19,699 tropical deep convective clouds observed by the CloudSat in 2008, the averaged effective emission level (EEL, where the weighting function attains its maximum) is at optical depth 0.91 with a standard deviation of 0.33. Furthermore, the vertical gradient of CloudSat radar reflectivity, an indicator of the fuzziness of convective cloud top, is linearly proportional to, d_{CTH-EEL}, the distance between the EEL of 11μm channel and cloud top height (CTH) determined by the CloudSat when d_{CTH-EEL}<0.6km. Beyond 0.6km, the distance has little sensitivity to the vertical gradient of CloudSat radar reflectivity. Based on these findings, we derive a formula between the fuzziness in the cloud top region, which is measurable by CloudSat, and the MODIS 11μm brightness temperature assuming that the difference between effective emission temperature and the 11μm brightness temperature is proportional to the cloud top fuzziness. This formula is verified using the simulated deep convective cloud profiles by the Goddard Cumulus Ensemble model. We further discuss the application of this formula in estimating cloud top buoyancy as well as the error characteristics of the radiative calculation within such deep-convective clouds.

The accuracy of remotely-sensed IWC: An assessment from MLS, TRMM and CloudSat statistics

Science.gov (United States)

Wu, D. L.; Heymsfield, A. J.

2006-12-01

Understanding climate change requires accurate global cloud ice water content (IWC) measurements. Satellite remote sensing has been the major tool to provide such global observations, but the accuracy of deduced IWC depends on knowledge of cloud microphysics learned from in-situ samples. Because only limited number and type of ice clouds have been measured by in-situ sensors, the knowledge about cloud microphysics is incomplete, and the IWC accuracy from remote sensing can vary from 30% to 200% from case to case. Recent observations from MLS, TRMM and CloudSat allow us to evaluate consistency and accuracy of IWCs deduced from passive and active satellite techniques. In this study we conduct statistical analyses on the tropical and subtropical IWCs observed by MLS, TRMM and CloudSat. The probability density functions (PDFs) of IWC are found to depend on the volume size of averaging, and therefore data need to be averaged into the same volume in order for fair comparisons. Showing measurement noise, bias and sensitivity, the PDF is a better characterization than an average for evaluating IWC accuracy because an averaged IWC depends on cloud-detection threshold that can vary from sensor to sensor. Different thresholds will not only change the average value but also change cloud fraction and occurrence frequency. Our study shows that MLS and TRMM IWCs, despite large differences in sensitivity with little overlap, can still be compared under PDF. The two statistics are generally consistent within 50% at ~13 km, obeying an approximate lognormal distribution as suggested by some ground-based radar observations. MLS has sensitivity to IWC of 1-100 mg/m3 whereas TRMM can improve its sensitivity to IWC as low as 70 mg/m3 if the radar data are averaged properly for the equivalent volume of MLS samples. The proper statistical averaging requires full characteristics of IWC noise, which are not available for products normally derived from radar reflectivity, and therefore we

Quantitative Measures of Immersion in Cloud and the Biogeography of Cloud Forests

Science.gov (United States)

Lawton, R. O.; Nair, U. S.; Ray, D.; Regmi, A.; Pounds, J. A.; Welch, R. M.

2010-01-01

Sites described as tropical montane cloud forests differ greatly, in part because observers tend to differ in their opinion as to what constitutes frequent and prolonged immersion in cloud. This definitional difficulty interferes with hydrologic analyses, assessments of environmental impacts on ecosystems, and biogeographical analyses of cloud forest communities and species. Quantitative measurements of cloud immersion can be obtained on site, but the observations are necessarily spatially limited, although well-placed observers can examine 10 50 km of a mountain range under rainless conditions. Regional analyses, however, require observations at a broader scale. This chapter discusses remote sensing and modeling approaches that can provide quantitative measures of the spatiotemporal patterns of cloud cover and cloud immersion in tropical mountain ranges. These approaches integrate remote sensing tools of various spatial resolutions and frequencies of observation, digital elevation models, regional atmospheric models, and ground-based observations to provide measures of cloud cover, cloud base height, and the intersection of cloud and terrain. This combined approach was applied to the Monteverde region of northern Costa Rica to illustrate how the proportion of time the forest is immersed in cloud may vary spatially and temporally. The observed spatial variation was largely due to patterns of airflow over the mountains. The temporal variation reflected the diurnal rise and fall of the orographic cloud base, which was influenced in turn by synoptic weather conditions, the seasonal movement of the Intertropical Convergence Zone and the north-easterly trade winds. Knowledge of the proportion of the time that sites are immersed in clouds should facilitate ecological comparisons and biogeographical analyses, as well as land use planning and hydrologic assessments in areas where intensive on-site work is not feasible.

A Cloud Computing Workflow for Scalable Integration of Remote Sensing and Social Media Data in Urban Studies

Science.gov (United States)

Soliman, A.; Soltani, K.; Yin, J.; Subramaniam, B.; Liu, Y.; Padmanabhan, A.; Riteau, P.; Keahey, K.; Wang, S. W.

2015-12-01

Urban ecosystems are unique earth environments because both their physical and social components contribute to the overall dynamics of the system. Up-to-date, remote sensing data (e.g. optical and LiDAR) allowed researchers to monitor the development of impervious surfaces however, it was not adequate to detect associated social dynamics. Geo-located social media (e.g. Twitter) provides a data source to detect population dynamics and understand the interaction of people with their physical environment. Although, integrating social media with remote sensing data has been hindered by large volumes of data and the lack of models for integrating remote sensing products with unstructured social media data. In this research work, we leveraged the NSF chameleon cloud computing platform to provide virtual clusters and elastic auto-scaling of resources that are needed for the synthesis of landuse and geo-located Twitter data. In this context, data synthesis was used to address research questions related to population dynamics in major metropolitan areas. We provide an overview of a cloud computing workflow comprised of a set of coupled scalable synthesis modules for: a) preprocessing data, which includes storage and query of heterogeneous data streams, b) spatial data integration, which matches geo-located Twitter data with user defined landuse maps based on a conceptual model of human mobility and c) visualization of urban mobility patterns. Our results demonstrate the flexibility to connect data, synthesis methods and computing resources using cloud computing, which would be otherwise very difficult for untrained scientists to setup and control. Furthermore, we demonstrate the capabilities of CyberGIS-based workflow using the case study of comparing commuting distances across major US cities from 2013 through the present. We demonstrate how our workflow will support discoveries in urban ecological studies as well as linking human and physical dimensions in environmental

Coupled fvGCM-GCE Modeling System, 3D Cloud-Resolving Model and Cloud Library

Science.gov (United States)

Tao, Wei-Kuo

2005-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud- resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. A seed fund is available at NASA Goddard to build a MMF based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM). A prototype MMF in being developed and production runs will be conducted at the beginning of 2005. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes, ( 2 ) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), (3) A cloud library generated by Goddard MMF, and 3D GCE model, and (4) A brief discussion on the GCE model on developing a global cloud simulator.

Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

Science.gov (United States)

Ben-David, Avishai; Davidson, Charles E

2012-04-23

A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics. © 2012 Optical Society of America

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

Directory of Open Access Journals (Sweden)

Adam M Wilson

2016-03-01

Full Text Available Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Satellite remote sensing of aerosol and cloud properties over Eurasia

Science.gov (United States)

Sogacheva, Larisa; Kolmonen, Pekka; Saponaro, Giulia; Virtanen, Timo; Rodriguez, Edith; Sundström, Anu-Maija; Atlaskina, Ksenia; de Leeuw, Gerrit

2015-04-01

Satellite remote sensing provides the spatial distribution of aerosol and cloud properties over a wide area. In our studies large data sets are used for statistical studies on aerosol and cloud interaction in an area over Fennoscandia, the Baltic Sea and adjacent regions over the European mainland. This area spans several regimes with different influences on aerosol cloud interaction such as a the transition from relative clean air over Fennoscandia to more anthropogenically polluted air further south, and the influence maritime air over the Baltic and oceanic air advected from the North Atlantic. Anthropogenic pollution occurs in several parts of the study area, and in particular near densely populated areas and megacities, but also in industrialized areas and areas with dense traffic. The aerosol in such areas is quite different from that produced over the boreal forest and has different effects on air quality and climate. Studies have been made on the effects of aerosols on air quality and on the radiation balance in China. The aim of the study is to study the effect of these different regimes on aerosol-cloud interaction using a large aerosol and cloud data set retrieved with the (Advanced) Along Track Scanning Radiometer (A)ATSR Dual View algorithm (ADV) further developed at Finnish Meteorological Institute and aerosol and cloud data provided by MODIS. Retrieval algorithms for aerosol and clouds have been developed for the (A)ATSR, consisting of a series of instruments of which we use the second and third one: ATSR-2 which flew on the ERS-2 satellite (1995-2003) and AATSR which flew on the ENVISAT satellite (2002-2012) (both from the European Space Agency, ESA). The ADV algorithm provides aerosol data on a global scale with a default resolution of 10x10km2 (L2) and an aggregate product on 1x1 degree (L3). Optional, a 1x1 km2 retrieval products is available over smaller areas for specific studies. Since for the retrieval of AOD no prior knowledge is needed on

Examining the impact of overlying aerosols on the retrieval of cloud optical properties from passive remote sensing

Science.gov (United States)

Coddington, O. M.; Pilewskie, P.; Redemann, J.; Platnick, S.; Russell, P. B.; Schmidt, K. S.; Gore, W. J.; Livingston, J.; Wind, G.; Vukicevic, T.

2010-05-01

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 μm) 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 μm, 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.

Examining the Impact of Overlying Aerosols on the Retrieval of Cloud Optical Properties from Passive Remote Sensing

Science.gov (United States)

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.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

Science.gov (United States)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Polar clouds and radiation in satellite observations, reanalyses, and climate models

NARCIS (Netherlands)

Lenaerts, JTM; Van Tricht, Kristof; Lhermitte, S.L.M.; L'Ecuyer, T.S.

2017-01-01

Clouds play a pivotal role in the surface energy budget of the polar regions. Here we use two largely independent data sets of cloud and surface downwelling radiation observations derived by satellite remote sensing (2007–2010) to evaluate simulated clouds and radiation over both polar ice sheets

Spectral Dependence of MODIS Cloud Droplet Effective Radius Retrievals for Marine Boundary Layer Clouds

Science.gov (United States)

Zhang, Zhibo; Platnick, Steven E.; Ackerman, Andrew S.; Cho, Hyoun-Myoung

2014-01-01

Low-level warm marine boundary layer (MBL) clouds cover large regions of Earth's surface. They have a significant role in Earth's radiative energy balance and hydrological cycle. Despite the fundamental role of low-level warm water clouds in climate, our understanding of these clouds is still limited. In particular, connections between their properties (e.g. cloud fraction, cloud water path, and cloud droplet size) and environmental factors such as aerosol loading and meteorological conditions continue to be uncertain or unknown. Modeling these clouds in climate models remains a challenging problem. As a result, the influence of aerosols on these clouds in the past and future, and the potential impacts of these clouds on global warming remain open questions leading to substantial uncertainty in climate projections. To improve our understanding of these clouds, we need continuous observations of cloud properties on both a global scale and over a long enough timescale for climate studies. At present, satellite-based remote sensing is the only means of providing such observations.

Investigation of Arctic mixed-phase clouds by combining airborne remote sensing and in situ observations during VERDI, RACEPAC and ACLOUD

Science.gov (United States)

Ehrlich, André; Bierwirth, Eike; Borrmann, Stephan; Crewell, Susanne; Herber, Andreas; Hoor, Peter; Jourdan, Olivier; Krämer, Martina; Lüpkes, Christof; Mertes, Stephan; Neuber, Roland; Petzold, Andreas; Schnaiter, Martin; Schneider, Johannes; Weigel, Ralf; Weinzierl, Bernadett; Wendisch, Manfred

2016-04-01

To improve our understanding of Arctic mixed-phase clouds a series of airborne research campaigns has been initiated by a collaboration of German research institutes. Clouds in areas dominated by a close sea-ice cover were observed during the research campaign Vertical distribution of ice in Arctic mixed-phase clouds (VERDI, April/May 2012) and the Radiation-Aerosol-Cloud Experiment in the Arctic Circle (RACEPAC, April/May 2014) which both were based in Inuvik, Canada. The aircraft (Polar 5 & 6, Basler BT-67) operated by the Alfred Wegener Institute for Polar and Marine Research, Germany did cover a wide area above the Canadian Beaufort with in total 149 flight hours (62h during VERDI, 87h during RACEPAC). For May/June 2017 a third campaign ACLOUD (Arctic Clouds - Characterization of Ice, aerosol Particles and Energy fluxes) with base in Svalbard is planned within the Transregional Collaborative Research Centre TR 172 ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3 to investigate Arctic clouds in the transition zone between open ocean and sea ice. The aim of all campaigns is to combine remote sensing and in-situ cloud, aerosol and trace gas measurements to investigate interactions between radiation, cloud and aerosol particles. While during VERDI remote sensing and in-situ measurements were performed by one aircraft subsequently, for RACEPAC and ACLOUD two identical aircraft are coordinated at different altitudes to horizontally collocate both remote sensing and in-situ measurements. The campaign showed that in this way radiative and microphysical processes in the clouds can by studied more reliably and remote sensing methods can be validated efficiently. Here we will illustrate the scientific strategy of the projects including the progress in instrumentation. Differences in the general synoptic and sea ice situation and related changes in cloud properties at the different locations and seasons will be

Cloud remote sensing from space in the era of the A-Train

Science.gov (United States)

Stephens, Graeme L.; Vane, Deborah G.

2006-09-01

The clouds of Earth are fundamental to most aspects of human life. Through production of precipitation, they are essential for delivering and sustaining the supplies of fresh water upon which human life depends. Clouds further exert a principal influence on the planet's energy balance. It is in clouds that latent heat is released through the process of condensation and the formation of precipitation affecting the development and evolution of the planet's storm systems. Clouds further exert a profound influence on the solar and infrared radiation that enters and leaves the atmosphere, further exerting profound effects on climate and on forces that affect climate change (Stephens, 2005). It is for these reasons, among others, that the need to observe the distribution and variability of the properties of clouds and precipitation has emerged as a priority in Earth observations. Most past and current observational programs are contructed in such a way that clouds and precipitation are treated as separate entities. Nature does not work this way and there is much to be gained scientifically in moving away from these artificial practices toward observing clouds and precipitation properties jointly. We are now embarking on a new age of remote sensing of clouds and precipitation using active sensors, starting with the tropical rainfall measurement mission (TRMM) and continuing on with the A-Train (described below). This new age provides us with the opportunity to move away from past and present artificial observing practices offering a more unified approach to observing clouds and precipitation properties jointly.

Remote Cloud Sensing Intensive Observation Period (RCS-IOP) millimeter-wave radar calibration and data intercomparison

Energy Technology Data Exchange (ETDEWEB)

Sekelsky, S.M.; Firda, J.M.; McIntosh, R.E. [Univ. of Massachusetts, Amherst, MA (United States)] [and others

1996-04-01

During April 1994, the University of Massachusetts (UMass) and the Pennsylvania State University (Penn State) fielded two millimeter-wave atmospheric radars in the Atmospheric Radiation Measurement Remote Cloud Sensing Intensive Operation Period (RCS-IOP) experiment. The UMass Cloud Profiling Radar System (CPRS) operates simultaneously at 33.12 GHz and 94.92 GHz through a single antenna. The Penn State radar operates at 93.95 GHz and has separate transmitting and receiving antennas. The two systems were separated by approximately 75 meters and simultaneously observed a variety of cloud types at verticle incidence over the course of the experiment. This abstract presents some initial results from our calibration efforts. An absolute calibration of the UMass radar was made from radar measurements of a trihedral corner reflector, which has a known radar cross-section. A relative calibration of between the Penn State and UMass radars is made from the statistical comparison of zenith pointing measurements of low altitude liquid clouds. Attenuation is removed with the aid of radiosonde data, and the difference in the calibration between the UMass and Penn State radars is determined by comparing the ratio of 94-GHz and 95-GHz reflectivity values to a model that accounts for parallax effects of the two antennas used in the Penn State system.

Validation of Cloud Optical Parameters from Passive Remote Sensing in the Arctic by using the Aircraft Measurements

Science.gov (United States)

Chen, H.; Schmidt, S.; Coddington, O.; Wind, G.; Bucholtz, A.; Segal-Rosenhaimer, M.; LeBlanc, S. E.

2017-12-01

Cloud Optical Parameters (COPs: e.g., cloud optical thickness and cloud effective radius) and surface albedo are the most important inputs for determining the Cloud Radiative Effect (CRE) at the surface. In the Arctic, the COPs derived from passive remote sensing such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) are difficult to obtain with adequate accuracy owing mainly to insufficient knowledge about the snow/ice surface, but also because of the low solar zenith angle. This study aims to validate COPs derived from passive remote sensing in the Arctic by using aircraft measurements collected during two field campaigns based in Fairbanks, Alaska. During both experiments, ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and ARISE (Arctic Radiation-IceBridge Sea and Ice Experiment), the Solar Spectral Flux Radiometer (SSFR) measured upwelling and downwelling shortwave spectral irradiances, which can be used to derive surface and cloud albedo, as well as the irradiance transmitted by clouds. We assess the variability of the Arctic sea ice/snow surfaces albedo through these aircraft measurements and incorporate this variability into cloud retrievals for SSFR. We then compare COPs as derived from SSFR and MODIS for all suitable aircraft underpasses of the satellites. Finally, the sensitivities of the COPs to surface albedo and solar zenith angle are investigated.

Effects of ice crystal surface roughness and air bubble inclusions on cirrus cloud radiative properties from remote sensing perspective

International Nuclear Information System (INIS)

Tang, Guanglin; Panetta, R. Lee; Yang, Ping; Kattawar, George W.; Zhai, Peng-Wang

2017-01-01

We study the combined effects of surface roughness and inhomogeneity on the optical scattering properties of ice crystals and explore the consequent implications to remote sensing of cirrus cloud properties. Specifically, surface roughness and inhomogeneity are added to the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 (MC6) cirrus cloud particle habit model. Light scattering properties of the new habit model are simulated using a modified version of the Improved Geometric Optics Method (IGOM). Both inhomogeneity and surface roughness affect the single scattering properties significantly. In visible bands, inhomogeneity and surface roughness both tend to smooth the phase function and eliminate halos and the backscattering peak. The asymmetry parameter varies with the degree of surface roughness following a U shape - decreases and then increases - with a minimum at around 0.15, whereas it decreases monotonically with the air bubble volume fraction. Air bubble inclusions significantly increase phase matrix element -P_1_2 for scattering angles between 20°–120°, whereas surface roughness has a much weaker effect, increasing -P_1_2 slightly from 60°–120°. Radiative transfer simulations and cirrus cloud property retrievals are conducted by including both the factors. In terms of surface roughness and air bubble volume fraction, retrievals of cirrus cloud optical thickness or the asymmetry parameter using solar bands show similar patterns of variation. Polarimetric simulations using the MC6 cirrus cloud particle habit model are shown to be more consistent with observations when both surface roughness and inhomogeneity are simultaneously considered. - Highlights: • Surface roughness and air bubble inclusions affect optical properties of ice crystals significantly. • Including both factors improves simulations of ice cloud.• Cirrus cloud particle habit model of the MODIS collection 6 achieves better self-consistency and consistency with

A stochastic cloud model for cloud and ozone retrievals from UV measurements

International Nuclear Information System (INIS)

Efremenko, Dmitry S.; Schüssler, Olena; Doicu, Adrian; Loyola, Diego

2016-01-01

The new generation of satellite instruments provides measurements in and around the Oxygen A-band on a global basis and with a relatively high spatial resolution. These data are commonly used for the determination of cloud properties. A stochastic model and radiative transfer model, previously developed by the authors, is used as the forward model component in retrievals of cloud parameters and ozone total and partial columns. The cloud retrieval algorithm combines local and global optimization routines, and yields a retrieval accuracy of about 1% and a fast computational time. Retrieved parameters are the cloud optical thickness and the cloud-top height. It was found that the use of the independent pixel approximation instead of the stochastic cloud model leads to large errors in the retrieved cloud parameters, as well as, in the retrieved ozone height resolved partial columns. The latter can be reduced by using the stochastic cloud model to compute the optimal value of the regularization parameter in the framework of Tikhonov regularization. - Highlights: • A stochastic radiative transfer model for retrieving clouds/ozone is designed. • Errors of independent pixel approximation (IPA) for O3 total column are small. • The error of IPA for ozone profile retrieval may become large. • The use of stochastic model reduces the error of ozone profile retrieval.

Development and Testing of a Life Cycle Model and a Parameterization of Thin Mid-level Stratiform Clouds

Energy Technology Data Exchange (ETDEWEB)

Krueger, Steven K.

2008-03-03

We used a cloud-resolving model (a detailed computer model of cloud systems) to evaluate and improve the representation of clouds in global atmospheric models used for numerical weather prediction and climate modeling. We also used observations of the atmospheric state, including clouds, made at DOE's Atmospheric Radiation Measurement (ARM) Program's Climate Research Facility located in the Southern Great Plains (Kansas and Oklahoma) during Intensive Observation Periods to evaluate our detailed computer model as well as a single-column version of a global atmospheric model used for numerical weather prediction (the Global Forecast System of the NOAA National Centers for Environmental Prediction). This so-called Single-Column Modeling approach has proved to be a very effective method for testing the representation of clouds in global atmospheric models. The method relies on detailed observations of the atmospheric state, including clouds, in an atmospheric column comparable in size to a grid column used in a global atmospheric model. The required observations are made by a combination of in situ and remote sensing instruments. One of the greatest problems facing mankind at the present is climate change. Part of the problem is our limited ability to predict the regional patterns of climate change. In order to increase this ability, uncertainties in climate models must be reduced. One of the greatest of these uncertainties is the representation of clouds and cloud processes. This project, and ARM taken as a whole, has helped to improve the representation of clouds in global atmospheric models.

Multiview 3D sensing and analysis for high quality point cloud reconstruction

Science.gov (United States)

Satnik, Andrej; Izquierdo, Ebroul; Orjesek, Richard

2018-04-01

Multiview 3D reconstruction techniques enable digital reconstruction of 3D objects from the real world by fusing different viewpoints of the same object into a single 3D representation. This process is by no means trivial and the acquisition of high quality point cloud representations of dynamic 3D objects is still an open problem. In this paper, an approach for high fidelity 3D point cloud generation using low cost 3D sensing hardware is presented. The proposed approach runs in an efficient low-cost hardware setting based on several Kinect v2 scanners connected to a single PC. It performs autocalibration and runs in real-time exploiting an efficient composition of several filtering methods including Radius Outlier Removal (ROR), Weighted Median filter (WM) and Weighted Inter-Frame Average filtering (WIFA). The performance of the proposed method has been demonstrated through efficient acquisition of dense 3D point clouds of moving objects.

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

Science.gov (United States)

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

2017-09-15

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

Impacts of Subgrid Heterogeneous Mixing between Cloud Liquid and Ice on the Wegner-Bergeron-Findeisen Process and Mixed-phase Clouds in NCAR CAM5

Science.gov (United States)

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

2017-12-01

Mixed-phase clouds are persistently observed over the Arctic and the phase partitioning between cloud liquid and ice hydrometeors in mixed-phase clouds has important impacts on the surface energy budget and Arctic climate. In this study, we test the NCAR Community Atmosphere Model Version 5 (CAM5) with the single-column and weather forecast configurations and evaluate the model performance against observation data from the DOE Atmospheric Radiation Measurement (ARM) Program's M-PACE field campaign in October 2004 and long-term ground-based multi-sensor remote sensing measurements. Like most global climate models, we find that CAM5 also poorly simulates the phase partitioning in mixed-phase clouds by significantly underestimating the cloud liquid water content. Assuming pocket structures in the distribution of cloud liquid and ice in mixed-phase clouds as suggested by in situ observations provides a plausible solution to improve the model performance by reducing the Wegner-Bergeron-Findeisen (WBF) process rate. In this study, the modification of the WBF process in the CAM5 model has been achieved with applying a stochastic perturbation to the time scale of the WBF process relevant to both ice and snow to account for the heterogeneous mixture of cloud liquid and ice. Our results show that this modification of WBF process improves the modeled phase partitioning in the mixed-phase clouds. The seasonal variation of mixed-phase cloud properties is also better reproduced in the model in comparison with the long-term ground-based remote sensing observations. Furthermore, the phase partitioning is insensitive to the reassignment time step of perturbations.

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Directory of Open Access Journals (Sweden)

J. Strandgren

2017-11-01

Full Text Available Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3. Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is

Higher-fidelity yet efficient modeling of radiation energy transport through three-dimensional clouds

International Nuclear Information System (INIS)

Hall, M.L.; Davis, A.B.

2005-01-01

Accurate modeling of radiative energy transport through cloudy atmospheres is necessary for both climate modeling with GCMs (Global Climate Models) and remote sensing. Previous modeling efforts have taken advantage of extreme aspect ratios (cells that are very wide horizontally) by assuming a 1-D treatment vertically - the Independent Column Approximation (ICA). Recent attempts to resolve radiation transport through the clouds have drastically changed the aspect ratios of the cells, moving them closer to unity, such that the ICA model is no longer valid. We aim to provide a higher-fidelity atmospheric radiation transport model which increases accuracy while maintaining efficiency. To that end, this paper describes the development of an efficient 3-D-capable radiation code that can be easily integrated into cloud resolving models as an alternative to the resident 1-D model. Applications to test cases from the Intercomparison of 3-D Radiation Codes (I3RC) protocol are shown

Multi-scale Modeling of Arctic Clouds

Science.gov (United States)

Hillman, B. R.; Roesler, E. L.; Dexheimer, D.

2017-12-01

The presence and properties of clouds are critically important to the radiative budget in the Arctic, but clouds are notoriously difficult to represent in global climate models (GCMs). The challenge stems partly from a disconnect in the scales at which these models are formulated and the scale of the physical processes important to the formation of clouds (e.g., convection and turbulence). Because of this, these processes are parameterized in large-scale models. Over the past decades, new approaches have been explored in which a cloud system resolving model (CSRM), or in the extreme a large eddy simulation (LES), is embedded into each gridcell of a traditional GCM to replace the cloud and convective parameterizations to explicitly simulate more of these important processes. This approach is attractive in that it allows for more explicit simulation of small-scale processes while also allowing for interaction between the small and large-scale processes. The goal of this study is to quantify the performance of this framework in simulating Arctic clouds relative to a traditional global model, and to explore the limitations of such a framework using coordinated high-resolution (eddy-resolving) simulations. Simulations from the global model are compared with satellite retrievals of cloud fraction partioned by cloud phase from CALIPSO, and limited-area LES simulations are compared with ground-based and tethered-balloon measurements from the ARM Barrow and Oliktok Point measurement facilities.

Remote Sensing of Cloud Top Heights Using the Research Scanning Polarimeter

Science.gov (United States)

Sinclair, Kenneth; van Diedenhoven, Bastiaan; Cairns, Brian; Yorks, John; Wasilewski, Andrzej

2015-01-01

Clouds cover roughly two thirds of the globe and act as an important regulator of Earth's radiation budget. Of these, multilayered clouds occur about half of the time and are predominantly two-layered. Changes in cloud top height (CTH) have been predicted by models to have a globally averaged positive feedback, however observational changes in CTH have shown uncertain results. Additional CTH observations are necessary to better and quantify the effect. Improved CTH observations will also allow for improved sub-grid parameterizations in large-scale models and accurate CTH information is important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. RSP scans along the aircraft track and obtains measurements at 152 viewing angles at any aircraft location. The approach presented here aggregates measurements from multiple scans to a single location at cloud altitude using a correlation function designed to identify the location-distinct features in each scan. During NASAs SEAC4RS air campaign, the RSP was mounted on the ER-2 aircraft along with the Cloud Physics Lidar (CPL), which made simultaneous measurements of CTH. The RSPs unique method of determining CTH is presented. The capabilities of using single and combinations of channels within the approach are investigated. A detailed comparison of RSP retrieved CTHs with those of CPL reveal the accuracy of the approach. Results indicate a strong ability for the RSP to accurately identify cloud heights. Interestingly, the analysis reveals an ability for the approach to identify multiple cloud layers in a single scene and estimate the CTH of each layer. Capabilities and limitations of identifying single and multiple cloud layers heights are explored. Special focus is given to sources of error in the method including optically thin clouds, physically thick clouds, multi

Detecting Super-Thin Clouds With Polarized Light

Science.gov (United States)

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.

The Explicit-Cloud Parameterized-Pollutant hybrid approach for aerosol-cloud interactions in multiscale modeling framework models: tracer transport results

International Nuclear Information System (INIS)

Jr, William I Gustafson; Berg, Larry K; Easter, Richard C; Ghan, Steven J

2008-01-01

All estimates of aerosol indirect effects on the global energy balance have either completely neglected the influence of aerosol on convective clouds or treated the influence in a highly parameterized manner. Embedding cloud-resolving models (CRMs) within each grid cell of a global model provides a multiscale modeling framework for treating both the influence of aerosols on convective as well as stratiform clouds and the influence of clouds on the aerosol, but treating the interactions explicitly by simulating all aerosol processes in the CRM is computationally prohibitive. An alternate approach is to use horizontal statistics (e.g., cloud mass flux, cloud fraction, and precipitation) from the CRM simulation to drive a single-column parameterization of cloud effects on the aerosol and then use the aerosol profile to simulate aerosol effects on clouds within the CRM. Here, we present results from the first component of the Explicit-Cloud Parameterized-Pollutant parameterization to be developed, which handles vertical transport of tracers by clouds. A CRM with explicit tracer transport serves as a benchmark. We show that this parameterization, driven by the CRM's cloud mass fluxes, reproduces the CRM tracer transport significantly better than a single-column model that uses a conventional convective cloud parameterization

The Explicit-Cloud Parameterized-Pollutant hybrid approach for aerosol-cloud interactions in multiscale modeling framework models: tracer transport results

Energy Technology Data Exchange (ETDEWEB)

Jr, William I Gustafson; Berg, Larry K; Easter, Richard C; Ghan, Steven J [Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, PO Box 999, MSIN K9-30, Richland, WA (United States)], E-mail: William.Gustafson@pnl.gov

2008-04-15

All estimates of aerosol indirect effects on the global energy balance have either completely neglected the influence of aerosol on convective clouds or treated the influence in a highly parameterized manner. Embedding cloud-resolving models (CRMs) within each grid cell of a global model provides a multiscale modeling framework for treating both the influence of aerosols on convective as well as stratiform clouds and the influence of clouds on the aerosol, but treating the interactions explicitly by simulating all aerosol processes in the CRM is computationally prohibitive. An alternate approach is to use horizontal statistics (e.g., cloud mass flux, cloud fraction, and precipitation) from the CRM simulation to drive a single-column parameterization of cloud effects on the aerosol and then use the aerosol profile to simulate aerosol effects on clouds within the CRM. Here, we present results from the first component of the Explicit-Cloud Parameterized-Pollutant parameterization to be developed, which handles vertical transport of tracers by clouds. A CRM with explicit tracer transport serves as a benchmark. We show that this parameterization, driven by the CRM's cloud mass fluxes, reproduces the CRM tracer transport significantly better than a single-column model that uses a conventional convective cloud parameterization.

Enterprise Cloud Adoption - Cloud Maturity Assessment Model

OpenAIRE

Conway, Gerry; Doherty, Eileen; Carcary, Marian; Crowley, Catherine

2017-01-01

The introduction and use of cloud computing by an organization has the promise of significant benefits that include reduced costs, improved services, and a pay-per-use model. Organizations that successfully harness these benefits will potentially have a distinct competitive edge, due to their increased agility and flexibility to rapidly respond to an ever changing and complex business environment. However, as cloud technology is a relatively new ph...

Determining Best Estimates and Uncertainties in Cloud Microphysical Parameters from ARM Field Data: Implications for Models, Retrieval Schemes and Aerosol-Cloud-Radiation Interactions

Energy Technology Data Exchange (ETDEWEB)

McFarquhar, Greg [Univ. of Illinois, Urbana, IL (United States)

2015-12-28

We proposed to analyze in-situ cloud data collected during ARM/ASR field campaigns to create databases of cloud microphysical properties and their uncertainties as needed for the development of improved cloud parameterizations for models and remote sensing retrievals, and for evaluation of model simulations and retrievals. In particular, we proposed to analyze data collected over the Southern Great Plains (SGP) during the Mid-latitude Continental Convective Clouds Experiment (MC3E), the Storm Peak Laboratory Cloud Property Validation Experiment (STORMVEX), the Small Particles in Cirrus (SPARTICUS) Experiment and the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign, over the North Slope of Alaska during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE), and over the Tropical Western Pacific (TWP) during The Tropical Warm Pool International Cloud Experiment (TWP-ICE), to meet the following 3 objectives; derive statistical databases of single ice particle properties (aspect ratio AR, dominant habit, mass, projected area) and distributions of ice crystals (size distributions SDs, mass-dimension m-D, area-dimension A-D relations, mass-weighted fall speeds, single-scattering properties, total concentrations N, ice mass contents IWC), complete with uncertainty estimates; assess processes by which aerosols modulate cloud properties in arctic stratus and mid-latitude cumuli, and quantify aerosol’s influence in context of varying meteorological and surface conditions; and determine how ice cloud microphysical, single-scattering and fall-out properties and contributions of small ice crystals to such properties vary according to location, environment, surface, meteorological and aerosol conditions, and develop parameterizations of such effects.In this report we describe the accomplishments that we made on all 3 research objectives.

A Categorisation of Cloud Computing Business Models

OpenAIRE

Chang, Victor; Bacigalupo, David; Wills, Gary; De Roure, David

2010-01-01

This paper reviews current cloud computing business models and presents proposals on how organisations can achieve sustainability by adopting appropriate models. We classify cloud computing business models into eight types: (1) Service Provider and Service Orientation; (2) Support and Services Contracts; (3) In-House Private Clouds; (4) All-In-One Enterprise Cloud; (5) One-Stop Resources and Services; (6) Government funding; (7) Venture Capitals; and (8) Entertainment and Social Networking. U...

A Review of Cloud Business Models and Sustainability

OpenAIRE

Chang, Victor; Wills, Gary; De Roure, David

2010-01-01

This paper reviews current cloud computing business models and presents proposals on how organisations can achieve sustainability by adopting appropriate models. Using the Jericho Forum's Cloud Cube Model (CCM), we classify cloud computing business models into eight types: (1) Service Provider and Service Orientation; (2) Support and Services Contracts; (3) In-House Private Clouds; (4) All-In-One Enterprise Cloud; (5) One-Stop Resources and Services; (6) Government Funding; (7) Venture Capita...

Combined observational and modeling efforts of aerosol-cloud-precipitation interactions over Southeast Asia

Science.gov (United States)

Loftus, Adrian; Tsay, Si-Chee; Nguyen, Xuan Anh

2016-04-01

droplet size and number concentration, but also the spectral width of the cloud droplet size distribution, the 3M scheme is well suited to simulate aerosol-cloud-precipitation interactions within a three-dimensional regional cloud model. Moreover, the additional variability predicted on the hydrometeor distributions provides beneficial input for forward models to link the simulated microphysical processes with observations as well as to assess both ground-based and satellite retrieval methods. In this presentation, we provide an overview of the 7 South East Asian Studies / Biomass-burning Aerosols and Stratocumulus Environment: Lifecycles and Interactions Experiment (7-SEAS/BASELInE) operations during the spring of 2013. Preliminary analyses of pre-monsoon Sc system lifecycles observed during the first-ever deployment of a ground-based cloud radar to northern Vietnam will be also be presented. Initial results from GCE model simulations of these Sc using double-moment and the new 3M bulk microphysics schemes under various aerosol loadings will be used to showcase the 3M scheme as well as provide insight into how the impact of aerosols on cloud and precipitation processes in stratocumulus over land may manifest themselves in simulated remote-sensing signals. Applications and future work involving ongoing 7-SEAS campaigns aimed at improving our understanding of aerosol-cloud-precipitation interactions of will also be discussed.

Modeling of clouds and radiation for development of parameterizations for general circulation models

International Nuclear Information System (INIS)

Westphal, D.; Toon, B.; Jensen, E.; Kinne, S.; Ackerman, A.; Bergstrom, R.; Walker, A.

1994-01-01

Atmospheric Radiation Measurement (ARM) Program research at NASA Ames Research Center (ARC) includes radiative transfer modeling, cirrus cloud microphysics, and stratus cloud modeling. These efforts are designed to provide the basis for improving cloud and radiation parameterizations in our main effort: mesoscale cloud modeling. The range of non-convective cloud models used by the ARM modeling community can be crudely categorized based on the number of predicted hydrometers such as cloud water, ice water, rain, snow, graupel, etc. The simplest model has no predicted hydrometers and diagnoses the presence of clouds based on the predicted relative humidity. The vast majority of cloud models have two or more predictive bulk hydrometers and are termed either bulk water (BW) or size-resolving (SR) schemes. This study compares the various cloud models within the same dynamical framework, and compares results with observations rather than climate statistics

Assessment of the accuracy of the conventional ray-tracing technique: Implications in remote sensing and radiative transfer involving ice clouds

International Nuclear Information System (INIS)

Bi, Lei; Yang, Ping; Liu, Chao; Yi, Bingqi; Baum, Bryan A.; Diedenhoven, Bastiaan van; Iwabuchi, Hironobu

2014-01-01

A fundamental problem in remote sensing and radiative transfer simulations involving ice clouds is the ability to compute accurate optical properties for individual ice particles. While relatively simple and intuitively appealing, the conventional geometric-optics method (CGOM) is used frequently for the solution of light scattering by ice crystals. Due to the approximations in the ray-tracing technique, the CGOM accuracy is not well quantified. The result is that the uncertainties are introduced that can impact many applications. Improvements in the Invariant Imbedding T-matrix method (II-TM) and the Improved Geometric-Optics Method (IGOM) provide a mechanism to assess the aforementioned uncertainties. The results computed by the II-TM+IGOM are considered as a benchmark because the II-TM solves Maxwell's equations from first principles and is applicable to particle size parameters ranging into the domain at which the IGOM has reasonable accuracy. To assess the uncertainties with the CGOM in remote sensing and radiative transfer simulations, two independent optical property datasets of hexagonal columns are developed for sensitivity studies by using the CGOM and the II-TM+IGOM, respectively. Ice cloud bulk optical properties obtained from the two datasets are compared and subsequently applied to retrieve the optical thickness and effective diameter from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Additionally, the bulk optical properties are tested in broadband radiative transfer (RT) simulations using the general circulation model (GCM) version of the Rapid Radiative Transfer Model (RRTMG) that is adopted in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM, version 5.1). For MODIS retrievals, the mean bias of uncertainties of applying the CGOM in shortwave bands (0.86 and 2.13 μm) can be up to 5% in the optical thickness and as high as 20% in the effective diameter, depending on cloud optical

A Condensation–coalescence Cloud Model for Exoplanetary Atmospheres: Formulation and Test Applications to Terrestrial and Jovian Clouds

Energy Technology Data Exchange (ETDEWEB)

Ohno, Kazumasa; Okuzumi, Satoshi [Department of Earth and Planetary Sciences, Tokyo Institute of Technology, Meguro, Tokyo 152-8551 (Japan)

2017-02-01

A number of transiting exoplanets have featureless transmission spectra that might suggest the presence of clouds at high altitudes. A realistic cloud model is necessary to understand the atmospheric conditions under which such high-altitude clouds can form. In this study, we present a new cloud model that takes into account the microphysics of both condensation and coalescence. Our model provides the vertical profiles of the size and density of cloud and rain particles in an updraft for a given set of physical parameters, including the updraft velocity and the number density of cloud condensation nuclei (CCNs). We test our model by comparing with observations of trade-wind cumuli on Earth and ammonia ice clouds in Jupiter. For trade-wind cumuli, the model including both condensation and coalescence gives predictions that are consistent with observations, while the model including only condensation overestimates the mass density of cloud droplets by up to an order of magnitude. For Jovian ammonia clouds, the condensation–coalescence model simultaneously reproduces the effective particle radius, cloud optical thickness, and cloud geometric thickness inferred from Voyager observations if the updraft velocity and CCN number density are taken to be consistent with the results of moist convection simulations and Galileo probe measurements, respectively. These results suggest that the coalescence of condensate particles is important not only in terrestrial water clouds but also in Jovian ice clouds. Our model will be useful to understand how the dynamics, compositions, and nucleation processes in exoplanetary atmospheres affect the vertical extent and optical thickness of exoplanetary clouds via cloud microphysics.

A Condensation–coalescence Cloud Model for Exoplanetary Atmospheres: Formulation and Test Applications to Terrestrial and Jovian Clouds

International Nuclear Information System (INIS)

Ohno, Kazumasa; Okuzumi, Satoshi

2017-01-01

A number of transiting exoplanets have featureless transmission spectra that might suggest the presence of clouds at high altitudes. A realistic cloud model is necessary to understand the atmospheric conditions under which such high-altitude clouds can form. In this study, we present a new cloud model that takes into account the microphysics of both condensation and coalescence. Our model provides the vertical profiles of the size and density of cloud and rain particles in an updraft for a given set of physical parameters, including the updraft velocity and the number density of cloud condensation nuclei (CCNs). We test our model by comparing with observations of trade-wind cumuli on Earth and ammonia ice clouds in Jupiter. For trade-wind cumuli, the model including both condensation and coalescence gives predictions that are consistent with observations, while the model including only condensation overestimates the mass density of cloud droplets by up to an order of magnitude. For Jovian ammonia clouds, the condensation–coalescence model simultaneously reproduces the effective particle radius, cloud optical thickness, and cloud geometric thickness inferred from Voyager observations if the updraft velocity and CCN number density are taken to be consistent with the results of moist convection simulations and Galileo probe measurements, respectively. These results suggest that the coalescence of condensate particles is important not only in terrestrial water clouds but also in Jovian ice clouds. Our model will be useful to understand how the dynamics, compositions, and nucleation processes in exoplanetary atmospheres affect the vertical extent and optical thickness of exoplanetary clouds via cloud microphysics.

Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part II: Multi-layered cloud

Energy Technology Data Exchange (ETDEWEB)

Morrison, H; McCoy, R B; Klein, S A; Xie, S; Luo, Y; Avramov, A; Chen, M; Cole, J; Falk, M; Foster, M; Genio, A D; Harrington, J; Hoose, C; Khairoutdinov, M; Larson, V; Liu, X; McFarquhar, G; Poellot, M; Shipway, B; Shupe, M; Sud, Y; Turner, D; Veron, D; Walker, G; Wang, Z; Wolf, A; Xu, K; Yang, F; Zhang, G

2008-02-27

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a deep, multi-layered, mixed-phase cloud system observed during the ARM Mixed-Phase Arctic Cloud Experiment. This cloud system was associated with strong surface turbulent sensible and latent heat fluxes as cold air flowed over the open Arctic Ocean, combined with a low pressure system that supplied moisture at mid-level. The simulations, performed by 13 single-column and 4 cloud-resolving models, generally overestimate the liquid water path and strongly underestimate the ice water path, although there is a large spread among the models. This finding is in contrast with results for the single-layer, low-level mixed-phase stratocumulus case in Part I of this study, as well as previous studies of shallow mixed-phase Arctic clouds, that showed an underprediction of liquid water path. The overestimate of liquid water path and underestimate of ice water path occur primarily when deeper mixed-phase clouds extending into the mid-troposphere were observed. These results suggest important differences in the ability of models to simulate Arctic mixed-phase clouds that are deep and multi-layered versus shallow and single-layered. In general, models with a more sophisticated, two-moment treatment of the cloud microphysics produce a somewhat smaller liquid water path that is closer to observations. The cloud-resolving models tend to produce a larger cloud fraction than the single-column models. The liquid water path and especially the cloud fraction have a large impact on the cloud radiative forcing at the surface, which is dominated by the longwave flux for this case.

Model for Semantically Rich Point Cloud Data

Science.gov (United States)

Poux, F.; Neuville, R.; Hallot, P.; Billen, R.

2017-10-01

This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

F. Poux

2017-10-01

Full Text Available This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

Submm-Wave Radiometry for Cloud/Humidity/Precipitation Sciences

Science.gov (United States)

Wu, Dong L.

2011-01-01

Although active sensors can provide cloud profiles at good vertical resolution, clouds are often coupled with dynamics to form fast and organized structures. Lack of understanding of these organized systems leads to great challenge for numerical models. The deficiency is partly reflected, for example, in poorly modeled intraseasonal variations (e.g., MJD). Remote sensing clouds in the middle and upper troposphere has been challenging from space. Vis/IR sensors are sensitive to the topmost cloud layers whereas low-frequency MW techniques are sensitivity to liquid and precipitation at the bottom of cloud layers. The middle-level clouds, mostly in the ice phase, require a sensor that has moderate penetration and sensitivity to cloud scattering, in order to measure cloud water content. Sensors at submm wavelengths provide promising sensitivity and coverage with the spatial resolution needed to measure cloud water content floating in the upper air. In addition, submm-wave sensors are able to provide better measurements of upper-tropospheric humidity than traditional microwave instruments.

Impact of cloud microphysics on cloud-radiation interactions in the CSU general circulation model

Energy Technology Data Exchange (ETDEWEB)

Fowler, L.D.; Randall, D.A.

1995-04-01

Our ability to study and quantify the impact of cloud-radiation interactions in studying global scale climate variations strongly relies upon the ability of general circulation models (GCMs) to simulate the coupling between the spatial and temporal variations of the model-generated cloudiness and atmospheric moisture budget components. In particular, the ability of GCMs to reproduce the geographical distribution of the sources and sinks of the planetary radiation balance depends upon their representation of the formation and dissipation of cloudiness in conjunction with cloud microphysics processes, and the fractional amount and optical characteristics of cloudiness in conjunction with the mass of condensate stored in the atmosphere. A cloud microphysics package which encompasses five prognostic variables for the mass of water vapor, cloud water, cloud ice, rain, and snow has been implemented in the Colorado State University General Circulation Model (CSU GCM) to simulate large-scale condensation processes. Convection interacts with the large-scale environment through the detrainment of cloud water and cloud ice at the top of cumulus towers. The cloud infrared emissivity and cloud optical depth of the model-generated cloudiness are interactive and depend upon the mass of cloud water and cloud ice suspended in the atmosphere. The global atmospheric moisture budget and planetary radiation budget of the CSU GCM obtained from a perpetual January simulation are discussed. Geographical distributions of the atmospheric moisture species are presented. Global maps of the top-of-atmosphere outgoing longwave radiation and planetary albedo are compared against Earth Radiation Budget Experiment (ERBE) satellite data.

Security model for VM in cloud

Science.gov (United States)

Kanaparti, Venkataramana; Naveen K., R.; Rajani, S.; Padmvathamma, M.; Anitha, C.

2013-03-01

Cloud computing is a new approach emerged to meet ever-increasing demand for computing resources and to reduce operational costs and Capital Expenditure for IT services. As this new way of computation allows data and applications to be stored away from own corporate server, it brings more issues in security such as virtualization security, distributed computing, application security, identity management, access control and authentication. Even though Virtualization forms the basis for cloud computing it poses many threats in securing cloud. As most of Security threats lies at Virtualization layer in cloud we proposed this new Security Model for Virtual Machine in Cloud (SMVC) in which every process is authenticated by Trusted-Agent (TA) in Hypervisor as well as in VM. Our proposed model is designed to with-stand attacks by unauthorized process that pose threat to applications related to Data Mining, OLAP systems, Image processing which requires huge resources in cloud deployed on one or more VM's.

Architecting the cloud design decisions for cloud computing service models (SaaS, PaaS, and IaaS)

CERN Document Server

Kavis, Michael J

2014-01-01

An expert guide to selecting the right cloud service model for your business Cloud computing is all the rage, allowing for the delivery of computing and storage capacity to a diverse community of end-recipients. However, before you can decide on a cloud model, you need to determine what the ideal cloud service model is for your business. Helping you cut through all the haze, Architecting the Cloud is vendor neutral and guides you in making one of the most critical technology decisions that you will face: selecting the right cloud service model(s) based on a combination of both business and tec

Modelling ice microphysics of mixed-phase clouds

Science.gov (United States)

Ahola, J.; Raatikainen, T.; Tonttila, J.; Romakkaniemi, S.; Kokkola, H.; Korhonen, H.

2017-12-01

The low-level Arctic mixed-phase clouds have a significant role for the Arctic climate due to their ability to absorb and reflect radiation. Since the climate change is amplified in polar areas, it is vital to apprehend the mixed-phase cloud processes. From a modelling point of view, this requires a high spatiotemporal resolution to capture turbulence and the relevant microphysical processes, which has shown to be difficult.In order to solve this problem about modelling mixed-phase clouds, a new ice microphysics description has been developed. The recently published large-eddy simulation cloud model UCLALES-SALSA offers a good base for a feasible solution (Tonttila et al., Geosci. Mod. Dev., 10:169-188, 2017). The model includes aerosol-cloud interactions described with a sectional SALSA module (Kokkola et al., Atmos. Chem. Phys., 8, 2469-2483, 2008), which represents a good compromise between detail and computational expense.Newly, the SALSA module has been upgraded to include also ice microphysics. The dynamical part of the model is based on well-known UCLA-LES model (Stevens et al., J. Atmos. Sci., 56, 3963-3984, 1999) which can be used to study cloud dynamics on a fine grid.The microphysical description of ice is sectional and the included processes consist of formation, growth and removal of ice and snow particles. Ice cloud particles are formed by parameterized homo- or heterogeneous nucleation. The growth mechanisms of ice particles and snow include coagulation and condensation of water vapor. Autoconversion from cloud ice particles to snow is parameterized. The removal of ice particles and snow happens by sedimentation and melting.The implementation of ice microphysics is tested by initializing the cloud simulation with atmospheric observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC). The results are compared to the model results shown in the paper of Ovchinnikov et al. (J. Adv. Model. Earth Syst., 6, 223-248, 2014) and they show a good

Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds

Science.gov (United States)

Siler, Nicholas; Po-Chedley, Stephen; Bretherton, Christopher S.

2018-02-01

Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO_2 (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (α) in simulations of the current climate: high-feedback models exhibit lower (higher) α in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean α as temperatures rise and warm regions expand. The spatial pattern of α is found to be strongly predictive (r=0.84) of a model's global cloud feedback, with satellite observations indicating a most-likely value of 0.58± 0.31 Wm^{-2} K^{-1} (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm^{-2} K^{-1}, with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate.

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm.

Science.gov (United States)

Yang, Mengzhao; Song, Wei; Mei, Haibin

2017-07-23

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient.

Parameterization of clouds and radiation in climate models

Energy Technology Data Exchange (ETDEWEB)

Roeckner, E. [Max Planck Institute for Meterology, Hamburg (Germany)

1995-09-01

Clouds are a very important, yet poorly modeled element in the climate system. There are many potential cloud feedbacks, including those related to cloud cover, height, water content, phase change, and droplet concentration and size distribution. As a prerequisite to studying the cloud feedback issue, this research reports on the simulation and validation of cloud radiative forcing under present climate conditions using the ECHAM general circulation model and ERBE top-of-atmosphere radiative fluxes.

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Science.gov (United States)

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

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

Science.gov (United States)

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

Modeling microwave/electron-cloud interaction

International Nuclear Information System (INIS)

Mattes, M; Sorolla, E; Zimmermann, F

2013-01-01

Starting from the separate codes BI-RME and ECLOUD or PyECLOUD, we are developing a novel joint simulation tool, which models the combined effect of a charged particle beam and of microwaves on an electron cloud. Possible applications include the degradation of microwave transmission in telecommunication satellites by electron clouds; the microwave-transmission techniques being used in particle accelerators for the purpose of electroncloud diagnostics; the microwave emission by the electron cloud itself in the presence of a magnetic field; and the possible suppression of electron-cloud formation in an accelerator by injecting microwaves of suitable amplitude and frequency. A few early simulation results are presented. (author)

New photoionization models of intergalactic clouds

Science.gov (United States)

Donahue, Megan; Shull, J. M.

1991-01-01

New photoionization models of optically thin low-density intergalactic gas at constant pressure, photoionized by QSOs, are presented. All ion stages of H, He, C, N, O, Si, and Fe, plus H2 are modeled, and the column density ratios of clouds at specified values of the ionization parameter of n sub gamma/n sub H and cloud metallicity are predicted. If Ly-alpha clouds are much cooler than the previously assumed value, 30,000 K, the ionization parameter must be very low, even with the cooling contribution of a trace component of molecules. If the clouds cool below 6000 K, their final equilibrium must be below 3000 K, owing to the lack of a stable phase between 6000 and 3000 K. If it is assumed that the clouds are being irradiated by an EUV power-law continuum typical of WSOs, with J0 = 10 exp -21 ergs/s sq cm Hz, typical cloud thicknesses along the line of sight that are much smaller than would be expected from shocks, thermal instabilities, or gravitational collapse are derived.

A stratiform cloud parameterization for general circulation models

International Nuclear Information System (INIS)

Ghan, S.J.; Leung, L.R.; Chuang, C.C.; Penner, J.E.; McCaa, J.

1994-01-01

The crude treatment of clouds in general circulation models (GCMs) is widely recognized as a major limitation in applying these models to predictions of global climate change. The purpose of this project is to develop in GCMs a stratiform cloud parameterization that expresses clouds in terms of bulk microphysical properties and their subgrid variability. Various clouds variables and their interactions are summarized. Precipitating cloud species are distinguished from non-precipitating species, and the liquid phase is distinguished from the ice phase. The size of the non-precipitating cloud particles (which influences both the cloud radiative properties and the conversion of non-precipitating cloud species to precipitating species) is determined by predicting both the mass and number concentrations of each species

Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System

Directory of Open Access Journals (Sweden)

Shih-Hao Chang

2016-01-01

Full Text Available Mobile crowd sensing (MCS arises as a new sensing paradigm, which leverages citizens for large-scale sensing by various mobile devices to efficiently collect and share local information. Unlike other MCS application challenges that consider user privacy and data trustworthiness, this study focuses on the network trustworthiness problem, namely, Sybil attacks in MCS network. The Sybil attack in computer security is a type of security attack, which illegally forges multiple identities in peer-to-peer networks, namely, Sybil identities. These Sybil identities will falsify multiple identities that negatively influence the effectiveness of sensing data in this MCS network or degrading entire network performance. To cope with this problem, a cloud based trust management scheme (CbTMS was proposed to detect Sybil attacks in the MCS network. The CbTMS was proffered for performing active and passive checking scheme, in addition to the mobile PCS trustworthiness management, and includes a decision tree algorithm, to verify the covered nodes in the MCS network. Simulation studies show that our CbTMS can efficiently detect the malicious Sybil nodes in the network and cause 6.87 Wh power reduction compared with other malicious Sybil node attack mode.

Farm Management Support on Cloud Computing Platform: A System for Cropland Monitoring Using Multi-Source Remotely Sensed Data

Science.gov (United States)

Coburn, C. A.; Qin, Y.; Zhang, J.; Staenz, K.

2015-12-01

Food security is one of the most pressing issues facing humankind. Recent estimates predict that over one billion people don't have enough food to meet their basic nutritional needs. The ability of remote sensing tools to monitor and model crop production and predict crop yield is essential for providing governments and farmers with vital information to ensure food security. Google Earth Engine (GEE) is a cloud computing platform, which integrates storage and processing algorithms for massive remotely sensed imagery and vector data sets. By providing the capabilities of storing and analyzing the data sets, it provides an ideal platform for the development of advanced analytic tools for extracting key variables used in regional and national food security systems. With the high performance computing and storing capabilities of GEE, a cloud-computing based system for near real-time crop land monitoring was developed using multi-source remotely sensed data over large areas. The system is able to process and visualize the MODIS time series NDVI profile in conjunction with Landsat 8 image segmentation for crop monitoring. With multi-temporal Landsat 8 imagery, the crop fields are extracted using the image segmentation algorithm developed by Baatz et al.[1]. The MODIS time series NDVI data are modeled by TIMESAT [2], a software package developed for analyzing time series of satellite data. The seasonality of MODIS time series data, for example, the start date of the growing season, length of growing season, and NDVI peak at a field-level are obtained for evaluating the crop-growth conditions. The system fuses MODIS time series NDVI data and Landsat 8 imagery to provide information of near real-time crop-growth conditions through the visualization of MODIS NDVI time series and comparison of multi-year NDVI profiles. Stakeholders, i.e., farmers and government officers, are able to obtain crop-growth information at crop-field level online. This unique utilization of GEE in

Development of a cloud microphysical model and parameterizations to describe the effect of CCN on warm cloud

Directory of Open Access Journals (Sweden)

N. Kuba

2006-01-01

Full Text Available First, a hybrid cloud microphysical model was developed that incorporates both Lagrangian and Eulerian frameworks to study quantitatively the effect of cloud condensation nuclei (CCN on the precipitation of warm clouds. A parcel model and a grid model comprise the cloud model. The condensation growth of CCN in each parcel is estimated in a Lagrangian framework. Changes in cloud droplet size distribution arising from condensation and coalescence are calculated on grid points using a two-moment bin method in a semi-Lagrangian framework. Sedimentation and advection are estimated in the Eulerian framework between grid points. Results from the cloud model show that an increase in the number of CCN affects both the amount and the area of precipitation. Additionally, results from the hybrid microphysical model and Kessler's parameterization were compared. Second, new parameterizations were developed that estimate the number and size distribution of cloud droplets given the updraft velocity and the number of CCN. The parameterizations were derived from the results of numerous numerical experiments that used the cloud microphysical parcel model. The input information of CCN for these parameterizations is only several values of CCN spectrum (they are given by CCN counter for example. It is more convenient than conventional parameterizations those need values concerned with CCN spectrum, C and k in the equation of N=CSk, or, breadth, total number and median radius, for example. The new parameterizations' predictions of initial cloud droplet size distribution for the bin method were verified by using the aforesaid hybrid microphysical model. The newly developed parameterizations will save computing time, and can effectively approximate components of cloud microphysics in a non-hydrostatic cloud model. The parameterizations are useful not only in the bin method in the regional cloud-resolving model but also both for a two-moment bulk microphysical model and

A stratiform cloud parameterization for General Circulation Models

International Nuclear Information System (INIS)

Ghan, S.J.; Leung, L.R.; Chuang, C.C.; Penner, J.E.; McCaa, J.

1994-01-01

The crude treatment of clouds in General Circulation Models (GCMs) is widely recognized as a major limitation in the application of these models to predictions of global climate change. The purpose of this project is to develop a paxameterization for stratiform clouds in GCMs that expresses stratiform clouds in terms of bulk microphysical properties and their subgrid variability. In this parameterization, precipitating cloud species are distinguished from non-precipitating species, and the liquid phase is distinguished from the ice phase. The size of the non-precipitating cloud particles (which influences both the cloud radiative properties and the conversion of non-precipitating cloud species to precipitating species) is determined by predicting both the mass and number concentrations of each species

A multi-year data set on aerosol-cloud-precipitation-meteorology interactions for marine stratocumulus clouds.

Science.gov (United States)

Sorooshian, Armin; MacDonald, Alexander B; Dadashazar, Hossein; Bates, Kelvin H; Coggon, Matthew M; Craven, Jill S; Crosbie, Ewan; Hersey, Scott P; Hodas, Natasha; Lin, Jack J; Negrón Marty, Arnaldo; Maudlin, Lindsay C; Metcalf, Andrew R; Murphy, Shane M; Padró, Luz T; Prabhakar, Gouri; Rissman, Tracey A; Shingler, Taylor; Varutbangkul, Varuntida; Wang, Zhen; Woods, Roy K; Chuang, Patrick Y; Nenes, Athanasios; Jonsson, Haflidi H; Flagan, Richard C; Seinfeld, John H

2018-02-27

Airborne measurements of meteorological, aerosol, and stratocumulus cloud properties have been harmonized from six field campaigns during July-August months between 2005 and 2016 off the California coast. A consistent set of core instruments was deployed on the Center for Interdisciplinary Remotely-Piloted Aircraft Studies Twin Otter for 113 flight days, amounting to 514 flight hours. A unique aspect of the compiled data set is detailed measurements of aerosol microphysical properties (size distribution, composition, bioaerosol detection, hygroscopicity, optical), cloud water composition, and different sampling inlets to distinguish between clear air aerosol, interstitial in-cloud aerosol, and droplet residual particles in cloud. Measurements and data analysis follow documented methods for quality assurance. The data set is suitable for studies associated with aerosol-cloud-precipitation-meteorology-radiation interactions, especially owing to sharp aerosol perturbations from ship traffic and biomass burning. The data set can be used for model initialization and synergistic application with meteorological models and remote sensing data to improve understanding of the very interactions that comprise the largest uncertainty in the effect of anthropogenic emissions on radiative forcing.

Trust Model to Enhance Security and Interoperability of Cloud Environment

Science.gov (United States)

Li, Wenjuan; Ping, Lingdi

Trust is one of the most important means to improve security and enable interoperability of current heterogeneous independent cloud platforms. This paper first analyzed several trust models used in large and distributed environment and then introduced a novel cloud trust model to solve security issues in cross-clouds environment in which cloud customer can choose different providers' services and resources in heterogeneous domains can cooperate. The model is domain-based. It divides one cloud provider's resource nodes into the same domain and sets trust agent. It distinguishes two different roles cloud customer and cloud server and designs different strategies for them. In our model, trust recommendation is treated as one type of cloud services just like computation or storage. The model achieves both identity authentication and behavior authentication. The results of emulation experiments show that the proposed model can efficiently and safely construct trust relationship in cross-clouds environment.

Business model elements impacting cloud computing adoption

DEFF Research Database (Denmark)

Bogataj, Kristina; Pucihar, Andreja; Sudzina, Frantisek

The paper presents a proposed research framework for identification of business model elements impacting Cloud Computing Adoption. We provide a definition of main Cloud Computing characteristics, discuss previous findings on factors impacting Cloud Computing Adoption, and investigate technology a...

Validation of Satellite Derived Cloud Properties Over the Southeastern Pacific

Science.gov (United States)

Ayers, J.; Minnis, P.; Zuidema, P.; Sun-Mack, S.; Palikonda, R.; Nguyen, L.; Fairall, C.

2005-12-01

Satellite measurements of cloud properties and the radiation budget are essential for understanding meso- and large-scale processes that determine the variability in climate over the southeastern Pacific. Of particular interest in this region is the prevalent stratocumulus cloud deck. The stratocumulus albedos are directly related to cloud microphysical properties that need to be accurately characterized in Global Climate Models (GCMs) to properly estimate the Earth's radiation budget. Meteorological observations in this region are sparse causing large uncertainties in initialized model fields. Remote sensing from satellites can provide a wealth of information about the clouds in this region, but it is vital to validate the remotely sensed parameters and to understand their relationship to other parameters that are not directly observed by the satellites. The variety of measurements from the R/V Roger Revelle during the 2003 STRATUS cruise and from the R/V Ron Brown during EPIC 2001 and the 2004 STRATUS cruises are suitable for validating and improving the interpretation of the satellite derived cloud properties. In this study, satellite-derived cloud properties including coverage, height, optical depth, and liquid water path are compared with in situ measurements taken during the EPIC and STRATUS cruises. The remotely sensed values are derived from Geostationary Operational Environmental Satellite (GOES) imager data, Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites, and from the Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The products from this study will include regional monthly cloud climatologies derived from the GOES data for the 2003 and 2004 cruises as well as micro and macro physical cloud property retrievals centered over the ship tracks from MODIS and VIRS.

Model Based Business and IT Cloud Alignment as a Cloud Offering

OpenAIRE

Robert Woitsch; Wilfrid Utz

2015-01-01

Cloud computing proved to offer flexible IT solutions. Although large enterprises may benefit from this technology by educating their IT departments, SMEs are dramatically falling behind in cloud usage and hence lose the ability to efficiently adapt their IT to their business needs. This paper introduces the project idea of the H2020 project CloudSocket, by elaborating the idea of Business Processes as a Service, where concept models and semantics are applied to align business pro...

Macroscopic modelization of the cloud elasticity*

Directory of Open Access Journals (Sweden)

Etancelin J.-M.

2013-12-01

Full Text Available In order to achieve its promise of providing information technologies (IT on demand, cloud computing needs to rely on a mathematical model capable of directing IT on and off according to a demand pattern to provide a true elasticity. This article provides a first method to reach this goal using a “fluid type” partial differential equations model. On the one hand it examines the question of service time optimization for the simultaneous satisfaction of the cloud consumer and provider. On the other hand it tries to model a way to deliver resources according to the real time capacity of the cloud that depends on parameters such as burst requests and application timeouts. All these questions are illustrated via an implicit finite volume scheme.

Quantifying uncertainties in radar forward models through a comparison between CloudSat and SPartICus reflectivity factors

Science.gov (United States)

Mascio, Jeana; Mace, Gerald G.

2017-02-01

Interpretations of remote sensing measurements collected in sample volumes containing ice-phase hydrometeors are very sensitive to assumptions regarding the distributions of mass with ice crystal dimension, otherwise known as mass-dimensional or m-D relationships. How these microphysical characteristics vary in nature is highly uncertain, resulting in significant uncertainty in algorithms that attempt to derive bulk microphysical properties from remote sensing measurements. This uncertainty extends to radar reflectivity factors forward calculated from model output because the statistics of the actual m-D in nature is not known. To investigate the variability in m-D relationships in cirrus clouds, reflectivity factors measured by CloudSat are combined with particle size distributions (PSDs) collected by coincident in situ aircraft by using an optimal estimation-based (OE) retrieval of the m-D power law. The PSDs were collected by 12 flights of the Stratton Park Engineering Company Learjet during the Small Particles in Cirrus campaign. We find that no specific habit emerges as preferred, and instead, we find that the microphysical characteristics of ice crystal populations tend to be distributed over a continuum-defying simple categorization. With the uncertainties derived from the OE algorithm, the uncertainties in forward-modeled backscatter cross section and, in turn, radar reflectivity is calculated by using a bootstrapping technique, allowing us to infer the uncertainties in forward-modeled radar reflectivity that would be appropriately applied to remote sensing simulator algorithms.

Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud

Energy Technology Data Exchange (ETDEWEB)

Klein, S A; McCoy, R B; Morrison, H; Ackerman, A; Avramov, A; deBoer, G; Chen, M; Cole, J; DelGenio, A; Golaz, J; Hashino, T; Harrington, J; Hoose, C; Khairoutdinov, M; Larson, V; Liu, X; Luo, Y; McFarquhar, G; Menon, S; Neggers, R; Park, S; Poellot, M; von Salzen, K; Schmidt, J; Sednev, I; Shipway, B; Shupe, M; Spangenberg, D; Sud, Y; Turner, D; Veron, D; Falk, M; Foster, M; Fridlind, A; Walker, G; Wang, Z; Wolf, A; Xie, S; Xu, K; Yang, F; Zhang, G

2008-02-27

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud top temperature of -15 C. The observed liquid water path of around 160 g m{sup -2} was about two-thirds of the adiabatic value and much greater than the mass of ice crystal precipitation which when integrated from the surface to cloud top was around 15 g m{sup -2}. The simulations were performed by seventeen single-column models (SCMs) and nine cloud-resolving models (CRMs). While the simulated ice water path is generally consistent with the observed values, the median SCM and CRM liquid water path is a factor of three smaller than observed. Results from a sensitivity study in which models removed ice microphysics indicate that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this general underestimate, the simulated liquid and ice water paths of several models are consistent with the observed values. Furthermore, there is some evidence that models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter is also present. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics. This case study, which has been well observed from both aircraft and ground-based remote sensors, could be a benchmark for model simulations of mixed-phase clouds.

Errors resulting from assuming opaque Lambertian clouds in TOMS ozone retrieval

International Nuclear Information System (INIS)

Liu, X.; Newchurch, M.J.; Loughman, R.; Bhartia, P.K.

2004-01-01

Accurate remote sensing retrieval of atmospheric constituents over cloudy areas is very challenging because of insufficient knowledge of cloud parameters. Cloud treatments are highly idealized in most retrieval algorithms. Using a radiative transfer model treating clouds as scattering media, we investigate the effects of assuming opaque Lambertian clouds and employing a Partial Cloud Model (PCM) on Total Ozone Mapping Spectrometer (TOMS) ozone retrievals, especially for tropical high-reflectivity clouds. Assuming angularly independent cloud reflection is good because the Ozone Retrieval Errors (OREs) are within 1.5% of the total ozone (i.e., within TOMS retrieval precision) when Cloud Optical Depth (COD)≥20. Because of Intra-Cloud Ozone Absorption ENhancement (ICOAEN), assuming opaque clouds can introduce large OREs even for optically thick clouds. For a water cloud of COD 40 spanning 2-12 km with 20.8 Dobson Unit (DU) ozone homogeneously distributed in the cloud, the ORE is 17.8 DU in the nadir view. The ICOAEN effect depends greatly on solar zenith angle, view zenith angle, and intra-cloud ozone amount and distribution. The TOMS PCM is good because negative errors from the cloud fraction being underestimated partly cancel other positive errors. At COD≤5, the TOMS algorithm retrieves approximately the correct total ozone because of compensating errors. With increasing COD up to 20-40, the overall positive ORE increases and is finally dominated by the ICOAEN effect. The ICOAEN effect is typically 5-13 DU on average over the Atlantic and Africa and 1-7 DU over the Pacific for tropical high-altitude (cloud top pressure ≤300 hPa) and high-reflectivity (reflectivity ≥ 80%) clouds. Knowledge of TOMS ozone retrieval errors has important implications for remote sensing of ozone/trace gases from other satellite instruments

Cloud-Resolving Model Simulations of Aerosol-Cloud Interactions Triggered by Strong Aerosol Emissions in the Arctic

Science.gov (United States)

Wang, H.; Kravitz, B.; Rasch, P. J.; Morrison, H.; Solomon, A.

2014-12-01

Previous process-oriented modeling studies have highlighted the dependence of effectiveness of cloud brightening by aerosols on cloud regimes in warm marine boundary layer. Cloud microphysical processes in clouds that contain ice, and hence the mechanisms that drive aerosol-cloud interactions, are more complicated than in warm clouds. Interactions between ice particles and liquid drops add additional levels of complexity to aerosol effects. A cloud-resolving model is used to study aerosol-cloud interactions in the Arctic triggered by strong aerosol emissions, through either geoengineering injection or concentrated sources such as shipping and fires. An updated cloud microphysical scheme with prognostic aerosol and cloud particle numbers is employed. Model simulations are performed in pure super-cooled liquid and mixed-phase clouds, separately, with or without an injection of aerosols into either a clean or a more polluted Arctic boundary layer. Vertical mixing and cloud scavenging of particles injected from the surface is still quite efficient in the less turbulent cold environment. Overall, the injection of aerosols into the Arctic boundary layer can delay the collapse of the boundary layer and increase low-cloud albedo. The pure liquid clouds are more susceptible to the increase in aerosol number concentration than the mixed-phase clouds. Rain production processes are more effectively suppressed by aerosol injection, whereas ice precipitation (snow) is affected less; thus the effectiveness of brightening mixed-phase clouds is lower than for liquid-only clouds. Aerosol injection into a clean boundary layer results in a greater cloud albedo increase than injection into a polluted one, consistent with current knowledge about aerosol-cloud interactions. Unlike previous studies investigating warm clouds, the impact of dynamical feedback due to precipitation changes is small. According to these results, which are dependent upon the representation of ice nucleation

Remote Sensing of Clouds And Precipitation: Event-Based Characterization, Life Cycle Evolution, and Aerosol Influences

Science.gov (United States)

Esmaili, Rebekah Bradley

Global climate models, numerical weather prediction, and flood models rely on accurate satellite precipitation products, which are the only datasets that are continuous in time and space across the globe. While there are more earth observing satellites than ever before, gaps in precipitation retrievals exist due to sensor and orbital limitations of low-earth (LEO) satellites, which are overcome by merging data from different sensors in satellite precipitation products (SPPs). Using cloud tracking at higher resolutions than the spatio-temporal scales of LEO satellites, this thesis examines how clouds typically form in the atmosphere, the rate that cloud size and temperature evolve over the life cycle, and the time of day that cloud development take place. This thesis found that cloud evolution was non-linear, which disagrees with the linear interpolation schemes used in SPPs. Longer lasting clouds tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lasting clouds. Over the ocean, longer lasting clouds were found to occur more frequently at night, while shorter lasting clouds were more common during the daytime. This thesis also examines whether large-scale Saharan dust outbreaks can impact the trajectories and intensity of cloud clusters in the tropical Atlantic, which is predicted by modeling studies. The presented results show that proximity to Saharan dust outbreaks shifts Atlantic cloud development northward and intense storms becoming more common, whereas on days with low dust loading small-scale, warmer clouds are more common. A simplified view of cloud evolution in merged rainfall retrievals is a possible source of errors, which can propagate into higher level analysis. This thesis investigates the difference in the intensity, duration, and frequency of precipitation in IMERG, a next-generation satellite precipitation product with ground radar observations over the

Aerosol-cloud interactions in a multi-scale modeling framework

Science.gov (United States)

Lin, G.; Ghan, S. J.

2017-12-01

Atmospheric aerosols play an important role in changing the Earth's climate through scattering/absorbing solar and terrestrial radiation and interacting with clouds. However, quantification of the aerosol effects remains one of the most uncertain aspects of current and future climate projection. Much of the uncertainty results from the multi-scale nature of aerosol-cloud interactions, which is very challenging to represent in traditional global climate models (GCMs). In contrast, the multi-scale modeling framework (MMF) provides a viable solution, which explicitly resolves the cloud/precipitation in the cloud resolved model (CRM) embedded in the GCM grid column. In the MMF version of community atmospheric model version 5 (CAM5), aerosol processes are treated with a parameterization, called the Explicit Clouds Parameterized Pollutants (ECPP). It uses the cloud/precipitation statistics derived from the CRM to treat the cloud processing of aerosols on the GCM grid. However, this treatment treats clouds on the CRM grid but aerosols on the GCM grid, which is inconsistent with the reality that cloud-aerosol interactions occur on the cloud scale. To overcome the limitation, here, we propose a new aerosol treatment in the MMF: Explicit Clouds Explicit Aerosols (ECEP), in which we resolve both clouds and aerosols explicitly on the CRM grid. We first applied the MMF with ECPP to the Accelerated Climate Modeling for Energy (ACME) model to have an MMF version of ACME. Further, we also developed an alternative version of ACME-MMF with ECEP. Based on these two models, we have conducted two simulations: one with the ECPP and the other with ECEP. Preliminary results showed that the ECEP simulations tend to predict higher aerosol concentrations than ECPP simulations, because of the more efficient vertical transport from the surface to the higher atmosphere but the less efficient wet removal. We also found that the cloud droplet number concentrations are also different between the

Retrieval of effective cloud field parameters from radiometric data

Science.gov (United States)

Paulescu, Marius; Badescu, Viorel; Brabec, Marek

2017-06-01

Clouds play a key role in establishing the Earth's climate. Real cloud fields are very different and very complex in both morphological and microphysical senses. Consequently, the numerical description of the cloud field is a critical task for accurate climate modeling. This study explores the feasibility of retrieving the effective cloud field parameters (namely the cloud aspect ratio and cloud factor) from systematic radiometric measurements at high frequency (measurement is taken every 15 s). Two different procedures are proposed, evaluated, and discussed with respect to both physical and numerical restrictions. None of the procedures is classified as best; therefore, the specific advantages and weaknesses are discussed. It is shown that the relationship between the cloud shade and point cloudiness computed using the estimated cloud field parameters recovers the typical relationship derived from measurements.

A fast infrared radiative transfer model for overlapping clouds

International Nuclear Information System (INIS)

Niu Jianguo; Yang Ping; Huang Hunglung; Davies, James E.; Li Jun; Baum, Bryan A.; Hu, Yong X.

2007-01-01

A fast infrared radiative transfer model (FIRTM2) appropriate for application to both single-layered and overlapping cloud situations is developed for simulating the outgoing infrared spectral radiance at the top of the atmosphere (TOA). In FIRTM2 a pre-computed library of cloud reflectance and transmittance values is employed to account for one or two cloud layers, whereas the background atmospheric optical thickness due to gaseous absorption can be computed from a clear-sky radiative transfer model. FIRTM2 is applicable to three atmospheric conditions: (1) clear-sky (2) single-layered ice or water cloud, and (3) two simultaneous cloud layers in a column (e.g., ice cloud overlying water cloud). Moreover, FIRTM2 outputs the derivatives (i.e., Jacobians) of the TOA brightness temperature with respect to cloud optical thickness and effective particle size. Sensitivity analyses have been carried out to assess the performance of FIRTM2 for two spectral regions, namely the longwave (LW) band (587.3-1179.5 cm -1 ) and the short-to-medium wave (SMW) band (1180.1-2228.9 cm -1 ). The assessment is carried out in terms of brightness temperature differences (BTD) between FIRTM2 and the well-known discrete ordinates radiative transfer model (DISORT), henceforth referred to as BTD (F-D). The BTD (F-D) values for single-layered clouds are generally less than 0.8 K. For the case of two cloud layers (specifically ice cloud over water cloud), the BTD (F-D) values are also generally less than 0.8 K except for the SMW band for the case of a very high altitude (>15 km) cloud comprised of small ice particles. Note that for clear-sky atmospheres, FIRTM2 reduces to the clear-sky radiative transfer model that is incorporated into FIRTM2, and the errors in this case are essentially those of the clear-sky radiative transfer model

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yuying [Lawrence Livermore National Laboratory, Livermore, California; Xie, Shaocheng [Lawrence Livermore National Laboratory, Livermore, California; Klein, Stephen A. [Lawrence Livermore National Laboratory, Livermore, California; Marchand, Roger [University of Washington, Seattle, Washington; Kollias, Pavlos [Stony Brook University, Stony Brook, New York; Clothiaux, Eugene E. [The Pennsylvania State University, University Park, Pennsylvania; Lin, Wuyin [Brookhaven National Laboratory, Upton, New York; Johnson, Karen [Brookhaven National Laboratory, Upton, New York; Swales, Dustin [CIRES and NOAA/Earth System Research Laboratory, Boulder, Colorado; Bodas-Salcedo, Alejandro [Met Office Hadley Centre, Exeter, United Kingdom; Tang, Shuaiqi [Lawrence Livermore National Laboratory, Livermore, California; Haynes, John M. [Cooperative Institute for Research in the Atmosphere/Colorado State University, Fort Collins, Colorado; Collis, Scott [Argonne National Laboratory, Argonne, Illinois; Jensen, Michael [Brookhaven National Laboratory, Upton, New York; Bharadwaj, Nitin [Pacific Northwest National Laboratory, Richland, Washington; Hardin, Joseph [Pacific Northwest National Laboratory, Richland, Washington; Isom, Bradley [Pacific Northwest National Laboratory, Richland, Washington

2018-01-01

Clouds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have had difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors which are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators”, which convert model variables into pseudo-instrument observations, has evolved with the goal to improve and to facilitate the comparison of modeled clouds with observations. Many simulators have (and continue to be developed) for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the global climate modeling community to exploit satellite observations for model cloud evaluation (e.g., Klein et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to CloudSat radar observations, ARM radar measurements occur with higher temporal resolution and finer vertical resolution. This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are

Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud

Energy Technology Data Exchange (ETDEWEB)

Klein, Stephen A.; McCoy, Renata B.; Morrison, Hugh; Ackerman, Andrew S.; Avramov, Alexander; de Boer, Gijs; Chen, Mingxuan; Cole, Jason N.S.; Del Genio, Anthony D.; Falk, Michael; Foster, Michael J.; Fridlind, Ann; Golaz, Jean-Christophe; Hashino, Tempei; Harrington, Jerry Y.; Hoose, Corinna; Khairoutdinov, Marat F.; Larson, Vincent E.; Liu, Xiaohong; Luo, Yali; McFarquhar, Greg M.; Menon, Surabi; Neggers, Roel A. J.; Park, Sungsu; Poellot, Michael R.; Schmidt, Jerome M.; Sednev, Igor; Shipway, Ben J.; Shupe, Matthew D.; Spangenberg, Douglas A.; Sud, Yogesh C.; Turner, David D.; Veron, Dana E.; von Salzen, Knut; Walker, Gregory K.; Wang, Zhien; Wolf, Audrey B.; Xie, Shaocheng; Xu, Kuan-Man; Yang, Fanglin; Zhang, Gong

2009-02-02

Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) program's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud top temperature of -15 C. The observed average liquid water path of around 160 g m{sup -2} was about two-thirds of the adiabatic value and much greater than the average mass of ice crystal precipitation which when integrated from the surface to cloud top was around 15 g m{sup -2}. The simulations were performed by seventeen single-column models (SCMs) and nine cloud-resolving models (CRMs). While the simulated ice water path is generally consistent with the observed values, the median SCM and CRM liquid water path is a factor of three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path. Despite this general underestimate, the simulated liquid and ice water paths of several models are consistent with the observed values. Furthermore, there is evidence that models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter is also present. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.

The ARM-GCSS Intercomparison Study of Single-Column Models and Cloud System Models

International Nuclear Information System (INIS)

Cederwall, R.T.; Rodriques, D.J.; Krueger, S.K.; Randall, D.A.

1999-01-01

The Single-Column Model (SCM) Working Group (WC) and the Cloud Working Group (CWG) in the Atmospheric Radiation Measurement (ARM) Program have begun a collaboration with the GEWEX Cloud System Study (GCSS) WGs. The forcing data sets derived from the special ARM radiosonde measurements made during the SCM Intensive Observation Periods (IOPs), the wealth of cloud and related data sets collected by the ARM Program, and the ARM infrastructure support of the SCM WG are of great value to GCSS. In return, GCSS brings the efforts of an international group of cloud system modelers to bear on ARM data sets and ARM-related scientific questions. The first major activity of the ARM-GCSS collaboration is a model intercomparison study involving SCMs and cloud system models (CSMs), also known as cloud-resolving or cloud-ensemble models. The SCM methodologies developed in the ARM Program have matured to the point where an intercomparison will help identify the strengths and weaknesses of various approaches. CSM simulations will bring much additional information about clouds to evaluate cloud parameterizations used in the SCMs. CSMs and SCMs have been compared successfully in previous GCSS intercomparison studies for tropical conditions. The ARM Southern Great Plains (SGP) site offers an opportunity for GCSS to test their models in continental, mid-latitude conditions. The Summer 1997 SCM IOP has been chosen since it provides a wide range of summertime weather events that will be a challenging test of these models

Cloud Type Classification (cldtype) Value-Added Product

Energy Technology Data Exchange (ETDEWEB)

Flynn, Donna [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Shi, Yan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lim, K-S [Korean Atomic Energy Research Inst., Daejeon (South Korea); Riihimaki, Laura [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2017-08-15

The Cloud Type (cldtype) value-added product (VAP) provides an automated cloud type classification based on macrophysical quantities derived from vertically pointing lidar and radar. Up to 10 layers of clouds are classified into seven cloud types based on predetermined and site-specific thresholds of cloud top, base and thickness. Examples of thresholds for selected U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility sites are provided in Tables 1 and 2. Inputs for the cldtype VAP include lidar and radar cloud boundaries obtained from the Active Remotely Sensed Cloud Location (ARSCL) and Surface Meteorological Systems (MET) data. Rain rates from MET are used to determine when radar signal attenuation precludes accurate cloud detection. Temporal resolution and vertical resolution for cldtype are 1 minute and 30 m respectively and match the resolution of ARSCL. The cldtype classification is an initial step for further categorization of clouds. It was developed for use by the Shallow Cumulus VAP to identify potential periods of interest to the LASSO model and is intended to find clouds of interest for a variety of users.

A CLOUD BOUNDARY DETECTION SCHEME COMBINED WITH ASLIC AND CNN USING ZY-3, GF-1/2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

Z. Guo

2018-04-01

Full Text Available 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.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Science.gov (United States)

Tao, Wei-Kuo; Li, Xiaowen; Khain, Alexander; Matsui, Toshihisa; Lang, Stephen; Simpson, Joanne

2008-01-01

]. Please see Tao et al. (2007) for more detailed description on aerosol impact on precipitation. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

Scaling predictive modeling in drug development with cloud computing.

Science.gov (United States)

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

CloudLM: a Cloud-based Language Model for Machine Translation

Directory of Open Access Journals (Sweden)

Ferrández-Tordera Jorge

2016-04-01

Full Text Available Language models (LMs are an essential element in statistical approaches to natural language processing for tasks such as speech recognition and machine translation (MT. The advent of big data leads to the availability of massive amounts of data to build LMs, and in fact, for the most prominent languages, using current techniques and hardware, it is not feasible to train LMs with all the data available nowadays. At the same time, it has been shown that the more data is used for a LM the better the performance, e.g. for MT, without any indication yet of reaching a plateau. This paper presents CloudLM, an open-source cloud-based LM intended for MT, which allows to query distributed LMs. CloudLM relies on Apache Solr and provides the functionality of state-of-the-art language modelling (it builds upon KenLM, while allowing to query massive LMs (as the use of local memory is drastically reduced, at the expense of slower decoding speed.

A Reputation-Based Identity Management Model for Cloud Computing

Directory of Open Access Journals (Sweden)

Lifa Wu

2015-01-01

Full Text Available In the field of cloud computing, most research on identity management has concentrated on protecting user data. However, users typically leave a trail when they access cloud services, and the resulting user traceability can potentially lead to the leakage of sensitive user information. Meanwhile, malicious users can do harm to cloud providers through the use of pseudonyms. To solve these problems, we introduce a reputation mechanism and design a reputation-based identity management model for cloud computing. In the model, pseudonyms are generated based on a reputation signature so as to guarantee the untraceability of pseudonyms, and a mechanism that calculates user reputation is proposed, which helps cloud service providers to identify malicious users. Analysis verifies that the model can ensure that users access cloud services anonymously and that cloud providers assess the credibility of users effectively without violating user privacy.

Comparison of convective clouds observed by spaceborne W-band radar and simulated by cloud-resolving atmospheric models

Science.gov (United States)

Dodson, Jason B.

Deep convective clouds (DCCs) play an important role in regulating global climate through vertical mass flux, vertical water transport, and radiation. For general circulation models (GCMs) to simulate the global climate realistically, they must simulate DCCs realistically. GCMs have traditionally used cumulus parameterizations (CPs). Much recent research has shown that multiple persistent unrealistic behaviors in GCMs are related to limitations of CPs. Two alternatives to CPs exist: the global cloud-resolving model (GCRM), and the multiscale modeling framework (MMF). Both can directly simulate the coarser features of DCCs because of their multi-kilometer horizontal resolutions, and can simulate large-scale meteorological processes more realistically than GCMs. However, the question of realistic behavior of simulated DCCs remains. How closely do simulated DCCs resemble observed DCCs? In this study I examine the behavior of DCCs in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and Superparameterized Community Atmospheric Model (SP-CAM), the latter with both single-moment and double-moment microphysics. I place particular emphasis on the relationship between cloud vertical structure and convective environment. I also emphasize the transition between shallow clouds and mature DCCs. The spatial domains used are the tropical oceans and the contiguous United States (CONUS), the latter of which produces frequent vigorous convection during the summer. CloudSat is used to observe DCCs, and A-Train and reanalysis data are used to represent the large-scale environment in which the clouds form. The CloudSat cloud mask and radar reflectivity profiles for CONUS cumuliform clouds (defined as clouds with a base within the planetary boundary layer) during boreal summer are first averaged and compared. Both NICAM and SP-CAM greatly underestimate the vertical growth of cumuliform clouds. Then they are sorted by three large-scale environmental variables: total preciptable

Reconstruction of 3D Shapes of Opaque Cumulus Clouds from Airborne Multiangle Imaging: A Proof-of-Concept

Science.gov (United States)

Davis, A. B.; Bal, G.; Chen, J.

2015-12-01

Operational remote sensing of microphysical and optical cloud properties is invariably predicated on the assumption of plane-parallel slab geometry for the targeted cloud. The sole benefit of this often-questionable assumption about the cloud is that it leads to one-dimensional (1D) radiative transfer (RT)---a textbook, computationally tractable model. We present new results as evidence that, thanks to converging advances in 3D RT, inverse problem theory, algorithm implementation, and computer hardware, we are at the dawn of a new era in cloud remote sensing where we can finally go beyond the plane-parallel paradigm. Granted, the plane-parallel/1D RT assumption is reasonable for spatially extended stratiform cloud layers, as well as the smoothly distributed background aerosol layers. However, these 1D RT-friendly scenarios exclude cases that are critically important for climate physics. 1D RT---whence operational cloud remote sensing---fails catastrophically for cumuliform clouds that have fully 3D outer shapes and internal structures driven by shallow or deep convection. For these situations, the first order of business in a robust characterization by remote sensing is to abandon the slab geometry framework and determine the 3D geometry of the cloud, as a first step toward bone fide 3D cloud tomography. With this specific goal in mind, we deliver a proof-of-concept for an entirely new kind of remote sensing applicable to 3D clouds. It is based on highly simplified 3D RT and exploits multi-angular suites of cloud images at high spatial resolution. Airborne sensors like AirMSPI readily acquire such data. The key element of the reconstruction algorithm is a sophisticated solution of the nonlinear inverse problem via linearization of the forward model and an iteration scheme supported, where necessary, by adaptive regularization. Currently, the demo uses a 2D setting to show how either vertical profiles or horizontal slices of the cloud can be accurately reconstructed

Clouds enhance Greenland ice sheet mass loss

Science.gov (United States)

Van Tricht, Kristof; Gorodetskaya, Irina V.; L'Ecuyer, Tristan; Lenaerts, Jan T. M.; Lhermitte, Stef; Noel, Brice; Turner, David D.; van den Broeke, Michiel R.; van Lipzig, Nicole P. M.

2015-04-01

Clouds have a profound influence on both the Arctic and global climate, while they still represent one of the key uncertainties in climate models, limiting the fidelity of future climate projections. The potentially important role of thin liquid-containing clouds over Greenland in enhancing ice sheet melt has recently gained interest, yet current research is spatially and temporally limited, focusing on particular events, and their large scale impact on the surface mass balance remains unknown. We used a combination of satellite remote sensing (CloudSat - CALIPSO), ground-based observations and climate model (RACMO) data to show that liquid-containing clouds warm the Greenland ice sheet 94% of the time. High surface reflectivity (albedo) for shortwave radiation reduces the cloud shortwave cooling effect on the absorbed fluxes, while not influencing the absorption of longwave radiation. Cloud warming over the ice sheet therefore dominates year-round. Only when albedo values drop below ~0.6 in the coastal areas during summer, the cooling effect starts to overcome the warming effect. The year-round excess of energy due to the presence of liquid-containing clouds has an extensive influence on the mass balance of the ice sheet. Simulations using the SNOWPACK snow model showed not only a strong influence of these liquid-containing clouds on melt increase, but also on the increased sublimation mass loss. Simulations with the Community Earth System Climate Model for the end of the 21st century (2080-2099) show that Greenland clouds contain more liquid water path and less ice water path. This implies that cloud radiative forcing will be further enhanced in the future. Our results therefore urge the need for improving cloud microphysics in climate models, to improve future projections of ice sheet mass balance and global sea level rise.

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

Science.gov (United States)

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.

Determination of clouds in MSG data for the validation of clouds in a regional climate model

OpenAIRE

Huckle, Roger

2009-01-01

Regional climate models (e.g. CLM) can help to asses the influence of the antropogenic climate change on the different regions of the earth. Validation of these models is very important. Satellite data are of great benefit, as data on a global scale and high temporal resolution is available. In this thesis a cloud detection and object based cloud classification for Meteosat Second Generation (MSG) was developed and used to validate CLM clouds. Results show sometimes too many clouds in the CLM.

Analysis of albedo versus cloud fraction relationships in liquid water clouds using heuristic models and large eddy simulation

Science.gov (United States)

Feingold, Graham; Balsells, Joseph; Glassmeier, Franziska; Yamaguchi, Takanobu; Kazil, Jan; McComiskey, Allison

2017-07-01

The relationship between the albedo of a cloudy scene A and cloud fraction fc is studied with the aid of heuristic models of stratocumulus and cumulus clouds. Existing work has shown that scene albedo increases monotonically with increasing cloud fraction but that the relationship varies from linear to superlinear. The reasons for these differences in functional dependence are traced to the relationship between cloud deepening and cloud widening. When clouds deepen with no significant increase in fc (e.g., in solid stratocumulus), the relationship between A and fc is linear. When clouds widen as they deepen, as in cumulus cloud fields, the relationship is superlinear. A simple heuristic model of a cumulus cloud field with a power law size distribution shows that the superlinear A-fc behavior is traced out either through random variation in cloud size distribution parameters or as the cloud field oscillates between a relative abundance of small clouds (steep slopes on a log-log plot) and a relative abundance of large clouds (flat slopes). Oscillations of this kind manifest in large eddy simulation of trade wind cumulus where the slope and intercept of the power law fit to the cloud size distribution are highly correlated. Further analysis of the large eddy model-generated cloud fields suggests that cumulus clouds grow larger and deeper as their underlying plumes aggregate; this is followed by breakup of large plumes and a tendency to smaller clouds. The cloud and thermal size distributions oscillate back and forth approximately in unison.

A comparison of food crispness based on the cloud model.

Science.gov (United States)

Wang, Minghui; Sun, Yonghai; Hou, Jumin; Wang, Xia; Bai, Xue; Wu, Chunhui; Yu, Libo; Yang, Jie

2018-02-01

The cloud model is a typical model which transforms the qualitative concept into the quantitative description. The cloud model has been used less extensively in texture studies before. The purpose of this study was to apply the cloud model in food crispness comparison. The acoustic signals of carrots, white radishes, potatoes, Fuji apples, and crystal pears were recorded during compression. And three time-domain signal characteristics were extracted, including sound intensity, maximum short-time frame energy, and waveform index. The three signal characteristics and the cloud model were used to compare the crispness of the samples mentioned above. The crispness based on the Ex value of the cloud model, in a descending order, was carrot > potato > white radish > Fuji apple > crystal pear. To verify the results of the acoustic signals, mechanical measurement and sensory evaluation were conducted. The results of the two verification experiments confirmed the feasibility of the cloud model. The microstructures of the five samples were also analyzed. The microstructure parameters were negatively related with crispness (p cloud model method can be used for crispness comparison of different kinds of foods. The method is more accurate than the traditional methods such as mechanical measurement and sensory evaluation. The cloud model method can also be applied to other texture studies extensively. © 2017 Wiley Periodicals, Inc.

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

Science.gov (United States)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

Route Assessment for Unmanned Aerial Vehicle Based on Cloud Model

Directory of Open Access Journals (Sweden)

Xixia Sun

2014-01-01

Full Text Available An integrated route assessment approach based on cloud model is proposed in this paper, where various sources of uncertainties are well kept and modeled by cloud theory. Firstly, a systemic criteria framework incorporating models for scoring subcriteria is developed. Then, the cloud model is introduced to represent linguistic variables, and survivability probability histogram of each route is converted into normal clouds by cloud transformation, enabling both randomness and fuzziness in the assessment environment to be managed simultaneously. Finally, a new way to measure the similarity between two normal clouds satisfying reflexivity, symmetry, transitivity, and overlapping is proposed. Experimental results demonstrate that the proposed route assessment approach outperforms fuzzy logic based assessment approach with regard to feasibility, reliability, and consistency with human thinking.

Evaluation of NCMRWF unified model vertical cloud structure with CloudSat over the Indian summer monsoon region

Science.gov (United States)

Jayakumar, A.; Mamgain, Ashu; Jisesh, A. S.; Mohandas, Saji; Rakhi, R.; Rajagopal, E. N.

2016-05-01

Representation of rainfall distribution and monsoon circulation in the high resolution versions of NCMRWF Unified model (NCUM-REG) for the short-range forecasting of extreme rainfall event is vastly dependent on the key factors such as vertical cloud distribution, convection and convection/cloud relationship in the model. Hence it is highly relevant to evaluate the vertical structure of cloud and precipitation of the model over the monsoon environment. In this regard, we utilized the synergy of the capabilities of CloudSat data for long observational period, by conditioning it for the synoptic situation of the model simulation period. Simulations were run at 4-km grid length with the convective parameterization effectively switched off and on. Since the sample of CloudSat overpasses through the monsoon domain is small, the aforementioned methodology may qualitatively evaluate the vertical cloud structure for the model simulation period. It is envisaged that the present study will open up the possibility of further improvement in the high resolution version of NCUM in the tropics for the Indian summer monsoon associated rainfall events.

A model of the microphysical evolution of a cloud

International Nuclear Information System (INIS)

Zinn, J.

1994-01-01

The earth's weather and climate are influenced strongly by phenomena associated with clouds. Therefore, a general circulation model (GCM) that models the evolution of weather and climate must include an accurate physical model of the clouds. This paper describes efforts to develop a suitable cloud model. It concentrates on the microphysical processes that determine the evolution of droplet and ice crystal size distributions, precipitation rates, total and condensed water content, and radiative extinction coefficients

Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model.

Science.gov (United States)

Sato, Yousuke; Goto, Daisuke; Michibata, Takuro; Suzuki, Kentaroh; Takemura, Toshihiko; Tomita, Hirofumi; Nakajima, Teruyuki

2018-03-07

Aerosols affect climate by modifying cloud properties through their role as cloud condensation nuclei or ice nuclei, called aerosol-cloud interactions. In most global climate models (GCMs), the aerosol-cloud interactions are represented by empirical parameterisations, in which the mass of cloud liquid water (LWP) is assumed to increase monotonically with increasing aerosol loading. Recent satellite observations, however, have yielded contradictory results: LWP can decrease with increasing aerosol loading. This difference implies that GCMs overestimate the aerosol effect, but the reasons for the difference are not obvious. Here, we reproduce satellite-observed LWP responses using a global simulation with explicit representations of cloud microphysics, instead of the parameterisations. Our analyses reveal that the decrease in LWP originates from the response of evaporation and condensation processes to aerosol perturbations, which are not represented in GCMs. The explicit representation of cloud microphysics in global scale modelling reduces the uncertainty of climate prediction.

Evolution in Cloud Population Statistics of the MJO: From AMIE Field Observations to Global Cloud-Permiting Models

Energy Technology Data Exchange (ETDEWEB)

Zhang, Chidong [Univ. of Miami, Coral Gables, FL (United States)

2016-08-14

Motivated by the success of the AMIE/DYNAMO field campaign, which collected unprecedented observations of cloud and precipitation from the tropical Indian Ocean in Octber 2011 – March 2012, this project explored how such observations can be applied to assist the development of global cloud-permitting models through evaluating and correcting model biases in cloud statistics. The main accomplishment of this project were made in four categories: generating observational products for model evaluation, using AMIE/DYNAMO observations to validate global model simulations, using AMIE/DYNAMO observations in numerical studies of cloud-permitting models, and providing leadership in the field. Results from this project provide valuable information for building a seamless bridge between DOE ASR program’s component on process level understanding of cloud processes in the tropics and RGCM focus on global variability and regional extremes. In particular, experience gained from this project would be directly applicable to evaluation and improvements of ACME, especially as it transitions to a non-hydrostatic variable resolution model.

Final Technical Report for "High-resolution global modeling of the effects of subgrid-scale clouds and turbulence on precipitating cloud systems"

Energy Technology Data Exchange (ETDEWEB)

Larson, Vincent [Univ. of Wisconsin, Milwaukee, WI (United States)

2016-11-25

The Multiscale Modeling Framework (MMF) embeds a cloud-resolving model in each grid column of a General Circulation Model (GCM). A MMF model does not need to use a deep convective parameterization, and thereby dispenses with the uncertainties in such parameterizations. However, MMF models grossly under-resolve shallow boundary-layer clouds, and hence those clouds may still benefit from parameterization. In this grant, we successfully created a climate model that embeds a cloud parameterization (“CLUBB”) within a MMF model. This involved interfacing CLUBB’s clouds with microphysics and reducing computational cost. We have evaluated the resulting simulated clouds and precipitation with satellite observations. The chief benefit of the project is to provide a MMF model that has an improved representation of clouds and that provides improved simulations of precipitation.

Remote Sensing of Smoke, Land and Clouds from the NASA ER-2 during SAFARI 2000

Science.gov (United States)

King, Michael D.; Platnick, Steven; Moeller, Christopher C.; Revercomb, Henry E.; Chu, D. Allen

2002-01-01

The NASA ER-2 aircraft was deployed to southern Africa between August 17 and September 25, 2000 as part of the Southern Africa Regional Science Initiative (SAFARI) 2000. This aircraft carried a sophisticated array of multispectral scanners, multiangle spectroradiometers, a monostatic lidar, a gas correlation radiometer, upward and downward spectral flux radiometers, and two metric mapping cameras. These observations were obtained over a 3200 x 2800 km region of savanna, woody savanna, open shrubland, and grassland ecosystems throughout southern Africa, and were quite often coordinated with overflights by NASA's Terra and Landsat 7 satellites. The primary purpose of this sophisticated high altitude observing platform was to obtain independent observations of smoke, clouds, and land surfaces that could be used to check the validity of various remote sensing measurements derived by Earth-orbiting satellites. These include such things as the accuracy of the Moderate Resolution Imaging Spectro-radiometer (MODIS) cloud mask for distinguishing clouds and heavy aerosol from land and ocean surfaces, and Terra analyses of cloud optical and micro-physical properties, aerosol properties, leaf area index, vegetation index, fire occurrence, carbon monoxide, and surface radiation budget. In addition to coordination with Terra and Landsat 7 satellites, numerous flights were conducted over surface AERONET sites, flux towers in South Africa, Botswana, and Zambia, and in situ aircraft from the University of Washington, South Africa, and the United Kingdom.

Numerical simulations of altocumulus with a cloud resolving model

Energy Technology Data Exchange (ETDEWEB)

Liu, S.; Krueger, S.K. [Univ. of Utah, Salt Lake City, UT (United States)

1996-04-01

Altocumulus and altostratus clouds together cover approximately 22% of the earth`s surface. They play an important role in the earth`s energy budget through their effect on solar and infrared radiation. However, there has been little altocumulus cloud investigation by either modelers or observational programs. Starr and Cox (SC) (1985a,b) simulated an altostratus case as part of the same study in which they modeled a thin layer of cirrus. Although this calculation was originally described as representing altostratus, it probably better represents altocumulus stratiformis. In this paper, we simulate altocumulus cloud with a cloud resolving model (CRM). We simply describe the CRM first. We calculate the same middle-level cloud case as SC to compare our results with theirs. We will look at the role of cloud-scale processes in response to large-scale forcing. We will also discuss radiative effects by simulating diurnal and nocturnal cases. Finally, we discuss the utility of a 1D model by comparing 1D simulations and 2D simulations.

Security Management Model in Cloud Computing Environment

OpenAIRE

Ahmadpanah, Seyed Hossein

2016-01-01

In the cloud computing environment, cloud virtual machine (VM) will be more and more the number of virtual machine security and management faced giant Challenge. In order to address security issues cloud computing virtualization environment, this paper presents a virtual machine based on efficient and dynamic deployment VM security management model state migration and scheduling, study of which virtual machine security architecture, based on AHP (Analytic Hierarchy Process) virtual machine de...

Explicit prediction of ice clouds in general circulation models

Science.gov (United States)

Kohler, Martin

1999-11-01

Although clouds play extremely important roles in the radiation budget and hydrological cycle of the Earth, there are large quantitative uncertainties in our understanding of their generation, maintenance and decay mechanisms, representing major obstacles in the development of reliable prognostic cloud water schemes for General Circulation Models (GCMs). Recognizing their relative neglect in the past, both observationally and theoretically, this work places special focus on ice clouds. A recent version of the UCLA - University of Utah Cloud Resolving Model (CRM) that includes interactive radiation is used to perform idealized experiments to study ice cloud maintenance and decay mechanisms under various conditions in term of: (1) background static stability, (2) background relative humidity, (3) rate of cloud ice addition over a fixed initial time-period and (4) radiation: daytime, nighttime and no-radiation. Radiation is found to have major effects on the life-time of layer-clouds. Optically thick ice clouds decay significantly slower than expected from pure microphysical crystal fall-out (taucld = 0.9--1.4 h as opposed to no-motion taumicro = 0.5--0.7 h). This is explained by the upward turbulent fluxes of water induced by IR destabilization, which partially balance the downward transport of water by snowfall. Solar radiation further slows the ice-water decay by destruction of the inversion above cloud-top and the resulting upward transport of water. Optically thin ice clouds, on the other hand, may exhibit even longer life-times (>1 day) in the presence of radiational cooling. The resulting saturation mixing ratio reduction provides for a constant cloud ice source. These CRM results are used to develop a prognostic cloud water scheme for the UCLA-GCM. The framework is based on the bulk water phase model of Ose (1993). The model predicts cloud liquid water and cloud ice separately, and which is extended to split the ice phase into suspended cloud ice (predicted

Deployment Models: Towards Eliminating Security Concerns From Cloud Computing

OpenAIRE

Zhao, Gansen; Chunming, Rong; Jaatun, Martin Gilje; Sandnes, Frode Eika

2010-01-01

Cloud computing has become a popular choice as an alternative to investing new IT systems. When making decisions on adopting cloud computing related solutions, security has always been a major concern. This article summarizes security concerns in cloud computing and proposes five service deployment models to ease these concerns. The proposed models provide different security related features to address different requirements and scenarios and can serve as reference models for deployment. D...

Remote sensing, hydrological modeling and in situ observations in snow cover research: A review

Science.gov (United States)

Dong, Chunyu

2018-06-01

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models.

Implementation of a Novel Educational Modeling Approach for Cloud Computing

Directory of Open Access Journals (Sweden)

Sara Ouahabi

2014-12-01

Full Text Available The Cloud model is cost-effective because customers pay for their actual usage without upfront costs, and scalable because it can be used more or less depending on the customers’ needs. Due to its advantages, Cloud has been increasingly adopted in many areas, such as banking, e-commerce, retail industry, and academy. For education, cloud is used to manage the large volume of educational resources produced across many universities in the cloud. Keep interoperability between content in an inter-university Cloud is not always easy. Diffusion of pedagogical contents on the Cloud by different E-Learning institutions leads to heterogeneous content which influence the quality of teaching offered by university to teachers and learners. From this reason, comes the idea of using IMS-LD coupled with metadata in the cloud. This paper presents the implementation of our previous educational modeling by combining an application in J2EE with Reload editor that consists of modeling heterogeneous content in the cloud. The new approach that we followed focuses on keeping interoperability between Educational Cloud content for teachers and learners and facilitates the task of identification, reuse, sharing, adapting teaching and learning resources in the Cloud.

A novel cost based model for energy consumption in cloud computing.

Science.gov (United States)

Horri, A; Dastghaibyfard, Gh

2015-01-01

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment.

Mesoscale meteorological model based on radioactive explosion cloud simulation

International Nuclear Information System (INIS)

Zheng Yi; Zhang Yan; Ying Chuntong

2008-01-01

In order to simulate nuclear explosion and dirty bomb radioactive cloud movement and concentration distribution, mesoscale meteorological model RAMS was used. Particles-size, size-active distribution and gravitational fallout in the cloud were considered. The results show that the model can simulate the 'mushroom' clouds of explosion. Three-dimension fluid field and radioactive concentration field were received. (authors)

Modeling, Design, and Implementation of a Cloud Workflow Engine Based on Aneka

OpenAIRE

Zhou, Jiantao; Sun, Chaoxin; Fu, Weina; Liu, Jing; Jia, Lei; Tan, Hongyan

2014-01-01

This paper presents a Petri net-based model for cloud workflow which plays a key role in industry. Three kinds of parallelisms in cloud workflow are characterized and modeled. Based on the analysis of the modeling, a cloud workflow engine is designed and implemented in Aneka cloud environment. The experimental results validate the effectiveness of our approach of modeling, design, and implementation of cloud workflow.

A Multilateral Negotiation Model for Cloud Service Market

Science.gov (United States)

Yoo, Dongjin; Sim, Kwang Mong

Trading cloud services between consumers and providers is a complicated issue of cloud computing. Since a consumer can negotiate with multiple providers to acquire the same service and each provider can receive many requests from multiple consumers, to facilitate the trading of cloud services among multiple consumers and providers, a multilateral negotiation model for cloud market is necessary. The contribution of this work is the proposal of a business model supporting a multilateral price negotiation for trading cloud services. The design of proposed systems for cloud service market includes considering a many-to-many negotiation protocol, and price determining factor from service level feature. Two negotiation strategies are implemented: 1) MDA (Market Driven Agent); and 2) adaptive concession making responding to changes of bargaining position are proposed for cloud service market. Empirical results shows that MDA achieved better performance in some cases that the adaptive concession making strategy, it is noted that unlike the MDA, the adaptive concession making strategy does not assume that an agent has information of the number of competitors (e.g., a consumer agent adopting the adaptive concession making strategy need not know the number of consumer agents competing for the same service).

Cloud Robotics Model

OpenAIRE

Mester, Gyula

2015-01-01

Cloud Robotics was born from the merger of service robotics and cloud technologies. It allows robots to benefit from the powerful computational, storage, and communications resources of modern data centres. Cloud robotics allows robots to take advantage of the rapid increase in data transfer rates to offload tasks without hard real time requirements. Cloud Robotics has rapidly gained momentum with initiatives by companies such as Google, Willow Garage and Gostai as well as more than a dozen a...

Remote Sensing of Clouds for Solar Forecasting Applications

Science.gov (United States)

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

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6.

Science.gov (United States)

Webb, Mark J.; Andrews, Timothy; Bodas-Salcedo, Alejandro; Bony, Sandrine; Bretherton, Christopher S.; Chadwick, Robin; Chepfer, Helene; Douville, Herve; Good, Peter; Kay, Jennifer E.;

Validation of the Two-Layer Model for Correcting Clear Sky Reflectance Near Clouds

Science.gov (United States)

Wen, Guoyong; Marshak, Alexander; Evans, K. Frank; Vamal, Tamas

2014-01-01

A two-layer model was developed in our earlier studies to estimate the clear sky reflectance enhancement near clouds. This simple model accounts for the radiative interaction between boundary layer clouds and molecular layer above, the major contribution to the reflectance enhancement near clouds for short wavelengths. We use LES/SHDOM simulated 3D radiation fields to valid the two-layer model for reflectance enhancement at 0.47 micrometer. We find: (a) The simple model captures the viewing angle dependence of the reflectance enhancement near cloud, suggesting the physics of this model is correct; and (b) The magnitude of the 2-layer modeled enhancement agree reasonably well with the "truth" with some expected underestimation. We further extend our model to include cloud-surface interaction using the Poisson model for broken clouds. We found that including cloud-surface interaction improves the correction, though it can introduced some over corrections for large cloud albedo, large cloud optical depth, large cloud fraction, large cloud aspect ratio. This over correction can be reduced by excluding scenes (10 km x 10km) with large cloud fraction for which the Poisson model is not designed for. Further research is underway to account for the contribution of cloud-aerosol radiative interaction to the enhancement.

Global vertical mass transport by clouds - A two-dimensional model study

International Nuclear Information System (INIS)

Olofsson, Mats

1988-05-01

A two-dimensional global dispersion model, where vertical transport in the troposphere carried out by convective as well as by frontal cloud systems is explicitly treated, is developed from an existing diffusion model. A parameterization scheme for the cloud transport, based on global cloud statistics, is presented. The model has been tested by using Kr-85, Rn-222 and SO 2 as tracers. Comparisons have been made with observed distributions of these tracers, but also with model results without the cloud transport, using eddy diffusion as the primary means of vertical transport. The model results indicate that for trace species with a turnover time of days to weeks, the introduction of cloud-transport gives much more realistic simulations of their vertical distribution. Layers of increased mixing ratio with height, which can be found in real atmosphere, are reproduced in our cloud-transport model profiles, but can never be simulated with a pure eddy diffusion model. The horizontal transport in the model, by advection and eddy diffusion, gives a realistic distribution between the hemispheres of the more long-lived tracers (Kr-85). A combination of vertical transport by convective and frontal cloud systems is shown to improve the model simulations, compared to limiting it to convective transport only. The importance of including cumulus clouds in the convective transport scheme, in addition to the efficient transport by cumulonimbus clouds, is discussed. The model results are shown to be more sensitive to the vertical detrainment distribution profile than to the absolute magnitude of the vertical mass transport. The scavenging processes for SO 2 are parameterized without the introduction of detailed chemistry. An enhanced removal, due to the increased contact with droplets in the in-cloud lifting process, is introduced in the model. (author)

Prediction of cloud droplet number in a general circulation model

Energy Technology Data Exchange (ETDEWEB)

Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)

1996-04-01

We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.

A comparative analysis of extended water cloud model and backscatter modelling for above-ground biomass assessment in Corbett Tiger Reserve

Science.gov (United States)

Kumar, Yogesh; Singh, Sarnam; Chatterjee, R. S.; Trivedi, Mukul

2016-04-01

Forest biomass acts as a backbone in regulating the climate by storing carbon within itself. Thus the assessment of forest biomass is crucial in understanding the dynamics of the environment. Traditionally the destructive methods were adopted for the assessment of biomass which were further advanced to the non-destructive methods. The allometric equations developed by destructive methods were further used in non-destructive methods for the assessment, but they were mostly applied for woody/commercial timber species. However now days Remote Sensing data are primarily used for the biomass geospatial pattern assessment. The Optical Remote Sensing data (Landsat8, LISS III, etc.) are being used very successfully for the estimation of above ground biomass (AGB). However optical data is not suitable for all atmospheric/environmental conditions, because it can't penetrate through clouds and haze. Thus Radar data is one of the alternate possible ways to acquire data in all-weather conditions irrespective of weather and light. The paper examines the potential of ALOS PALSAR L-band dual polarisation data for the estimation of AGB in the Corbett Tiger Reserve (CTR) covering an area of 889 km2. The main focus of this study is to explore the accuracy of Polarimetric Scattering Model (Extended Water Cloud Model (EWCM) with respect to Backscatter model in the assessment of AGB. The parameters of the EWCM were estimated using the decomposition components (Raney Decomposition) and the plot level information. The above ground biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha.

A Review on Broker Based Cloud Service Model

Directory of Open Access Journals (Sweden)

Nagarajan Rajganesh

2016-09-01

Full Text Available Cloud computing emerged as a utility oriented computing that facilitates resource sharing under pay-as-you-go model. Nowadays, cloud offerings are not limited to range of services and anything can be shared as a service through the Internet. In this work, a detailed literature survey with respect to cloud service discovery and composition has been accounted. A proposed architecture with the inclusion of cloud broker is presented in our work. It focuses the importance of suitable service selection and its ranking towards fulfilling the customer’s service requirements. The proposed cloud broker advocates techniques such as reasoning and decision making capabilities for the improved cloud service selection and composition.

Remote sensing for global change, climate change and atmosphere and ocean forecasting. Volume 1

International Nuclear Information System (INIS)

1992-01-01

This volume is separated in three sessions. First part is on remote sensing for global change (with global modelling, land cover change on global scale, ocean colour studies of marine biosphere, biological and hydrological interactions and large scale experiments). Second part is on remote sensing for climate change (with earth radiation and clouds, sea ice, global climate research programme). Third part is on remote sensing for atmosphere and ocean forecasting (with temperatures and humidity, winds, data assimilation, cloud imagery, sea surface temperature, ocean waves and topography). (A.B.). refs., figs., tabs

Petri net modeling of encrypted information flow in federated cloud

Science.gov (United States)

Khushk, Abdul Rauf; Li, Xiaozhong

2017-08-01

Solutions proposed and developed for the cost-effective cloud systems suffer from a combination of secure private clouds and less secure public clouds. Need to locate applications within different clouds poses a security risk to the information flow of the entire system. This study addresses this by assigning security levels of a given lattice to the entities of a federated cloud system. A dynamic flow sensitive security model featuring Bell-LaPadula procedures is explored that tracks and authenticates the secure information flow in federated clouds. Additionally, a Petri net model is considered as a case study to represent the proposed system and further validate the performance of the said system.

IMAGE TO POINT CLOUD METHOD OF 3D-MODELING

Directory of Open Access Journals (Sweden)

A. G. Chibunichev

2012-07-01

Full Text Available This article describes the method of constructing 3D models of objects (buildings, monuments based on digital images and a point cloud obtained by terrestrial laser scanner. The first step is the automated determination of exterior orientation parameters of digital image. We have to find the corresponding points of the image and point cloud to provide this operation. Before the corresponding points searching quasi image of point cloud is generated. After that SIFT algorithm is applied to quasi image and real image. SIFT algorithm allows to find corresponding points. Exterior orientation parameters of image are calculated from corresponding points. The second step is construction of the vector object model. Vectorization is performed by operator of PC in an interactive mode using single image. Spatial coordinates of the model are calculated automatically by cloud points. In addition, there is automatic edge detection with interactive editing available. Edge detection is performed on point cloud and on image with subsequent identification of correct edges. Experimental studies of the method have demonstrated its efficiency in case of building facade modeling.

Cloud-Based Model Calibration Using OpenStudio: Preprint

Energy Technology Data Exchange (ETDEWEB)

Hale, E.; Lisell, L.; Goldwasser, D.; Macumber, D.; Dean, J.; Metzger, I.; Parker, A.; Long, N.; Ball, B.; Schott, M.; Weaver, E.; Brackney, L.

2014-03-01

OpenStudio is a free, open source Software Development Kit (SDK) and application suite for performing building energy modeling and analysis. The OpenStudio Parametric Analysis Tool has been extended to allow cloud-based simulation of multiple OpenStudio models parametrically related to a baseline model. This paper describes the new cloud-based simulation functionality and presents a model cali-bration case study. Calibration is initiated by entering actual monthly utility bill data into the baseline model. Multiple parameters are then varied over multiple iterations to reduce the difference between actual energy consumption and model simulation results, as calculated and visualized by billing period and by fuel type. Simulations are per-formed in parallel using the Amazon Elastic Cloud service. This paper highlights model parameterizations (measures) used for calibration, but the same multi-nodal computing architecture is available for other purposes, for example, recommending combinations of retrofit energy saving measures using the calibrated model as the new baseline.

Green Functions For Multiple Scattering As Mathematical Tools For Dense Cloud Remote Sensing: Theory, With Passive And Active Applications

International Nuclear Information System (INIS)

Davis, A.B.; Marshak, A.; Cahalan, R.F.

2001-01-01

We survey radiative Green function theory (1) in linear transport theory where numerical procedures are required to obtain specific results and (2) in the photon diffusion limit (large optical depths) where it is analytically tractable, at least for homogeneous plane-parallel media. We then describe two recent applications of Green function theory to passive cloud remote sensing in the presence of strong three-dimensional transport effects. Finally, we describe recent instrumental breakthroughs in 'off-beam' cloud lidar which is based on direct measurements of radiative Green functions with special attention to the data collected during the Shuttle-based Lidar In-space Technology Experiment (LITE) mission.

The emerging role of cloud computing in molecular modelling.

Science.gov (United States)

Ebejer, Jean-Paul; Fulle, Simone; Morris, Garrett M; Finn, Paul W

2013-07-01

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways. Copyright © 2013 Elsevier Inc. All rights reserved.

A Coupled fcGCM-GCE Modeling System: A 3D Cloud Resolving Model and a Regional Scale Model

Science.gov (United States)

Tao, Wei-Kuo

2005-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and ore sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicity calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A Brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), (3) A discussion on the Goddard WRF version (its developments and applications), and (4) The characteristics of the four-dimensional cloud data

Cloud Computing Adoption Business Model Factors: Does Enterprise Size Matter?

OpenAIRE

Bogataj Habjan, Kristina; Pucihar, Andreja

2017-01-01

This paper presents the results of research investigating the impact of business model factors on cloud computing adoption. The introduced research model consists of 40 cloud computing business model factors, grouped into eight factor groups. Their impact and importance for cloud computing adoption were investigated among enterpirses in Slovenia. Furthermore, differences in opinion according to enterprise size were investigated. Research results show no statistically significant impacts of in...

A cloud/particle model of the interstellar medium - Galactic spiral structure

Science.gov (United States)

Levinson, F. H.; Roberts, W. W., Jr.

1981-01-01

A cloud/particle model for gas flow in galaxies is developed that incorporates cloud-cloud collisions and supernovae as dominant local processes. Cloud-cloud collisions are the main means of dissipation. To counter this dissipation and maintain local dispersion, supernova explosions in the medium administer radial snowplow pushes to all nearby clouds. The causal link between these processes is that cloud-cloud collisions will form stars and that these stars will rapidly become supernovae. The cloud/particle model is tested and used to investigate the gas dynamics and spiral structures in galaxies where these assumptions may be reasonable. Particular attention is given to whether large-scale galactic shock waves, which are thought to underlie the regular well-delineated spiral structure in some galaxies, form and persist in a cloud-supernova dominated interstellar medium; this question is answered in the affirmative.

Comparisons of Satellite-Deduced Overlapping Cloud Properties and CALIPSO CloudSat Data

Science.gov (United States)

Chang, Fu-Lung; Minnis, Patrick; Lin, Bing; Sun-Mack, Sunny

2010-01-01

Introduction to the overlapped cloud properties derived from polar-orbiting (MODIS) and geostationary (GOES-12, -13, Meteosat-8, -9, etc.) meteorological satellites, which are produced at the NASA Langley Research Center (LaRC) cloud research & development team (NASA lead scientist: Dr. Patrick Minnis). Comparison of the LaRC CERES MODIS Edition-3 overlapped cloud properties to the CALIPSO and the CloudSat active sensing data. High clouds and overlapped clouds occur frequently as deduced by CALIPSO (44 & 25%), CloudSat (25 & 4%), and MODIS (37 & 6%). Large fractions of optically-thin cirrus and overlapped clouds are deduced from CALIPSO, but much smaller fractions are from CloudSat and MODIS. For overlapped clouds, the averaged upper-layer CTHs are about 12.8 (CALIPSO), 10.9 (CloudSat) and 10 km (MODIS), and the averaged lower-layer CTHs are about 3.6 (CALIPSO), 3.2 (CloudSat) and 3.9 km (MODIS). Based on comparisons of upper and lower-layer cloud properties as deduced from the MODIS, CALIPSO and CloudSat data, more enhanced passive satellite methods for retrieving thin cirrus and overlapped cloud properties are needed and are under development.

Dispersion modeling by kinematic simulation: Cloud dispersion model

International Nuclear Information System (INIS)

Fung, J C H; Perkins, R J

2008-01-01

A new technique has been developed to compute mean and fluctuating concentrations in complex turbulent flows (tidal current near a coast and deep ocean). An initial distribution of material is discretized into any small clouds which are advected by a combination of the mean flow and large scale turbulence. The turbulence can be simulated either by kinematic simulation (KS) or direct numerical simulation. The clouds also diffuse relative to their centroids; the statistics for this are obtained from a separate calculation of the growth of individual clouds in small scale turbulence, generated by KS. The ensemble of discrete clouds is periodically re-discretized, to limit the size of the small clouds and prevent overlapping. The model is illustrated with simulations of dispersion in uniform flow, and the results are compared with analytic, steady state solutions. The aim of this study is to understand how pollutants disperses in a turbulent flow through a numerical simulation of fluid particle motion in a random flow field generated by Fourier modes. Although this homogeneous turbulent is rather a 'simple' flow, it represents a building block toward understanding pollutant dispersion in more complex flow. The results presented here are preliminary in nature, but we expect that similar qualitative results should be observed in a genuine turbulent flow.

Establishing a Cloud Computing Success Model for Hospitals in Taiwan

Science.gov (United States)

Lian, Jiunn-Woei

2017-01-01

The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services. PMID:28112020

Establishing a Cloud Computing Success Model for Hospitals in Taiwan.

Science.gov (United States)

Lian, Jiunn-Woei

2017-01-01

The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services.

Establishing a Cloud Computing Success Model for Hospitals in Taiwan

Directory of Open Access Journals (Sweden)

Jiunn-Woei Lian PhD

2017-01-01

Full Text Available The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Science.gov (United States)

Ehrlich, André; Bierwirth, Eike; Istomina, Larysa; Wendisch, Manfred

2017-09-01

The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff

Measurement and modeling of shortwave irradiance components in cloud-free atmospheres

Energy Technology Data Exchange (ETDEWEB)

Halthore, R.N.

1999-08-04

Atmosphere scatters and absorbs incident solar radiation modifying its spectral content and decreasing its intensity at the surface. It is very useful to classify the earth-atmospheric solar radiation into several components--direct solar surface irradiance (E{sub direct}), diffuse-sky downward surface irradiance (E{sub diffuse}), total surface irradiance, and upwelling flux at the surface and at the top-of-the atmosphere. E{sub direct} depends only on the extinction properties of the atmosphere without regard to details of extinction, namely scattering or absorption; furthermore it can be accurately measured to high accuracy (0.3%) with the aid of an active cavity radiometer (ACR). E{sub diffuse} has relatively larger uncertainties both in its measurement using shaded pyranometers and in model estimates, owing to the difficulty in accurately characterizing pyranometers and in measuring model inputs such as surface reflectance, aerosol single scattering albedo, and phase function. Radiative transfer model simulations of the above surface radiation components in cloud-free skies using measured atmospheric properties show that while E{sub direct} estimates are closer to measurements, E{sub diffuse} is overestimated by an amount larger than the combined uncertainties in model inputs and measurements, illustrating a fundamental gap in the understanding of the magnitude of atmospheric absorption in cloud-free skies. The excess continuum type absorption required to reduce the E{sub diffuse} model overestimate ({approximately}3--8% absorptance) would significantly impact climate prediction and remote sensing. It is not clear at present what the source for this continuum absorption is. Here issues related to measurements and modeling of the surface irradiance components are discussed.

The Geospatial Data Cloud: An Implementation of Applying Cloud Computing in Geosciences

Directory of Open Access Journals (Sweden)

Xuezhi Wang

2014-11-01

Full Text Available The rapid growth in the volume of remote sensing data and its increasing computational requirements bring huge challenges for researchers as traditional systems cannot adequately satisfy the huge demand for service. Cloud computing has the advantage of high scalability and reliability, which can provide firm technical support. This paper proposes a highly scalable geospatial cloud platform named the Geospatial Data Cloud, which is constructed based on cloud computing. The architecture of the platform is first introduced, and then two subsystems, the cloud-based data management platform and the cloud-based data processing platform, are described.  ––– This paper was presented at the First Scientific Data Conference on Scientific Research, Big Data, and Data Science, organized by CODATA-China and held in Beijing on 24-25 February, 2014.

Modeling and Security in Cloud Ecosystems

Directory of Open Access Journals (Sweden)

Eduardo B. Fernandez

2016-04-01

Full Text Available Clouds do not work in isolation but interact with other clouds and with a variety of systems either developed by the same provider or by external entities with the purpose to interact with them; forming then an ecosystem. A software ecosystem is a collection of software systems that have been developed to coexist and evolve together. The stakeholders of such a system need a variety of models to give them a perspective of the possibilities of the system, to evaluate specific quality attributes, and to extend the system. A powerful representation when building or using software ecosystems is the use of architectural models, which describe the structural aspects of such a system. These models have value for security and compliance, are useful to build new systems, can be used to define service contracts, find where quality factors can be monitored, and to plan further expansion. We have described a cloud ecosystem in the form of a pattern diagram where its components are patterns and reference architectures. A pattern is an encapsulated solution to a recurrent problem. We have recently expanded these models to cover fog systems and containers. Fog Computing is a highly-virtualized platform that provides compute, storage, and networking services between end devices and Cloud Computing Data Centers; a Software Container provides an execution environment for applications sharing a host operating system, binaries, and libraries with other containers. We intend to use this architecture to answer a variety of questions about the security of this system as well as a reference to design interacting combinations of heterogeneous components. We defined a metamodel to relate security concepts which is being expanded.

Chemical equilibrium models of interstellar gas clouds

International Nuclear Information System (INIS)

Freeman, A.

1982-10-01

This thesis contains work which helps towards our understanding of the chemical processes and astrophysical conditions in interstellar clouds, across the whole range of cloud types. The object of the exercise is to construct a mathematical model representing a large system of two-body chemical reactions in order to deduce astrophysical parameters and predict molecular abundances and chemical pathways. Comparison with observations shows that this type of model is valid but also indicates that our knowledge of some chemical reactions is incomplete. (author)

Business Process as a Service Model Based Business and IT Cloud Alignment as a Cloud Offering

OpenAIRE

Robert Woitsch; Wilfrid Utz

2015-01-01

Cloud computing proved to offer flexible IT solutions. Although large enterprises may benefit from this technology, SMEs are falling behind in cloud usage due to missing ITcompetence and hence lose the ability to efficiently adapt their IT to their business needs. This paper introduces the project idea of the H2020 project CloudSocket, by elaborating the idea of Business Processes as a Service (BPaaS), where concept models and semantics are applied to align business processes with Cloud deplo...

A Hybrid of Optical Remote Sensing and Hydrological Modeling Improves Water Balance Estimation

Science.gov (United States)

Gleason, Colin J.; Wada, Yoshihide; Wang, Jida

2018-01-01

Declining gauging infrastructure and fractious water politics have decreased available information about river flows globally. Remote sensing and water balance modeling are frequently cited as potential solutions, but these techniques largely rely on these same in-decline gauge data to make accurate discharge estimates. A different approach is therefore needed, and we here combine remotely sensed discharge estimates made via at-many-stations hydraulic geometry (AMHG) and the PCR-GLOBWB hydrological model to estimate discharge over the Lower Nile. Specifically, we first estimate initial discharges from 87 Landsat images and AMHG (1984-2015), and then use these flow estimates to tune the model, all without using gauge data. The resulting tuned modeled hydrograph shows a large improvement in flow magnitude: validation of the tuned monthly hydrograph against a historical gauge (1978-1984) yields an RMSE of 439 m3/s (40.8%). By contrast, the original simulation had an order-of-magnitude flow error. This improvement is substantial but not perfect: tuned flows have a 1-2 month wet season lag and a negative base flow bias. Accounting for this 2 month lag yields a hydrograph RMSE of 270 m3/s (25.7%). Thus, our results coupling physical models and remote sensing is a promising first step and proof of concept toward future modeling of ungauged flows, especially as developments in cloud computing for remote sensing make our method easily applicable to any basin. Finally, we purposefully do not offer prescriptive solutions for Nile management, and rather hope that the methods demonstrated herein can prove useful to river stakeholders in managing their own water.

A Scalable Cloud Library Empowering Big Data Management, Diagnosis, and Visualization of Cloud-Resolving Models

Science.gov (United States)

Zhou, S.; Tao, W. K.; Li, X.; Matsui, T.; Sun, X. H.; Yang, X.

2015-12-01

A cloud-resolving model (CRM) is an atmospheric numerical model that can numerically resolve clouds and cloud systems at 0.25~5km horizontal grid spacings. The main advantage of the CRM is that it can allow explicit interactive processes between microphysics, radiation, turbulence, surface, and aerosols without subgrid cloud fraction, overlapping and convective parameterization. Because of their fine resolution and complex physical processes, it is challenging for the CRM community to i) visualize/inter-compare CRM simulations, ii) diagnose key processes for cloud-precipitation formation and intensity, and iii) evaluate against NASA's field campaign data and L1/L2 satellite data products due to large data volume (~10TB) and complexity of CRM's physical processes. We have been building the Super Cloud Library (SCL) upon a Hadoop framework, capable of CRM database management, distribution, visualization, subsetting, and evaluation in a scalable way. The current SCL capability includes (1) A SCL data model enables various CRM simulation outputs in NetCDF, including the NASA-Unified Weather Research and Forecasting (NU-WRF) and Goddard Cumulus Ensemble (GCE) model, to be accessed and processed by Hadoop, (2) A parallel NetCDF-to-CSV converter supports NU-WRF and GCE model outputs, (3) A technique visualizes Hadoop-resident data with IDL, (4) A technique subsets Hadoop-resident data, compliant to the SCL data model, with HIVE or Impala via HUE's Web interface, (5) A prototype enables a Hadoop MapReduce application to dynamically access and process data residing in a parallel file system, PVFS2 or CephFS, where high performance computing (HPC) simulation outputs such as NU-WRF's and GCE's are located. We are testing Apache Spark to speed up SCL data processing and analysis.With the SCL capabilities, SCL users can conduct large-domain on-demand tasks without downloading voluminous CRM datasets and various observations from NASA Field Campaigns and Satellite data to a

Two Models of Magnetic Support for Photoevaporated Molecular Clouds

International Nuclear Information System (INIS)

Ryutov, D; Kane, J; Mizuta, A; Pound, M; Remington, B

2004-01-01

The thermal pressure inside molecular clouds is insufficient for maintaining the pressure balance at an ablation front at the cloud surface illuminated by nearby UV stars. Most probably, the required stiffness is provided by the magnetic pressure. After surveying existing models of this type, we concentrate on two of them: the model of a quasi-homogeneous magnetic field and the recently proposed model of a ''magnetostatic turbulence''. We discuss observational consequences of the two models, in particular, the structure and the strength of the magnetic field inside the cloud and in the ionized outflow. We comment on the possible role of reconnection events and their observational signatures. We mention laboratory experiments where the most significant features of the models can be tested

Cloud forcing: A modeling perspective

International Nuclear Information System (INIS)

Potter, G.L.; Mobely, R.L.; Drach, R.S.; Corsetti, T.G.; Williams, D.N.; Slingo, J.M.

1990-11-01

Radiation fields from a perpetual July integration of a T106 version of the ECMWF operational model are used as surrogate observations of the radiation budget at the top of the atmosphere to illustrate various difficulties that modellers might face when trying to reconcile cloud radiation forcings derived from satellite observations with model-generated ones. Differences between the so-called Methods 1 and 2 of Cess and Potter (1987) and a variant Method 3 are addressed. Method 1 is shown to be the least robust of all methods, due to potential uncertainties related to persistent cloudiness, length of the period over which clear-sky conditions are looked for, biases in retrieved clear-sky quantities due to an insufficient sampling of the diurnal cycle. We advocate the use of Method 2 as the only unambiguous one to produce consistent radiative diagnostics for intercomparing model results. Impact of the three methods on the derived sensitivities and cloud feedbacks following an imposed change in sea surface temperature (used as a surrogate climate change) is discussed. 17 refs., 12 figs., 1 tab

A fast infrared radiative transfer model based on the adding-doubling method for hyperspectral remote-sensing applications

International Nuclear Information System (INIS)

Zhang Zhibo; Yang Ping; Kattawar, George; Huang, H.-L.; Greenwald, Thomas; Li Jun; Baum, Bryan A.; Zhou, Daniel K.; Hu Yongxiang

2007-01-01

A fast infrared radiative transfer (RT) model is developed on the basis of the adding-doubling principle, hereafter referred to as FIRTM-AD, to facilitate the forward RT simulations involved in hyperspectral remote-sensing applications under cloudy-sky conditions. A pre-computed look-up table (LUT) of the bidirectional reflection and transmission functions and emissivities of ice clouds in conjunction with efficient interpolation schemes is used in FIRTM-AD to alleviate the computational burden of the doubling process. FIRTM-AD is applicable to a variety of cloud conditions, including vertically inhomogeneous or multilayered clouds. In particular, this RT model is suitable for the computation of high-spectral-resolution radiance and brightness temperature (BT) spectra at both the top-of-atmosphere and surface, and thus is useful for satellite and ground-based hyperspectral sensors. In terms of computer CPU time, FIRTM-AD is approximately 100-250 times faster than the well-known discrete-ordinate (DISORT) RT model for the same conditions. The errors of FIRTM-AD, specified as root-mean-square (RMS) BT differences with respect to their DISORT counterparts, are generally smaller than 0.1 K

Treatment of cloud radiative effects in general circulation models

Energy Technology Data Exchange (ETDEWEB)

Wang, W.C.; Dudek, M.P.; Liang, X.Z.; Ding, M. [State Univ. of New York, Albany, NY (United States)] [and others

1996-04-01

We participate in the Atmospheric Radiation Measurement (ARM) program with two objectives: (1) to improve the general circulation model (GCM) cloud/radiation treatment with a focus on cloud verticle overlapping and layer cloud optical properties, and (2) to study the effects of cloud/radiation-climate interaction on GCM climate simulations. This report summarizes the project progress since the Fourth ARM Science Team meeting February 28-March 4, 1994, in Charleston, South Carolina.

Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

KAUST Repository

Angel, Yoseline

2016-10-25

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-Temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-Temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

KAUST Repository

Angel, Yoseline; Houborg, Rasmus; McCabe, Matthew

2016-01-01

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multi-temporal data and spatio-temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

Science.gov (United States)

Angel, Yoseline; Houborg, Rasmus; McCabe, Matthew F.

2016-10-01

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multitemporal data and spatio-temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

KAUST Repository

Angel, Yoseline

2016-09-26

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological cycles of crops. This paper explores a cloud removal approach based on reflectance prediction using multi-temporal data and spatio-temporal statistical models. In particular, a covariance model that captures the behavior of spatial and temporal components in data simultaneously (i.e. non-separable) is considered. Eight weekly images collected from the Hyperion hyper-spectrometer instrument over an agricultural region of Saudi Arabia were used to reconstruct a scene with the presence of cloudy affected pixels over a center-pivot crop. A subset of reflectance values of cloud-free pixels from 50 bands in the spectral range from 426.82 to 884.7 nm at each date, were used as input to fit a parametric family of non-separable and stationary spatio-temporal covariance functions. Applying simple kriging as an interpolator, cloud affected pixels were replaced by cloud-free predicted values per band, obtaining their respective predicted spectral profiles at the same time. An exercise of reconstructing simulated cloudy pixels in a different swath was conducted to assess the model accuracy, achieving root mean square error (RMSE) values per band less than or equal to 3%. The spatial coherence of the results was also checked through absolute error distribution maps demonstrating their consistency.

A sustainability model based on cloud infrastructures for core and downstream Copernicus services

Science.gov (United States)

Manunta, Michele; Calò, Fabiana; De Luca, Claudio; Elefante, Stefano; Farres, Jordi; Guzzetti, Fausto; Imperatore, Pasquale; Lanari, Riccardo; Lengert, Wolfgang; Zinno, Ivana; Casu, Francesco

2014-05-01

SAR products generation and exploitation. In particular, CNR is porting the multi-temporal DInSAR technique referred to as Small Baseline Subset (SBAS) into the ESA G-POD (Grid Processing On Demand) and CIOP (Cloud Computing Operational Pilot) platforms (Elefante et al., 2013) within the SuperSites Exploitation Platform (SSEP) project, which aim is contributing to the development of an ecosystem for big geo-data processing and dissemination. This work focuses on presenting the main results that have been achieved by the DORIS project concerning the use of advanced DInSAR products for supporting CPA during the risk management cycle. Furthermore, based on the DORIS experience, a sustainability model for Core and Downstream Copernicus services based on the effective exploitation of cloud platforms is proposed. In this framework, remote sensing community, both service providers and users, can significantly benefit from the Helix Nebula-The Science Cloud initiative, created by European scientific institutions, agencies, SMEs and enterprises to pave the way for the development and exploitation of a cloud computing infrastructure for science. REFERENCES Elefante, S., Imperatore, P. , Zinno, I., M. Manunta, E. Mathot, F. Brito, J. Farres, W. Lengert, R. Lanari, F. Casu, 2013, "SBAS-DINSAR Time series generation on cloud computing platforms". IEEE IGARSS Conference, Melbourne (AU), July 2013.

Defining generic architecture for Cloud IaaS provisioning model

NARCIS (Netherlands)

Demchenko, Y.; de Laat, C.; Mavrin, A.; Leymann, F.; Ivanov, I.; van Sinderen, M.; Shishkov, B.

2011-01-01

Infrastructure as a Service (IaaS) is one of the provisioning models for Clouds as defined in the NIST Clouds definition. Although widely used, current IaaS implementations and solutions doesn’t have common and well defined architecture model. The paper attempts to define a generic architecture for

SIRTA, a ground-based atmospheric observatory for cloud and aerosol research

Directory of Open Access Journals (Sweden)

M. Haeffelin

2005-02-01

Full Text Available Ground-based remote sensing observatories have a crucial role to play in providing data to improve our understanding of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA, an atmospheric observatory with these goals in mind. Today SIRTA, located 20km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. Detailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific research associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes involving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column. Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for future active remote sensing missions in space (e.g. CloudSat and CALIPSO. SIRTA observation and research activities take place in networks of atmospheric observatories that allow scientists to access consistent data sets from diverse regions on the globe.

SnowCloud - a Framework to Predict Streamflow in Snowmelt-dominated Watersheds Using Cloud-based Computing

Science.gov (United States)

Sproles, E. A.; Crumley, R. L.; Nolin, A. W.; Mar, E.; Lopez-Moreno, J. J.

2017-12-01

Streamflow in snowy mountain regions is extraordinarily challenging to forecast, and prediction efforts are hampered by the lack of timely snow data—particularly in data sparse regions. SnowCloud is a prototype web-based framework that integrates remote sensing, cloud computing, interactive mapping tools, and a hydrologic model to offer a new paradigm for delivering key data to water resource managers. We tested the skill of SnowCloud to forecast monthly streamflow with one month lead time in three snow-dominated headwaters. These watersheds represent a range of precipitation/runoff schemes: the Río Elqui in northern Chile (200 mm/yr, entirely snowmelt); the John Day River, Oregon, USA (635 mm/yr, primarily snowmelt); and the Río Aragon in the northern Spain (850 mm/yr, snowmelt dominated). Model skill corresponded to snowpack contribution with Nash-Sutcliffe Efficiencies of 0.86, 0.52, and 0.21 respectively. SnowCloud does not require the user to possess advanced programming skills or proprietary software. We access NASA's MOD10A1 snow cover product to calculate the snow metrics globally using Google Earth Engine's geospatial analysis and cloud computing service. The analytics and forecast tools are provided through a web-based portal that requires only internet access and minimal training. To test the efficacy of SnowCloud we provided the tools and a series of tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience and provided feedback, and the results of our multi-cultural assessment are also presented. While our results focus on SnowCloud, they outline methods to develop cloud-based tools that function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in developing

Clouds in ECMWF's 30 KM Resolution Global Atmospheric Forecast Model (TL639)

Science.gov (United States)

Cahalan, R. F.; Morcrette, J. J.

1999-01-01

Global models of the general circulation of the atmosphere resolve a wide range of length scales, and in particular cloud structures extend from planetary scales to the smallest scales resolvable, now down to 30 km in state-of-the-art models. Even the highest resolution models do not resolve small-scale cloud phenomena seen, for example, in Landsat and other high-resolution satellite images of clouds. Unresolved small-scale disturbances often grow into larger ones through non-linear processes that transfer energy upscale. Understanding upscale cascades is of crucial importance in predicting current weather, and in parameterizing cloud-radiative processes that control long term climate. Several movie animations provide examples of the temporal and spatial variation of cloud fields produced in 4-day runs of the forecast model at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, England, at particular times and locations of simultaneous measurement field campaigns. model resolution is approximately 30 km horizontally (triangular truncation TL639) with 31 vertical levels from surface to stratosphere. Timestep of the model is about 10 minutes, but animation frames are 3 hours apart, at timesteps when the radiation is computed. The animations were prepared from an archive of several 4-day runs at the highest available model resolution, and archived at ECMWF. Cloud, wind and temperature fields in an approximately 1000 km X 1000 km box were retrieved from the archive, then approximately 60 Mb Vis5d files were prepared with the help of Graeme Kelly of ECMWF, and were compressed into MPEG files each less than 3 Mb. We discuss the interaction of clouds and radiation in the model, and compare the variability of cloud liquid as a function of scale to that seen in cloud observations made in intensive field campaigns. Comparison of high-resolution global runs to cloud-resolving models, and to lower resolution climate models is leading to better

Prognostic cloud water in the Los Alamos general circulation model

International Nuclear Information System (INIS)

Kristjansson, J.E.; Kao, C.Y.J.

1993-01-01

Most of today's general circulation models (GCMS) have a greatly simplified treatment of condensation and clouds. Recent observational studies of the earth's radiation budget have suggested cloud-related feedback mechanisms to be of tremendous importance for the issue of global change. Thus, there has arisen an urgent need for improvements in the treatment of clouds in GCMS, especially as the clouds relate to radiation. In the present paper, we investigate the effects of introducing pregnostic cloud water into the Los Alamos GCM. The cloud water field, produced by both stratiform and convective condensation, is subject to 3-dimensional advection and vertical diffusion. The cloud water enters the radiation calculations through the long wave emissivity calculations. Results from several sensitivity simulations show that realistic cloud water and precipitation fields can be obtained with the applied method. Comparisons with observations show that the most realistic results are obtained when more sophisticated schemes for moist convection are introduced at the same time. The model's cold bias is reduced and the zonal winds become stronger, due to more realistic tropical convection

Cloud radiative effects and changes simulated by the Coupled Model Intercomparison Project Phase 5 models

Science.gov (United States)

Shin, Sun-Hee; Kim, Ok-Yeon; Kim, Dongmin; Lee, Myong-In

2017-07-01

Using 32 CMIP5 (Coupled Model Intercomparison Project Phase 5) models, this study examines the veracity in the simulation of cloud amount and their radiative effects (CREs) in the historical run driven by observed external radiative forcing for 1850-2005, and their future changes in the RCP (Representative Concentration Pathway) 4.5 scenario runs for 2006-2100. Validation metrics for the historical run are designed to examine the accuracy in the representation of spatial patterns for climatological mean, and annual and interannual variations of clouds and CREs. The models show large spread in the simulation of cloud amounts, specifically in the low cloud amount. The observed relationship between cloud amount and the controlling large-scale environment are also reproduced diversely by various models. Based on the validation metrics, four models—ACCESS1.0, ACCESS1.3, HadGEM2-CC, and HadGEM2-ES—are selected as best models, and the average of the four models performs more skillfully than the multimodel ensemble average. All models project global-mean SST warming at the increase of the greenhouse gases, but the magnitude varies across the simulations between 1 and 2 K, which is largely attributable to the difference in the change of cloud amount and distribution. The models that simulate more SST warming show a greater increase in the net CRE due to reduced low cloud and increased incoming shortwave radiation, particularly over the regions of marine boundary layer in the subtropics. Selected best-performing models project a significant reduction in global-mean cloud amount of about -0.99% K-1 and net radiative warming of 0.46 W m-2 K-1, suggesting a role of positive feedback to global warming.

Model of E-Cloud Instability in the Fermilab Recycler

Energy Technology Data Exchange (ETDEWEB)

Balbekov, V. [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

2015-06-24

Simple model of electron cloud is developed in the paper to explain e-cloud instability of bunched proton beam in the Fermilab Recycler. The cloud is presented as an immobile snake in strong vertical magnetic field. The instability is treated as an amplification of the bunch injection errors from the batch head to its tail. Nonlinearity of the e-cloud field is taken into account. Results of calculations are compared with experimental data demonstrating good correlation.

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Science.gov (United States)

Tao, Wei-Kuo; Li, Xiaowen; Khain, Alexander; Matsui, Toshihisa; Lang, Stephen; Simpson, Joanne

2012-01-01

Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low clean concentration and a high dirty concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

Comprehensive models of diffuse interstellar clouds : physical conditions and molecular abundances

NARCIS (Netherlands)

Dishoeck, van E.F.; Black, J.H.

1986-01-01

The limitations of steady state models of interstellar clouds are explored by means of comparison with observational data corresponding to clouds in front of Zeta Per, Zeta Oph, Chi Oph, and Omicron Per. The improved cloud models were constructed to reproduce the observed H and H2(J) column

The cloud-phase feedback in the Super-parameterized Community Earth System Model

Science.gov (United States)

Burt, M. A.; Randall, D. A.

2016-12-01

Recent comparisons of observations and climate model simulations by I. Tan and colleagues have suggested that the Wegener-Bergeron-Findeisen (WBF) process tends to be too active in climate models, making too much cloud ice, and resulting in an exaggerated negative cloud-phase feedback on climate change. We explore the WBF process and its effect on shortwave cloud forcing in present-day and future climate simulations with the Community Earth System Model, and its super-parameterized counterpart. Results show that SP-CESM has much less cloud ice and a weaker cloud-phase feedback than CESM.

ARM Cloud Radar Simulator Package for Global Climate Models Value-Added Product

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yuying [North Carolina State Univ., Raleigh, NC (United States); Xie, Shaocheng [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-05-01

It has been challenging to directly compare U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility ground-based cloud radar measurements with climate model output because of limitations or features of the observing processes and the spatial gap between model and the single-point measurements. To facilitate the use of ARM radar data in numerical models, an ARM cloud radar simulator was developed to converts model data into pseudo-ARM cloud radar observations that mimic the instrument view of a narrow atmospheric column (as compared to a large global climate model [GCM] grid-cell), thus allowing meaningful comparison between model output and ARM cloud observations. The ARM cloud radar simulator value-added product (VAP) was developed based on the CloudSat simulator contained in the community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) (Bodas-Salcedo et al., 2011), which has been widely used in climate model evaluation with satellite data (Klein et al., 2013, Zhang et al., 2010). The essential part of the CloudSat simulator is the QuickBeam radar simulator that is used to produce CloudSat-like radar reflectivity, but is capable of simulating reflectivity for other radars (Marchand et al., 2009; Haynes et al., 2007). Adapting QuickBeam to the ARM cloud radar simulator within COSP required two primary changes: one was to set the frequency to 35 GHz for the ARM Ka-band cloud radar, as opposed to 94 GHz used for the CloudSat W-band radar, and the second was to invert the view from the ground to space so as to attenuate the beam correctly. In addition, the ARM cloud radar simulator uses a finer vertical resolution (100 m compared to 500 m for CloudSat) to resolve the more detailed structure of clouds captured by the ARM radars. The ARM simulator has been developed following the COSP workflow (Figure 1) and using the capabilities available in COSP

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Directory of Open Access Journals (Sweden)

A. Ehrlich

2017-09-01

Full Text Available The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow. Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C.In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S, λ2 = 1650 nm (sensitive to τ, and λ3 = 2100 nm (sensitive to reff, C are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012 were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice

User Utility Oriented Queuing Model for Resource Allocation in Cloud Environment

Directory of Open Access Journals (Sweden)

Zhe Zhang

2015-01-01

Full Text Available Resource allocation is one of the most important research topics in servers. In the cloud environment, there are massive hardware resources of different kinds, and many kinds of services are usually run on virtual machines of the cloud server. In addition, cloud environment is commercialized, and economical factor should also be considered. In order to deal with commercialization and virtualization of cloud environment, we proposed a user utility oriented queuing model for task scheduling. Firstly, we modeled task scheduling in cloud environment as an M/M/1 queuing system. Secondly, we classified the utility into time utility and cost utility and built a linear programming model to maximize total utility for both of them. Finally, we proposed a utility oriented algorithm to maximize the total utility. Massive experiments validate the effectiveness of our proposed model.

A security model for saas in cloud computing

International Nuclear Information System (INIS)

Abbas, R.; Farooq, A.

2016-01-01

Cloud computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. It has many service modes like Software as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure-as-a-Service (IaaS). In SaaS model, service providers install and activate the applications in cloud and cloud customers access the software from cloud. So, the user does not have the need to purchase and install a particular software on his/her machine. While using SaaS model, there are multiple security issues and problems like Data security, Data breaches, Network security, Authentication and authorization, Data integrity, Availability, Web application security and Backup which are faced by users. Many researchers minimize these security problems by putting in hard work. A large work has been done to resolve these problems but there are a lot of issues that persist and need to overcome. In this research work, we have developed a security model that improves the security of data according to the desire of the End-user. The proposed model for different data security options can be helpful to increase the data security through which trade-off between functionalities can be optimized for private and public data. (author)

Spectral cumulus parameterization based on cloud-resolving model

Science.gov (United States)

Baba, Yuya

2018-02-01

We have developed a spectral cumulus parameterization using a cloud-resolving model. This includes a new parameterization of the entrainment rate which was derived from analysis of the cloud properties obtained from the cloud-resolving model simulation and was valid for both shallow and deep convection. The new scheme was examined in a single-column model experiment and compared with the existing parameterization of Gregory (2001, Q J R Meteorol Soc 127:53-72) (GR scheme). The results showed that the GR scheme simulated more shallow and diluted convection than the new scheme. To further validate the physical performance of the parameterizations, Atmospheric Model Intercomparison Project (AMIP) experiments were performed, and the results were compared with reanalysis data. The new scheme performed better than the GR scheme in terms of mean state and variability of atmospheric circulation, i.e., the new scheme improved positive bias of precipitation in western Pacific region, and improved positive bias of outgoing shortwave radiation over the ocean. The new scheme also simulated better features of convectively coupled equatorial waves and Madden-Julian oscillation. These improvements were found to be derived from the modification of parameterization for the entrainment rate, i.e., the proposed parameterization suppressed excessive increase of entrainment, thus suppressing excessive increase of low-level clouds.

Coupling spectral-bin cloud microphysics with the MOSAIC aerosol model in WRF-Chem: Methodology and results for marine stratocumulus clouds

Science.gov (United States)

Gao, Wenhua; Fan, Jiwen; Easter, R. C.; Yang, Qing; Zhao, Chun; Ghan, Steven J.

2016-09-01

Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces the treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.

Verifying Air Force Weather Passive Satellite Derived Cloud Analysis Products

Science.gov (United States)

Nobis, T. E.

2017-12-01

Air Force Weather (AFW) has developed an hourly World-Wide Merged Cloud Analysis (WWMCA) using imager data from 16 geostationary and polar-orbiting satellites. The analysis product contains information on cloud fraction, height, type and various optical properties including optical depth and integrated water path. All of these products are derived using a suite of algorithms which rely exclusively on passively sensed data from short, mid and long wave imager data. The system integrates satellites with a wide-range of capabilities, from the relatively simple two-channel OLS imager to the 16 channel ABI/AHI to create a seamless global analysis in real time. Over the last couple of years, AFW has started utilizing independent verification data from active sensed cloud measurements to better understand the performance limitations of the WWMCA. Sources utilized include space based lidars (CALIPSO, CATS) and radar (CloudSat) as well as ground based lidars from the Department of Energy ARM sites and several European cloud radars. This work will present findings from our efforts to compare active and passive sensed cloud information including comparison techniques/limitations as well as performance of the passive derived cloud information against the active.

Abs: a high-level modeling language for cloud-aware programming

NARCIS (Netherlands)

N. Bezirgiannis (Nikolaos); F.S. de Boer (Frank)

2016-01-01

textabstractCloud technology has become an invaluable tool to the IT business, because of its attractive economic model. Yet, from the programmers’ perspective, the development of cloud applications remains a major challenge. In this paper we introduce a programming language that allows Cloud

Remote Sensing of Aerosols from Satellites: Why Has It Been Do Difficult to Quantify Aerosol-Cloud Interactions for Climate Assessment, and How Can We Make Progress?

Science.gov (United States)

Kahn, Ralph A.

2015-01-01

The organizers of the National Academy of Sciences Arthur M. Sackler Colloquia Series on Improving Our Fundamental Understanding of the Role of Aerosol-Cloud Interactions in the Climate System would like to post Ralph Kahn's presentation entitled Remote Sensing of Aerosols from Satellites: Why has it been so difficult to quantify aerosol-cloud interactions for climate assessment, and how can we make progress? to their public website.

[Treatment of cloud radiative effects in general circulation models

International Nuclear Information System (INIS)

Wang, W.C.

1993-01-01

This is a renewal proposal for an on-going project of the Department of Energy (DOE)/Atmospheric Radiation Measurement (ARM) Program. The objective of the ARM Program is to improve the treatment of radiation-cloud in GCMs so that reliable predictions of the timing and magnitude of greenhouse gas-induced global warming and regional responses can be made. The ARM Program supports two research areas: (I) The modeling and analysis of data related to the parameterization of clouds and radiation in general circulation models (GCMs); and (II) the development of advanced instrumentation for both mapping the three-dimensional structure of the atmosphere and high accuracy/precision radiometric observations. The present project conducts research in area (I) and focuses on GCM treatment of cloud life cycle, optical properties, and vertical overlapping. The project has two tasks: (1) Development and Refinement of GCM Radiation-Cloud Treatment Using ARM Data; and (2) Validation of GCM Radiation-Cloud Treatment

Probing Clouds in Planets with a Simple Radiative Transfer Model: The Jupiter Case

Science.gov (United States)

Mendikoa, Inigo; Perez-Hoyos, Santiago; Sanchez-Lavega, Agustin

2012-01-01

Remote sensing of planets evokes using expensive on-orbit satellites and gathering complex data from space. However, the basic properties of clouds in planetary atmospheres can be successfully estimated with small telescopes even from an urban environment using currently available and affordable technology. This makes the process accessible for…

The effects of aerosols on precipitation and dimensions of subtropical clouds: a sensitivity study using a numerical cloud model

Directory of Open Access Journals (Sweden)

A. Teller

2006-01-01

Full Text Available Numerical experiments were carried out using the Tel-Aviv University 2-D cloud model to investigate the effects of increased concentrations of Cloud Condensation Nuclei (CCN, giant CCN (GCCN and Ice Nuclei (IN on the development of precipitation and cloud structure in mixed-phase sub-tropical convective clouds. In order to differentiate between the contribution of the aerosols and the meteorology, all simulations were conducted with the same meteorological conditions. The results show that under the same meteorological conditions, polluted clouds (with high CCN concentrations produce less precipitation than clean clouds (with low CCN concentrations, the initiation of precipitation is delayed and the lifetimes of the clouds are longer. GCCN enhance the total precipitation on the ground in polluted clouds but they have no noticeable effect on cleaner clouds. The increased rainfall due to GCCN is mainly a result of the increased graupel mass in the cloud, but it only partially offsets the decrease in rainfall due to pollution (increased CCN. The addition of more effective IN, such as mineral dust particles, reduces the total amount of precipitation on the ground. This reduction is more pronounced in clean clouds than in polluted ones. Polluted clouds reach higher altitudes and are wider than clean clouds and both produce wider clouds (anvils when more IN are introduced. Since under the same vertical sounding the polluted clouds produce less rain, more water vapor is left aloft after the rain stops. In our simulations about 3.5 times more water evaporates after the rain stops from the polluted cloud as compared to the clean cloud. The implication is that much more water vapor is transported from lower levels to the mid troposphere under polluted conditions, something that should be considered in climate models.

Improving Mixed-phase Cloud Parameterization in Climate Model with the ACRF Measurements

Energy Technology Data Exchange (ETDEWEB)

Wang, Zhien [Univ. of Wyoming, Laramie, WY (United States)

2016-12-13

Mixed-phase cloud microphysical and dynamical processes are still poorly understood, and their representation in GCMs is a major source of uncertainties in overall cloud feedback in GCMs. Thus improving mixed-phase cloud parameterizations in climate models is critical to reducing the climate forecast uncertainties. This study aims at providing improved knowledge of mixed-phase cloud properties from the long-term ACRF observations and improving mixed-phase clouds simulations in the NCAR Community Atmosphere Model version 5 (CAM5). The key accomplishments are: 1) An improved retrieval algorithm was developed to provide liquid droplet concentration for drizzling or mixed-phase stratiform clouds. 2) A new ice concentration retrieval algorithm for stratiform mixed-phase clouds was developed. 3) A strong seasonal aerosol impact on ice generation in Arctic mixed-phase clouds was identified, which is mainly attributed to the high dust occurrence during the spring season. 4) A suite of multi-senor algorithms was applied to long-term ARM observations at the Barrow site to provide a complete dataset (LWC and effective radius profile for liquid phase, and IWC, Dge profiles and ice concentration for ice phase) to characterize Arctic stratiform mixed-phase clouds. This multi-year stratiform mixed-phase cloud dataset provides necessary information to study related processes, evaluate model stratiform mixed-phase cloud simulations, and improve model stratiform mixed-phase cloud parameterization. 5). A new in situ data analysis method was developed to quantify liquid mass partition in convective mixed-phase clouds. For the first time, we reliably compared liquid mass partitions in stratiform and convective mixed-phase clouds. Due to the different dynamics in stratiform and convective mixed-phase clouds, the temperature dependencies of liquid mass partitions are significantly different due to much higher ice concentrations in convective mixed phase clouds. 6) Systematic evaluations

Isotopic modeling of the sub-cloud evaporation effect in precipitation

International Nuclear Information System (INIS)

Salamalikis, V.; Argiriou, A.A.; Dotsika, E.

2016-01-01

In dry and warm environments sub-cloud evaporation influences the falling raindrops modifying their final stable isotopic content. During their descent from the cloud base towards the ground surface, through the unsaturated atmosphere, hydrometeors are subjected to evaporation whereas the kinetic fractionation results to less depleted or enriched isotopic signatures compared to the initial isotopic composition of the raindrops at cloud base. Nowadays the development of Generalized Climate Models (GCMs) that include isotopic content calculation modules are of great interest for the isotopic tracing of the global hydrological cycle. Therefore the accurate description of the underlying processes affecting stable isotopic content can improve the performance of iso-GCMs. The aim of this study is to model the sub-cloud evaporation effect using a) mixing and b) numerical isotope evaporation models. The isotope-mixing evaporation model simulates the isotopic enrichment (difference between the ground and the cloud base isotopic composition of raindrops) in terms of raindrop size, ambient temperature and relative humidity (RH) at ground level. The isotopic enrichment (Δδ) varies linearly with the evaporated raindrops mass fraction of the raindrop resulting to higher values at drier atmospheres and for smaller raindrops. The relationship between Δδ and RH is described by a ‘heat capacity’ model providing high correlation coefficients for both isotopes (R"2 > 80%) indicating that RH is an ideal indicator of the sub-cloud evaporation effect. Vertical distribution of stable isotopes in falling raindrops is also investigated using a numerical isotope-evaporation model. Temperature and humidity dependence of the vertical isotopic variation is clearly described by the numerical isotopic model showing an increase in the isotopic values with increasing temperature and decreasing RH. At an almost saturated atmosphere (RH = 95%) sub-cloud evaporation is negligible and the

Isotopic modeling of the sub-cloud evaporation effect in precipitation

Energy Technology Data Exchange (ETDEWEB)

Salamalikis, V., E-mail: vsalamalik@upatras.gr [Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR 26500 Patras (Greece); Argiriou, A.A. [Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR 26500 Patras (Greece); Dotsika, E. [Stable Isotope Unit, Institute of Nanoscience and Nanotechnology, National Center of Scientific Research ‘Demokritos’, Ag. Paraskevi Attikis, 15310 Athens (Greece)

2016-02-15

In dry and warm environments sub-cloud evaporation influences the falling raindrops modifying their final stable isotopic content. During their descent from the cloud base towards the ground surface, through the unsaturated atmosphere, hydrometeors are subjected to evaporation whereas the kinetic fractionation results to less depleted or enriched isotopic signatures compared to the initial isotopic composition of the raindrops at cloud base. Nowadays the development of Generalized Climate Models (GCMs) that include isotopic content calculation modules are of great interest for the isotopic tracing of the global hydrological cycle. Therefore the accurate description of the underlying processes affecting stable isotopic content can improve the performance of iso-GCMs. The aim of this study is to model the sub-cloud evaporation effect using a) mixing and b) numerical isotope evaporation models. The isotope-mixing evaporation model simulates the isotopic enrichment (difference between the ground and the cloud base isotopic composition of raindrops) in terms of raindrop size, ambient temperature and relative humidity (RH) at ground level. The isotopic enrichment (Δδ) varies linearly with the evaporated raindrops mass fraction of the raindrop resulting to higher values at drier atmospheres and for smaller raindrops. The relationship between Δδ and RH is described by a ‘heat capacity’ model providing high correlation coefficients for both isotopes (R{sup 2} > 80%) indicating that RH is an ideal indicator of the sub-cloud evaporation effect. Vertical distribution of stable isotopes in falling raindrops is also investigated using a numerical isotope-evaporation model. Temperature and humidity dependence of the vertical isotopic variation is clearly described by the numerical isotopic model showing an increase in the isotopic values with increasing temperature and decreasing RH. At an almost saturated atmosphere (RH = 95%) sub-cloud evaporation is negligible and the

The effects of the Boussinesq model to the rising of the explosion clouds

International Nuclear Information System (INIS)

Li Xiaoli; Zheng Yi

2010-01-01

It is to study the rising of the explosion clouds in the normal atmosphere using Boussinesq model and the Incompressible model, the numerical model is based on the assumption that effects the clouds are gravity and buoyancy. By comparing the evolvement of different density cloud, and gives the conclusion-the Boussinesq model and the Incompressible model is accord when the cloud's density is larger compared to the density of the environment. (authors)

Observations of Co-variation in Cloud Properties and their Relationships with Atmospheric State

Science.gov (United States)

Sinclair, K.; van Diedenhoven, B.; Fridlind, A. M.; Arnold, T. G.; Yorks, J. E.; Heymsfield, G. M.; McFarquhar, G. M.; Um, J.

2017-12-01

Radiative properties of upper tropospheric ice clouds are generally not well represented in global and cloud models. Cloud top height, cloud thermodynamic phase, cloud optical thickness, cloud water path, particle size and ice crystal shape all serve as observational targets for models to constrain cloud properties. Trends or biases in these cloud properties could have profound effects on the climate since they affect cloud radiative properties. Better understanding of co-variation between these cloud properties and linkages with atmospheric state variables can lead to better representation of clouds in models by reducing biases in their micro- and macro-physical properties as well as their radiative properties. This will also enhance our general understanding of cloud processes. In this analysis we look at remote sensing, in situ and reanalysis data from the MODIS Airborne Simulator (MAS), Cloud Physics Lidar (CPL), Cloud Radar System (CRS), GEOS-5 reanalysis data and GOES imagery obtained during the Tropical Composition, Cloud and Climate Coupling (TC4) airborne campaign. The MAS, CPL and CRS were mounted on the ER-2 high-altitude aircraft during this campaign. In situ observations of ice size and shape were made aboard the DC8 and WB57 aircrafts. We explore how thermodynamic phase, ice effective radius, particle shape and radar reflectivity vary with altitude and also investigate how these observed cloud properties vary with cloud type, cloud top temperature, relative humidity and wind profiles. Observed systematic relationships are supported by physical interpretations of cloud processes and any unexpected differences are examined.

Cloud fluid models of gas dynamics and star formation in galaxies

Science.gov (United States)

Struck-Marcell, Curtis; Scalo, John M.; Appleton, P. N.

1987-01-01

The large dynamic range of star formation in galaxies, and the apparently complex environmental influences involved in triggering or suppressing star formation, challenges the understanding. The key to this understanding may be the detailed study of simple physical models for the dominant nonlinear interactions in interstellar cloud systems. One such model is described, a generalized Oort model cloud fluid, and two simple applications of it are explored. The first of these is the relaxation of an isolated volume of cloud fluid following a disturbance. Though very idealized, this closed box study suggests a physical mechanism for starbursts, which is based on the approximate commensurability of massive cloud lifetimes and cloud collisional growth times. The second application is to the modeling of colliding ring galaxies. In this case, the driving processes operating on a dynamical timescale interact with the local cloud processes operating on the above timescale. The results is a variety of interesting nonequilibrium behaviors, including spatial variations of star formation that do not depend monotonically on gas density.

Providing a New Model for Discovering Cloud Services Based on Ontology

Directory of Open Access Journals (Sweden)

B. Heydari

2017-12-01

Full Text Available Due to its efficient, flexible, and dynamic substructure in information technology and service quality parameters estimation, cloud computing has become one of the most important issues in computer world. Discovering cloud services has been posed as a fundamental issue in reaching out high efficiency. In order to do one’s own operations in cloud space, any user needs to request several various services either simultaneously or according to a working routine. These services can be presented by different cloud producers or different decision-making policies. Therefore, service management is one of the important and challenging issues in cloud computing. With the advent of semantic web and practical services accordingly in cloud computing space, access to different kinds of applications has become possible. Ontology is the core of semantic web and can be used to ease the process of discovering services. A new model based on ontology has been proposed in this paper. The results indicate that the proposed model has explored cloud services based on user search results in lesser time compared to other models.

Observational Constraints for Modeling Diffuse Molecular Clouds

Science.gov (United States)

Federman, S. R.

2014-02-01

Ground-based and space-borne observations of diffuse molecular clouds suggest a number of areas where further improvements to modeling efforts is warranted. I will highlight those that have the widest applicability. The range in CO fractionation caused by selective isotope photodissociation, in particular the large 12C16O/13C16O ratios observed toward stars in Ophiuchus, is not reproduced well by current models. Our ongoing laboratory measurements of oscillator strengths and predissociation rates for Rydberg transitions in CO isotopologues may help clarify the situtation. The CH+ abundance continues to draw attention. Small scale structure seen toward ζ Per may provide additional constraints on the possible synthesis routes. The connection between results from optical transitions and those from radio and sub-millimeter wave transitions requires further effort. A study of OH+ and OH toward background stars reveals that these species favor different environments. This brings to focus the need to model each cloud along the line of sight separately, and to allow the physical conditions to vary within an individual cloud, in order to gain further insight into the chemistry. Now that an extensive set of data on molecular excitation is available, the models should seek to reproduce these data to place further constraints on the modeling results.

LIDAR, Point Clouds, and their Archaeological Applications

Energy Technology Data Exchange (ETDEWEB)

White, Devin A [ORNL

2013-01-01

It is common in contemporary archaeological literature, in papers at archaeological conferences, and in grant proposals to see heritage professionals use the term LIDAR to refer to high spatial resolution digital elevation models and the technology used to produce them. The goal of this chapter is to break that association and introduce archaeologists to the world of point clouds, in which LIDAR is only one member of a larger family of techniques to obtain, visualize, and analyze three-dimensional measurements of archaeological features. After describing how point clouds are constructed, there is a brief discussion on the currently available software and analytical techniques designed to make sense of them.

A Coupled GCM-Cloud Resolving Modeling System, and a Regional Scale Model to Study Precipitation Processes

Science.gov (United States)

Tao, Wei-Kuo

2007-01-01

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a superparameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (2ICE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generatio11 regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Prognostic cloud water in the Los Alamos general circulation model

International Nuclear Information System (INIS)

Kristjansson, J.E.; Kao, C.Y.J.

1994-01-01

Most of today's general circulation models (GCMs) have a greatly simplified treatment of condensation and clouds. Recent observational studies of the earth's radiation budget have suggested cloud-related feedback mechanisms to be of tremendous importance for the issue of global change. Thus, an urgent need for improvements in the treatment of clouds in GCMs has arisen, especially as the clouds relate to radiation. In this paper, we investigate the effects of introducing prognostic cloud water into the Los Alamos GCM. The cloud water field, produced by both stratiform and convective condensation, is subject to 3-dimensional advection and vertical diffusion. The cloud water enters the radiation calculations through the longwave emissivity calculations. Results from several sensitivity simulations show that realistic water and precipitation fields can be obtained with the applied method. Comparisons with observations show that the most realistic results are obtained when more sophisticated schemes for moist convection are introduced at the same time. The model's cold bias is reduced and the zonal winds becomes stronger because of more realistic tropical convection

A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

Science.gov (United States)

Hagos, Samson; Feng, Zhe; Plant, Robert S.; Houze, Robert A.; Xiao, Heng

2018-02-01

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud-base mass flux, given a large-scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud-base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud-base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge-discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud-base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

Lidar Penetration Depth Observations for Constraining Cloud Longwave Feedbacks

Science.gov (United States)

Vaillant de Guelis, T.; Chepfer, H.; Noel, V.; Guzman, R.; Winker, D. M.; Kay, J. E.; Bonazzola, M.

2017-12-01

Satellite-borne active remote sensing Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations [CALIPSO; Winker et al., 2010] and CloudSat [Stephens et al., 2002] provide direct measurements of the cloud vertical distribution, with a very high vertical resolution. The penetration depth of the laser of the lidar Z_Opaque is directly linked to the LongWave (LW) Cloud Radiative Effect (CRE) at Top Of Atmosphere (TOA) [Vaillant de Guélis et al., in review]. In addition, this measurement is extremely stable in time making it an excellent observational candidate to verify and constrain the cloud LW feedback mechanism [Chepfer et al., 2014]. In this work, we present a method to decompose the variations of the LW CRE at TOA using cloud properties observed by lidar [GOCCP v3.0; Guzman et al., 2017]. We decompose these variations into contributions due to changes in five cloud properties: opaque cloud cover, opaque cloud altitude, thin cloud cover, thin cloud altitude, and thin cloud emissivity [Vaillant de Guélis et al., in review]. We apply this method, in the real world, to the CRE variations of CALIPSO 2008-2015 record, and, in climate model, to LMDZ6 and CESM simulations of the CRE variations of 2008-2015 period and of the CRE difference between a warm climate and the current climate. In climate model simulations, the same cloud properties as those observed by CALIOP are extracted from the CFMIP Observation Simulator Package (COSP) [Bodas-Salcedo et al., 2011] lidar simulator [Chepfer et al., 2008], which mimics the observations that would be performed by the lidar on board CALIPSO satellite. This method, when applied on multi-model simulations of current and future climate, could reveal the altitude of cloud opacity level observed by lidar as a strong constrain for cloud LW feedback, since the altitude feedback mechanism is physically explainable and the altitude of cloud opacity accurately observed by lidar.

Intelligent Cloud Learning Model for Online Overseas Chinese Education

Directory of Open Access Journals (Sweden)

Yidong Chen

2015-02-01

Full Text Available With the development of Chinese economy, oversea Chinese education has been paid more and more attention. However, the overseas Chinese education resource is relatively lack because of historical reasons, which hindered further development . How to better share the Chinese education resources and provide intelligent personalized information service for overseas student is a key problem to be solved. In recent years, the rise of cloud computing provides us an opportunity to realize intelligent learning mode. Cloud computing offers some advantages by allowing users to use infrastructure, platforms and software . In this paper we proposed an intelligent cloud learning model based on cloud computing. The learning model can utilize network resources sufficiently to implement resource sharing according to the personal needs of students, and provide a good practicability for online overseas Chinese education.

Cloud blueprint : A model-driven approach to configuring federated clouds

NARCIS (Netherlands)

Papazoglou, M.; Abello, A.; Bellatreche, L.; Benatallah, B.

2012-01-01

Current cloud solutions are fraught with problems. They introduce a monolithic cloud stack that imposes vendor lock-in and donot permit developers to mix and match services freely from diverse cloud service tiers and configure them dynamically to address application needs. Cloud blueprinting is a

Representation of Arctic mixed-phase clouds and the Wegener-Bergeron-Findeisen process in climate models: Perspectives from a cloud-resolving study

Science.gov (United States)

Fan, Jiwen; Ghan, Steven; Ovchinnikov, Mikhail; Liu, Xiaohong; Rasch, Philip J.; Korolev, Alexei

2011-01-01

Two types of Arctic mixed-phase clouds observed during the ISDAC and M-PACE field campaigns are simulated using a 3-dimensional cloud-resolving model (CRM) with size-resolved cloud microphysics. The modeled cloud properties agree reasonably well with aircraft measurements and surface-based retrievals. Cloud properties such as the probability density function (PDF) of vertical velocity (w), cloud liquid and ice, regimes of cloud particle growth, including the Wegener-Bergeron-Findeisen (WBF) process, and the relationships among properties/processes in mixed-phase clouds are examined to gain insights for improving their representation in General Circulation Models (GCMs). The PDF of the simulated w is well represented by a Gaussian function, validating, at least for arctic clouds, the subgrid treatment used in GCMs. The PDFs of liquid and ice water contents can be approximated by Gamma functions, and a Gaussian function can describe the total water distribution, but a fixed variance assumption should be avoided in both cases. The CRM results support the assumption frequently used in GCMs that mixed phase clouds maintain water vapor near liquid saturation. Thus, ice continues to grow throughout the stratiform cloud but the WBF process occurs in about 50% of cloud volume where liquid and ice co-exist, predominantly in downdrafts. In updrafts, liquid and ice particles grow simultaneously. The relationship between the ice depositional growth rate and cloud ice strongly depends on the capacitance of ice particles. The simplified size-independent capacitance of ice particles used in GCMs could lead to large deviations in ice depositional growth.

Is ozone model bias driven by errors in cloud predictions? A quantitative assessment using satellite cloud retrievals in WRF-Chem

Science.gov (United States)

Ryu, Y. H.; Hodzic, A.; Barré, J.; Descombes, G.; Minnis, P.

2017-12-01

Clouds play a key role in radiation and hence O3 photochemistry by modulating photolysis rates and light-dependent emissions of biogenic volatile organic compounds (BVOCs). It is not well known, however, how much of the bias in O3 predictions is caused by inaccurate cloud predictions. This study quantifies the errors in surface O3 predictions associated with clouds in summertime over CONUS using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Cloud fields used for photochemistry are corrected based on satellite cloud retrievals in sensitivity simulations. It is found that the WRF-Chem model is able to detect about 60% of clouds in the right locations and generally underpredicts cloud optical depths. The errors in hourly O3 due to the errors in cloud predictions can be up to 60 ppb. On average in summertime over CONUS, the errors in 8-h average O3 of 1-6 ppb are found to be attributable to those in cloud predictions under cloudy sky conditions. The contribution of changes in photolysis rates due to clouds is found to be larger ( 80 % on average) than that of light-dependent BVOC emissions. The effects of cloud corrections on O­3 are about 2 times larger in VOC-limited than NOx-limited regimes, suggesting that the benefits of accurate cloud predictions would be greater in VOC-limited than NOx-limited regimes.

Study of tropical clouds feedback to a climate warming as simulated by climate models

International Nuclear Information System (INIS)

Brient, Florent

2012-01-01

The last IPCC report affirms the predominant role of low cloud-radiative feedbacks in the inter-model spread of climate sensitivity. Understanding the mechanisms that control the behavior of low-level clouds is thus crucial. However, the complexity of coupled ocean-atmosphere models and the large number of processes potentially involved make the analysis of this response difficult. To simplify the analysis and to identify the most critical controls of cloud feedbacks, we analyze the cloud response to climate change simulated by the IPSL-CM5A model in a hierarchy of configurations. A comparison between three model configurations (coupled, atmospheric and aqua-planet) using the same physical parametrizations shows that the cloud response to global warming is dominated by a decrease of low clouds in regimes of moderate subsidence. Using a Single Column Model, forced by weak subsidence large-scale forcing, allows us to reproduce the vertical cloud profile predicted in the 3D model, as well as its response to climate change (if a stochastic forcing is added on vertical velocity). We analyze the sensitivity of this low-cloud response to external forcing and also to uncertain parameters of physical parameterizations involved on the atmospheric model. Through a moist static energy (MSE) budget, we highlight several mechanisms: (1) Robust: Over weak subsidence regimes, the Clausius-Clapeyron relationship predicts that a warmer atmosphere leads to a increase of the vertical MSE gradient, resulting on a strengthening of the import of low-MSE from the free atmosphere into the cloudy boundary layer. The MSE budget links changes of vertical advection and cloud radiative effects. (2) Physics Model Dependent: The coupling between shallow convection, turbulence and cloud schemes allows the intensification of low-MSE transport so that cloud radiative cooling becomes 'less necessary' to balance the energy budget (Robust positive low cloud-radiative feedback for the model). The

Cloud ice: A climate model challenge with signs and expectations of progress

Science.gov (United States)

Waliser, Duane E.; Li, Jui-Lin F.; Woods, Christopher P.; Austin, Richard T.; Bacmeister, Julio; Chern, Jiundar; Del Genio, Anthony; Jiang, Jonathan H.; Kuang, Zhiming; Meng, Huan; Minnis, Patrick; Platnick, Steve; Rossow, William B.; Stephens, Graeme L.; Sun-Mack, Szedung; Tao, Wei-Kuo; Tompkins, Adrian M.; Vane, Deborah G.; Walker, Christopher; Wu, Dong

2009-04-01

Present-day shortcomings in the representation of upper tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high-quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global-scale measurements. With the relatively recent addition of satellite-derived products from Aura/Microwave Limb Sounder (MLS) and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their "cloud" ice and related fields. Using this information as background, we (1) discuss some of the main considerations and cautions that must be taken into account in making model-data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of

Microphysical variability of vigorous Amazonian deep convection observed by CloudSat, and relevance for cloud-resolving model

Science.gov (United States)

Dodson, J. B.; Taylor, P. C.

2017-12-01

The number and varieties of both satellite cloud observations and cloud simulations are increasing rapidly. This create a challenge in identifying the best methods for quantifying the physical processes associated with deep convection, and then comparing convective observations with simulations. The use of satellite simulators in conjunction with model output is an increasingly popular method of comparison studies. However, the complexity of deep convective systems renders simplistic comparison metrics hazardous, possibly resulting is misleading or even contradicting conclusions. To investigate this, CloudSat observations of Amazonian deep convective cores (DCCs) and associated anvils are compared and contrasted with output from cloud resolving models in a manner that both highlights microphysical proprties of observed convection, and displays the effects of microphysical parameterizations on allowing robust comparisons. First, contoured frequency by altitude diagrams (CFAD) are calculated from the reflectivity fields of DCCs observed by CloudSat. This reveals two distinct modes of hydrometeor variability in the high level cloud region, with one dominated by snow and aggregates, and the other by large graupel and hail. Second, output from the superparameterized Community Atmospheric Model (SP-CAM) data are processed with the Quickbeam radar simulator to produce CFADs which can be compared with the observed CFADs. Two versions of SP-CAM are used, with one (version 4) having single-moment microphysics which excludes graupel/hail, and the other (version 5) a double-moment scheme with graupel. The change from version 4 to 5 improves the reflectivity CFAD, even without corresponding changes to non-hydrometeor fields such as vertical velocity. However, it does not produce a realistic double hydrometeor mode. Finally, the influences of microphysics are further tested in the System for Atmospheric Modeling (SAM), which allows for higher control over model parameters than

Cloud-turbulence interactions: Sensitivity of a general circulation model to closure assumptions

International Nuclear Information System (INIS)

Brinkop, S.; Roeckner, E.

1993-01-01

Several approaches to parameterize the turbulent transport of momentum, heat, water vapour and cloud water for use in a general circulation model (GCM) have been tested in one-dimensional and three-dimensional model simulations. The schemes differ with respect to their closure assumptions (conventional eddy diffusivity model versus turbulent kinetic energy closure) and also regarding their treatment of cloud-turbulence interactions. The basis properties of these parameterizations are discussed first in column simulations of a stratocumulus-topped atmospheric boundary layer (ABL) under a strong subsidence inversion during the KONTROL experiment in the North Sea. It is found that the K-models tend to decouple the cloud layer from the adjacent layers because the turbulent activity is calculated from local variables. The higher-order scheme performs better in this respect because internally generated turbulence can be transported up and down through the action of turbulent diffusion. Thus, the TKE-scheme provides not only a better link between the cloud and the sub-cloud layer but also between the cloud and the inversion as a result of cloud-top entrainment. In the stratocumulus case study, where the cloud is confined by a pronounced subsidence inversion, increased entrainment favours cloud dilution through enhanced evaporation of cloud droplets. In the GCM study, however, additional cloud-top entrainment supports cloud formation because indirect cloud generating processes are promoted through efficient ventilation of the ABL, such as the enhanced moisture supply by surface evaporation and the increased depth of the ABL. As a result, tropical convection is more vigorous, the hydrological cycle is intensified, the whole troposphere becomes warmer and moister in general and the cloudiness in the upper part of the ABL is increased. (orig.)

Microphysical Modeling of Mineral Clouds in GJ1214 b and GJ436 b: Predicting Upper Limits on the Cloud-top Height

Science.gov (United States)

Ohno, Kazumasa; Okuzumi, Satoshi

2018-05-01

The ubiquity of clouds in the atmospheres of exoplanets, especially of super-Earths, is one of the outstanding issues for the transmission spectra survey. Understanding the formation process of clouds in super-Earths is necessary to interpret the observed spectra correctly. In this study, we investigate the vertical distributions of particle size and mass density of mineral clouds in super-Earths using a microphysical model that takes into account the vertical transport and growth of cloud particles in a self-consistent manner. We demonstrate that the vertical profiles of mineral clouds significantly vary with the concentration of cloud condensation nuclei and atmospheric metallicity. We find that the height of the cloud top increases with increasing metallicity as long as the metallicity is lower than the threshold. If the metallicity is larger than the threshold, the cloud-top height no longer increases appreciably with metallicity because coalescence yields larger particles of higher settling velocities. We apply our cloud model to GJ1214 b and GJ436 b, for which recent transmission observations suggest the presence of high-altitude opaque clouds. For GJ436 b, we show that KCl particles can ascend high enough to explain the observation. For GJ1214 b, by contrast, the height of KCl clouds predicted from our model is too low to explain its flat transmission spectrum. Clouds made of highly porous KCl particles could explain the observations if the atmosphere is highly metal-rich, and hence the particle microstructure might be a key to interpret the flat spectrum of GJ1214 b.

Modelling cloud data for prototype manufacturing

NARCIS (Netherlands)

Liu, G.H.; Wong, Y.S.; Zhang, Y.F.; Loh, H.T.

2003-01-01

In this paper, the authors have developed a novel method to integrate reverse engineering (RE) and rapid prototyping (RP). Unorganised cloud data are directly sliced and modelled with two-dimensional (2D) cross-sections. Based on such a 2D CAD model, the data points are directly converted into RP

Evaluating and Improving Cloud Processes in the Multi-Scale Modeling Framework

Energy Technology Data Exchange (ETDEWEB)

Ackerman, Thomas P. [Univ. of Washington, Seattle, WA (United States)

2015-03-01

The research performed under this grant was intended to improve the embedded cloud model in the Multi-scale Modeling Framework (MMF) for convective clouds by using a 2-moment microphysics scheme rather than the single moment scheme used in all the MMF runs to date. The technical report and associated documents describe the results of testing the cloud resolving model with fixed boundary conditions and evaluation of model results with data. The overarching conclusion is that such model evaluations are problematic because errors in the forcing fields control the results so strongly that variations in parameterization values cannot be usefully constrained

Constraining the models' response of tropical low clouds to SST forcings using CALIPSO observations

Science.gov (United States)

Cesana, G.; Del Genio, A. D.; Ackerman, A. S.; Brient, F.; Fridlind, A. M.; Kelley, M.; Elsaesser, G.

2017-12-01

Low-cloud response to a warmer climate is still pointed out as being the largest source of uncertainty in the last generation of climate models. To date there is no consensus among the models on whether the tropical low cloudiness would increase or decrease in a warmer climate. In addition, it has been shown that - depending on their climate sensitivity - the models either predict deeper or shallower low clouds. Recently, several relationships between inter-model characteristics of the present-day climate and future climate changes have been highlighted. These so-called emergent constraints aim to target relevant model improvements and to constrain models' projections based on current climate observations. Here we propose to use - for the first time - 10 years of CALIPSO cloud statistics to assess the ability of the models to represent the vertical structure of tropical low clouds for abnormally warm SST. We use a simulator approach to compare observations and simulations and focus on the low-layered clouds (i.e. z fraction. Vertically, the clouds deepen namely by decreasing the cloud fraction in the lowest levels and increasing it around the top of the boundary-layer. This feature is coincident with an increase of the high-level cloud fraction (z > 6.5km). Although the models' spread is large, the multi-model mean captures the observed variations but with a smaller amplitude. We then employ the GISS model to investigate how changes in cloud parameterizations affect the response of low clouds to warmer SSTs on the one hand; and how they affect the variations of the model's cloud profiles with respect to environmental parameters on the other hand. Finally, we use CALIPSO observations to constrain the model by determining i) what set of parameters allows reproducing the observed relationships and ii) what are the consequences on the cloud feedbacks. These results point toward process-oriented constraints of low-cloud responses to surface warming and environmental

BUSINESS MODELLING AND DATABASE DESIGN IN CLOUD COMPUTING

Directory of Open Access Journals (Sweden)

Mihai-Constantin AVORNICULUI

2015-04-01

Full Text Available Electronic commerce is growing constantly from one year to another in the last decade, few are the areas that also register such a growth. It covers the exchanges of computerized data, but also electronic messaging, linear data banks and electronic transfer payment. Cloud computing, a relatively new concept and term, is a model of access services via the internet to distributed systems of configurable calculus resources at request which can be made available quickly with minimum management effort and intervention from the client and the provider. Behind an electronic commerce system in cloud there is a data base which contains the necessary information for the transactions in the system. Using business modelling, we get many benefits, which makes the design of the database used by electronic commerce systems in cloud considerably easier.

Cloud Impacts on Pavement Temperature in Energy Balance Models

Science.gov (United States)

Walker, C. L.

2013-12-01

Forecast systems provide decision support for end-users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex, yet direct relationship exists between tire and pavement temperatures. Literature has shown that as tire temperature increases, friction decreases which affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focused on forecast improvement by determining how cloud type impacts the amount of shortwave radiation reaching the surface and subsequent pavement temperatures. The study region was the Great Plains where surface solar radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Road pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud properties and radiative transfer quantities were obtained from the Clouds and Earth's Radiant Energy System mission via Aqua and Terra Moderate Resolution Imaging Spectroradiometer satellite products. An additional cloud data set was incorporated from the Naval Research Laboratory Cloud Classification algorithm. Statistical analyses using a modified nearest neighbor approach were first performed relating shortwave radiation variability with road pavement temperature fluctuations. Then statistical associations were determined between the shortwave radiation and cloud property data sets. Preliminary results suggest that substantial pavement forecasting improvement is possible with the inclusion of cloud-specific information. Future model sensitivity testing seeks to quantify the magnitude of forecast improvement.

Single-Column Model Simulations of Subtropical Marine Boundary-Layer Cloud Transitions Under Weakening Inversions: SCM SIMULATIONS OF CLOUD TRANSITIONS

Energy Technology Data Exchange (ETDEWEB)

Neggers, R. A. J. [Institute for Geophysics and Meteorology, Department of Geosciences, University of Cologne, Cologne Germany; Royal Netherlands Meteorological Institute, De Bilt The Netherlands; Ackerman, A. S. [NASA Goddard Institute for Space Studies, New York NY USA; Angevine, W. M. [CIRES, University of Colorado, Boulder CO USA; NOAA Earth System Research Laboratory, Boulder CO USA; Bazile, E. [Météo France/CNRM, Toulouse France; Beau, I. [Météo France/ENM, Toulouse France; Blossey, P. N. [Department of Atmospheric Sciences, University of Washington, Seattle WA USA; Boutle, I. A. [Met Office, Exeter UK; de Bruijn, C. [Royal Netherlands Meteorological Institute, De Bilt The Netherlands; Cheng, A. [NOAA Center for Weather and Climate Prediction, Environmental Modeling Center, College Park MD USA; van der Dussen, J. [Department of Geoscience and Remote Sensing, Delft University of Technology, Delft The Netherlands; Fletcher, J. [Department of Atmospheric Sciences, University of Washington, Seattle WA USA; University of Leeds, Leeds UK; Dal Gesso, S. [Institute for Geophysics and Meteorology, Department of Geosciences, University of Cologne, Cologne Germany; Royal Netherlands Meteorological Institute, De Bilt The Netherlands; Jam, A. [Météo-France/CNRM & CNRS/IPSL/LMD, Toulouse France; Kawai, H. [Meteorological Research Institute, Climate Research Department, Japan Meteorological Agency, Tsukuba Japan; Cheedela, S. K. [Department of Atmosphere in the Earth System, Max-Planck Institut für Meteorologie, Hamburg Germany; Larson, V. E. [Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee WI USA; Lefebvre, M. -P. [Météo-France/CNRM & CNRS/IPSL/LMD, Toulouse France; Lock, A. P. [Met Office, Exeter UK; Meyer, N. R. [Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee WI USA; de Roode, S. R. [Department of Geoscience and Remote Sensing, Delft University of Technology, Delft The Netherlands; de Rooy, W. [Royal Netherlands Meteorological Institute, De Bilt The Netherlands; Sandu, I. [Section of Physical Aspects, European Centre for Medium-Range Weather Forecasts, Reading UK; Xiao, H. [University of California at Los Angeles, Los Angeles CA USA; Pacific Northwest National Laboratory, Richland WA USA; Xu, K. -M. [NASA Langley Research Centre, Hampton VI USA

2017-10-01

Results are presented of the GASS/EUCLIPSE single-column model inter-comparison study on the subtropical marine low-level cloud transition. A central goal is to establish the performance of state-of-the-art boundary-layer schemes for weather and climate mod- els for this cloud regime, using large-eddy simulations of the same scenes as a reference. A novelty is that the comparison covers four different cases instead of one, in order to broaden the covered parameter space. Three cases are situated in the North-Eastern Pa- cific, while one reflects conditions in the North-Eastern Atlantic. A set of variables is considered that reflects key aspects of the transition process, making use of simple met- rics to establish the model performance. Using this method some longstanding problems in low level cloud representation are identified. Considerable spread exists among models concerning the cloud amount, its vertical structure and the associated impact on radia- tive transfer. The sign and amplitude of these biases differ somewhat per case, depending on how far the transition has progressed. After cloud breakup the ensemble median ex- hibits the well-known “too few too bright” problem. The boundary layer deepening rate and its state of decoupling are both underestimated, while the representation of the thin capping cloud layer appears complicated by a lack of vertical resolution. Encouragingly, some models are successful in representing the full set of variables, in particular the verti- cal structure and diurnal cycle of the cloud layer in transition. An intriguing result is that the median of the model ensemble performs best, inspiring a new approach in subgrid pa- rameterization.

Cloud Infrastructure & Applications - CloudIA

Science.gov (United States)

Sulistio, Anthony; Reich, Christoph; Doelitzscher, Frank

The idea behind Cloud Computing is to deliver Infrastructure-as-a-Services and Software-as-a-Service over the Internet on an easy pay-per-use business model. To harness the potentials of Cloud Computing for e-Learning and research purposes, and to small- and medium-sized enterprises, the Hochschule Furtwangen University establishes a new project, called Cloud Infrastructure & Applications (CloudIA). The CloudIA project is a market-oriented cloud infrastructure that leverages different virtualization technologies, by supporting Service-Level Agreements for various service offerings. This paper describes the CloudIA project in details and mentions our early experiences in building a private cloud using an existing infrastructure.

Defining Generic Architecture for Cloud Infrastructure as a Service model

NARCIS (Netherlands)

Demchenko, Y.; de Laat, C.

2011-01-01

Infrastructure as a Service (IaaS) is one of the provisioning models for Clouds as defined in the NIST Clouds definition. Although widely used, current IaaS implementations and solutions doesn’t have common and well defined architecture model. The paper attempts to define a generic architecture for

Using cloud models of heartbeats as the entity identifier to secure mobile devices.

Science.gov (United States)

Fu, Donglai; Liu, Yanhua

2017-01-01

Mobile devices are extensively used to store more private and often sensitive information. Therefore, it is important to protect them against unauthorised access. Authentication ensures that authorised users can use mobile devices. However, traditional authentication methods, such as numerical or graphic passwords, are vulnerable to passive attacks. For example, an adversary can steal the password by snooping from a shorter distance. To avoid these problems, this study presents a biometric approach that uses cloud models of heartbeats as the entity identifier to secure mobile devices. Here, it is identified that these concepts including cloud model or cloud have nothing to do with cloud computing. The cloud model appearing in the study is the cognitive model. In the proposed method, heartbeats are collected by two ECG electrodes that are connected to one mobile device. The backward normal cloud generator is used to generate ECG standard cloud models characterising the heartbeat template. When a user tries to have access to their mobile device, cloud models regenerated by fresh heartbeats will be compared with ECG standard cloud models to determine if the current user can use this mobile device. This authentication method was evaluated from three aspects including accuracy, authentication time and energy consumption. The proposed method gives 86.04% of true acceptance rate with 2.73% of false acceptance rate. One authentication can be done in 6s, and this processing consumes about 2000 mW of power.

Satellite remote sensing of dust aerosol indirect effects on ice cloud formation.

Science.gov (United States)

Ou, Steve Szu-Cheng; Liou, Kuo-Nan; Wang, Xingjuan; Hansell, Richard; Lefevre, Randy; Cocks, Stephen

2009-01-20

We undertook a new approach to investigate the aerosol indirect effect of the first kind on ice cloud formation by using available data products from the Moderate-Resolution Imaging Spectrometer (MODIS) and obtained physical understanding about the interaction between aerosols and ice clouds. Our analysis focused on the examination of the variability in the correlation between ice cloud parameters (optical depth, effective particle size, cloud water path, and cloud particle number concentration) and aerosol optical depth and number concentration that were inferred from available satellite cloud and aerosol data products. Correlation results for a number of selected scenes containing dust and ice clouds are presented, and dust aerosol indirect effects on ice clouds are directly demonstrated from satellite observations.

A review of our understanding of the aerosol-cloud interaction from the perspective of a bin resolved cloud scale modelling

Science.gov (United States)

Flossmann, Andrea I.; Wobrock, Wolfram

2010-09-01

This review compiles the main results obtained using a mesoscale cloud model with bin resolved cloud micophysics and aerosol particle scavenging, as developed by our group over the years and applied to the simulation of shallow and deep convective clouds. The main features of the model are reviewed in different dynamical frameworks covering parcel model dynamics, as well as 1.5D, 2D and 3D dynamics. The main findings are summarized to yield a digested presentation which completes the general understanding of cloud-aerosol interaction, as currently available from textbook knowledge. Furthermore, it should provide support for general cloud model development, as it will suggest potentially minor processes that might be neglected with respect to more important ones and can support development of parameterizations for air quality, chemical transport and climate models. Our work has shown that in order to analyse dedicated campaign results, the supersaturation field and the complex dynamics of the specific clouds needs to be reproduced. Only 3D dynamics represents the variation of the supersaturation over the entire cloud, the continuous nucleation and deactivation of hydrometeors, and the dependence upon initial particle size distribution and solubility. However, general statements on certain processes can be obtained also by simpler dynamics. In particular, we found: Nucleation incorporates about 90% of the initial aerosol particle mass inside the cloud drops. Collision and coalescence redistributes the scavenged aerosol particle mass in such a way that the particle mass follows the main water mass. Small drops are more polluted than larger ones, as pollutant mass mixing ratio decreases with drops size. Collision and coalescence mixes the chemical composition of the generated drops. Their complete evaporation will release processed particles that are mostly larger and more hygroscopic than the initial particles. An interstitial aerosol is left unactivated between the

A State-of-the-Art Experimental Laboratory for Cloud and Cloud-Aerosol Interaction Research

Science.gov (United States)

Fremaux, Charles M.; Bushnell, Dennis M.

2011-01-01

The state of the art for predicting climate changes due to increasing greenhouse gasses in the atmosphere with high accuracy is problematic. Confidence intervals on current long-term predictions (on the order of 100 years) are so large that the ability to make informed decisions with regard to optimum strategies for mitigating both the causes of climate change and its effects is in doubt. There is ample evidence in the literature that large sources of uncertainty in current climate models are various aerosol effects. One approach to furthering discovery as well as modeling, and verification and validation (V&V) for cloud-aerosol interactions is use of a large "cloud chamber" in a complimentary role to in-situ and remote sensing measurement approaches. Reproducing all of the complex interactions is not feasible, but it is suggested that the physics of certain key processes can be established in a laboratory setting so that relevant fluid-dynamic and cloud-aerosol phenomena can be experimentally simulated and studied in a controlled environment. This report presents a high-level argument for significantly improved laboratory capability, and is meant to serve as a starting point for stimulating discussion within the climate science and other interested communities.

Using satellites and global models to investigate aerosol-cloud interactions

Science.gov (United States)

Gryspeerdt, E.; Quaas, J.; Goren, T.; Sourdeval, O.; Mülmenstädt, J.

2017-12-01

Aerosols are known to impact liquid cloud properties, through both microphysical and radiative processes. Increasing the number concentration of aerosol particles can increase the cloud droplet number concentration (CDNC). Through impacts on precipitation processes, this increase in CDNC may also be able to impact the cloud fraction (CF) and the cloud liquid water path (LWP). Several studies have looked into the effect of aerosols on the CDNC, but as the albedo of a cloudy scene depends much more strongly on LWP and CF, an aerosol influence on these properties could generate a significant radiative forcing. While the impact of aerosols on cloud properties can be seen in case studies involving shiptracks and volcanoes, producing a global estimate of these effects remains challenging due to the confounding effect of local meteorology. For example, relative humidity significantly impacts the aerosol optical depth (AOD), a common satellite proxy for CCN, as well as being a strong control on cloud properties. This can generate relationships between AOD and cloud properties, even when there is no impact of aerosol-cloud interactions. In this work, we look at how aerosol-cloud interactions can be distinguished from the effect of local meteorology in satellite studies. With a combination global climate models and multiple sources of satellite data, we show that the choice of appropriate mediating variables and case studies can be used to develop constraints on the aerosol impact on CF and LWP. This will lead to improved representations of clouds in global climate models and help to reduce the uncertainty in the global impact of anthropogenic aerosols on cloud properties.

Multi-Spectral Cloud Retrievals from Moderate Image Spectrometer (MODIS)

Science.gov (United States)

Platnick, Steven

2004-01-01

MODIS observations from the NASA EOS Terra spacecraft (1030 local time equatorial sun-synchronous crossing) launched in December 1999 have provided a unique set of Earth observation data. With the launch of the NASA EOS Aqua spacecraft (1330 local time crossing! in May 2002: two MODIS daytime (sunlit) and nighttime observations are now available in a 24-hour period allowing some measure of diurnal variability. A comprehensive set of remote sensing algorithms for cloud masking and the retrieval of cloud physical and optical properties has been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate modeling, climate change studies, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. An overview of the instrument and cloud algorithms will be presented along with various examples, including an initial analysis of several operational global gridded (Level-3) cloud products from the two platforms. Statistics of cloud optical and microphysical properties as a function of latitude for land and Ocean regions will be shown. Current algorithm research efforts will also be discussed.

Model simulations of aerosol effects on clouds and precipitation in comparison with ARM data

Energy Technology Data Exchange (ETDEWEB)

Penner, Joyce E. [Univ. of Michigan, Ann Arbor, MI (United States); Zhou, Cheng [Univ. of Michigan, Ann Arbor, MI (United States)

2017-01-12

Observation-based studies have shown that the aerosol cloud lifetime effect or the increase of cloud liquid water path (LWP) with increased aerosol loading may have been overestimated in climate models. Here, we simulate shallow warm clouds on 05/27/2011 at the Southern Great Plains (SGP) measurement site established by Department of Energy's Atmospheric Radiation Measurement (ARM) Program using a single column version of a global climate model (Community Atmosphere Model or CAM) and a cloud resolving model (CRM). The LWP simulated by CAM increases substantially with aerosol loading while that in the CRM does not. The increase of LWP in CAM is caused by a large decrease of the autoconversion rate when cloud droplet number increases. In the CRM, the autoconversion rate is also reduced, but this is offset or even outweighed by the increased evaporation of cloud droplets near cloud top, resulting in an overall decrease in LWP. Our results suggest that climate models need to include the dependence of cloud top growth and the evaporation/condensation process on cloud droplet number concentrations.

Baryon magnetic moments in the quark model and pion cloud contributions

International Nuclear Information System (INIS)

Sato, Toshiro; Sawada, Shoji

1981-01-01

Baryon magnetic moment is studied paying attention to the effects of pion cloud which is surrounding the 'bare' baryon whose magnetic moment is given by the quark model with broken SU(6) symmetry. The precisely measured nucleon magnetic moments are reproduced by the pion cloud contributions from the distance larger than 1.4 fm. The effects of pion cloud on the hyperon magnetic moments are also discussed. It is shown that the pion cloud contributions largely reduce the discrepancies between the quark model predictions and the recent accurate experimental data on the hyperon magnetic moments. (author)

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

Science.gov (United States)

Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

2017-11-01

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using

Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

Science.gov (United States)

Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using

MODELING DUST IN THE MAGELLANIC CLOUDS

Energy Technology Data Exchange (ETDEWEB)

Zonca, Alberto; Casu, Silvia; Mulas, Giacomo; Aresu, Giambattista [INAF—Osservatorio Astronomico di Cagliari, Via della Scienza 5, I-09047 Selargius (Italy); Cecchi-Pestellini, Cesare, E-mail: azonca@oa-cagliari.inaf.it, E-mail: silvia@oa-cagliari.inaf.it, E-mail: gmulas@oa-cagliari.inaf.it, E-mail: garesu@oa-cagliari.inaf.it, E-mail: cecchi-pestellini@astropa.inaf.it [INAF—Osservatorio Astronomico di Palermo, P.za Parlamento 1, I-90134 Palermo (Italy)

2015-09-01

We model the extinction profiles observed in the Small and Large Magellanic clouds with a synthetic population of dust grains consisting of core-mantle particles and a collection of free-flying polycyclic aromatic hydrocarbons (PAHs). All different flavors of the extinction curves observed in the Magellanic Clouds (MCs) can be described by the present model, which has been previously (successfully) applied to a large sample of diffuse and translucent lines of sight in the Milky Way. We find that in the MCs the extinction produced by classical grains is generally larger than absorption by PAHs. Within this model, the nonlinear far-UV rise is accounted for by PAHs, whose presence in turn is always associated with a gap in the size distribution of classical particles. This hints either at a physical connection between (e.g., a common cause for) PAHs and the absence of middle-sized dust particles or the need for an additional component in the model that can account for the nonlinear far-UV rise without contributing to the UV bump at ∼217 nm such as, e.g., nanodiamonds.

MODELING DUST IN THE MAGELLANIC CLOUDS

International Nuclear Information System (INIS)

Zonca, Alberto; Casu, Silvia; Mulas, Giacomo; Aresu, Giambattista; Cecchi-Pestellini, Cesare

2015-01-01

We model the extinction profiles observed in the Small and Large Magellanic clouds with a synthetic population of dust grains consisting of core-mantle particles and a collection of free-flying polycyclic aromatic hydrocarbons (PAHs). All different flavors of the extinction curves observed in the Magellanic Clouds (MCs) can be described by the present model, which has been previously (successfully) applied to a large sample of diffuse and translucent lines of sight in the Milky Way. We find that in the MCs the extinction produced by classical grains is generally larger than absorption by PAHs. Within this model, the nonlinear far-UV rise is accounted for by PAHs, whose presence in turn is always associated with a gap in the size distribution of classical particles. This hints either at a physical connection between (e.g., a common cause for) PAHs and the absence of middle-sized dust particles or the need for an additional component in the model that can account for the nonlinear far-UV rise without contributing to the UV bump at ∼217 nm such as, e.g., nanodiamonds

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Directory of Open Access Journals (Sweden)

Ye Jin

2018-04-01

Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

A Diagnostic PDF Cloud Scheme to Improve Subtropical Low Clouds in NCAR Community Atmosphere Model (CAM5)

Science.gov (United States)

Qin, Yi; Lin, Yanluan; Xu, Shiming; Ma, Hsi-Yen; Xie, Shaocheng

2018-02-01

Low clouds strongly impact the radiation budget of the climate system, but their simulation in most GCMs has remained a challenge, especially over the subtropical stratocumulus region. Assuming a Gaussian distribution for the subgrid-scale total water and liquid water potential temperature, a new statistical cloud scheme is proposed and tested in NCAR Community Atmospheric Model version 5 (CAM5). The subgrid-scale variance is diagnosed from the turbulent and shallow convective processes in CAM5. The approach is able to maintain the consistency between cloud fraction and cloud condensate and thus alleviates the adjustment needed in the default relative humidity-based cloud fraction scheme. Short-term forecast simulations indicate that low cloud fraction and liquid water content, including their diurnal cycle, are improved due to a proper consideration of subgrid-scale variance over the southeastern Pacific Ocean region. Compared with the default cloud scheme, the new approach produced the mean climate reasonably well with improved shortwave cloud forcing (SWCF) due to more reasonable low cloud fraction and liquid water path over regions with predominant low clouds. Meanwhile, the SWCF bias over the tropical land regions is also alleviated. Furthermore, the simulated marine boundary layer clouds with the new approach extend further offshore and agree better with observations. The new approach is able to obtain the top of atmosphere (TOA) radiation balance with a slightly alleviated double ITCZ problem in preliminary coupled simulations. This study implies that a close coupling of cloud processes with other subgrid-scale physical processes is a promising approach to improve cloud simulations.

Geographical point cloud modelling with the 3D medial axis transform

NARCIS (Netherlands)

Peters, R.Y.

2018-01-01

A geographical point cloud is a detailed three-dimensional representation of the geometry of our geographic environment.
Using geographical point cloud modelling, we are able to extract valuable information from geographical point clouds that can be used for applications in asset management,

Enabling Virtual Sensing as a Service

Directory of Open Access Journals (Sweden)

Yang Li

2016-03-01

Full Text Available In many situations, placing a physical sensor in the ideal position in or on the human body to acquire sensing data is incredibly difficult. Virtual sensors, in contrast to physical sensors, can provide indirect measurements by making use of other available sensor data. In this paper, we demonstrate a virtual sensing application developed as a service on top of a cloud-based health sensor data management platform called Wiki-Health. The proposed application “implants” virtual sensors in the human body by integrating environmental, geographic and personal sensor data with physiological models to compute temperature estimations of various parts of the body. The feasibility of the proposed virtual sensing service is supported by a case study. The ability to share computational models relevant to do calculations on measured data on the go is also discussed.

Shallow layer modelling of dense gas clouds

Energy Technology Data Exchange (ETDEWEB)

Ott, S.; Nielsen, M.

1996-11-01

The motivation for making shallow layer models is that they can deal with the dynamics of gravity driven flow in complex terrain at a modest computational cost compared to 3d codes. The main disadvantage is that the air-cloud interactions still have to be added `by hand`, where 3d models inherit the correct dynamics from the fundamental equations. The properties of the inviscid shallow water equations are discussed, focusing on existence and uniqueness of solutions. It is demonstrated that breaking waves and fronts pose severe problems, that can only be overcome if the hydrostatic approximation is given up and internal friction is added to the model. A set of layer integrated equations is derived starting from the Navier-Stokes equations. The various steps in the derivation are accompanied by plausibility arguments. These form the scientific basis of the model. The principle of least action is introduced as a means of generating consistent models, and as a tool for making discrete equations for numerical models, which automatically obey conservation laws. A numerical model called SLAM (Shallow LAyer Model) is presented. SLAM has some distinct features compared to other shallow layer models: A Lagrangian, moving grid; Explicit account for the turbulent kinetic energy budget; The entrainment rate is estimated on the basis of the local turbulent kinetic energy; Non-hydrostatic pressure; and Numerical methods respect conservation laws even for coarse grids. Thorney Island trial 8 is used as a reference case model tuning. The model reproduces the doughnut shape of the cloud and yield concentrations in reasonable agreement with observations, even when a small number of cells (e.g. 16) is used. It is concluded that lateral exchange of matter within the cloud caused by shear is important, and that the model should be improved on this point. (au) 16 ills., 38 refs.

Combining observations and models to reduce uncertainty in the cloud response to global warming

Science.gov (United States)

Norris, J. R.; Myers, T.; Chellappan, S.

2017-12-01

Currently there is large uncertainty on how subtropical low-level clouds will respond to global warming and whether they will act as a positive feedback or negative feedback. Global climate models substantially agree on what changes in atmospheric structure and circulation will occur with global warming but greatly disagree over how clouds will respond to these changes in structure and circulation. An examination of models with the most realistic simulations of low-level cloudiness indicates that the model cloud response to atmospheric changes associated with global warming is quantitatively similar to the model cloud response to atmospheric changes at interannual time scales. For these models, the cloud response to global warming predicted by multilinear regression using coefficients derived from interannual time scales is quantitatively similar to the cloud response to global warming directly simulated by the model. Since there is a large spread among cloud response coefficients even among models with the most realistic cloud simulations, substitution of coefficients derived from satellite observations reduces the uncertainty range of the low-level cloud feedback. Increased sea surface temperature associated with global warming acts to reduce low-level cloudiness, which is partially offset by increased lower tropospheric stratification that acts to enhance low-level cloudiness. Changes in free-tropospheric relative humidity, subsidence, and horizontal advection have only a small impact on low-level cloud. The net reduction in subtropical low-level cloudiness increases absorption of solar radiation by the climate system, thus resulting in a weak positive feedback.

Lidar Cloud Detection with Fully Convolutional Networks

Science.gov (United States)

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%.

A modeling perspective on cloud radiative forcing

International Nuclear Information System (INIS)

Potter, G.L.; Corsetti, L.; Slingo, J.M.

1993-02-01

Radiation fields from a perpetual July integration of a T106 version of the ECM-WF operational model are used to identify the most appropriate way to diagnose cloud radiative forcing in a general circulation model, for the purposes of intercomparison between models. Differences between the Methods I and II of Cess and Potter (1987) and a variant method are addressed. Method I is shown to be the least robust of all methods, due to the potential uncertainties related to persistent cloudiness, length of the sampling period and biases in retrieved clear-sky quantities due to insufficient sampling of the diurnal cycle. Method II is proposed as an unambiguous way to produce consistent radiative diagnostics for intercomparing model results. The impact of the three methods on the derived sensitivities and cloud feedbacks following an imposed change in sea surface temperature is discussed. The sensitivity of the results to horizontal resolution is considered by using the diagnostics from parallel integrations with T21 version of the model

Insights on the Feasibility, Modeling and Field Testing of Cirrus Cloud Thinning from Satellite Remote Sensing

Science.gov (United States)

Mitchell, D. L.; Garnier, A.; Mejia, J.; Avery, M. A.; Erfani, E.

2016-12-01

To date, it is not clear whether the climate intervention method known as cirrus cloud thinning (CCT) can be viable since it requires cirrus clouds to form through homogeneous ice nucleation (henceforth hom) and some recent GCM studies predict cirrus are formed primarily through heterogeneous ice nucleation (henceforth het). A new CALIPSO infrared retrieval method has been developed for single-layer cirrus cloud that measures the temperature dependence of their layer-averaged number concentration N, effective diameter De and ice water content for optical depths (OD) between 0.3 and 3.0. Based on N, the prevailing ice nucleation mechanism (hom or het) can be estimated as a function of temperature, season, latitude and surface type. These satellite results indicate that seeding cirrus clouds at high latitudes during winter may produce significant global surface cooling. This is because hom often appears to dominate over land during winter north of 30°N latitude while the same appears true for most of the Southern Hemisphere (south of 30°S) during all seasons. Moreover, the sampled cirrus cloud frequency of occurrence in the Arctic is at least twice as large during winter relative to other seasons, while frequency of occurrence in the Antarctic peaks in the spring and is second-highest during winter. During Arctic winter, a combination of frequent hom cirrus, maximum cirrus coverage and an extreme or absent sun angle produces the maximum seasonal cirrus net radiative forcing (warming). Thus a reduction in OD and coverage (via CCT) for these cirrus clouds could yield a significant net cooling effect. From these CALIPSO retrievals, De-T relationships are generated as a function of season, latitude and surface type (land vs. ocean). These will be used in CAM5 to estimate De and the ice fall speed, from which the cirrus radiative forcing will be estimated during winter north of 30°latitude, where hom cirrus are common. Another CAM5 simulation will replace the hom

Space Science Cloud: a Virtual Space Science Research Platform Based on Cloud Model

Science.gov (United States)

Hu, Xiaoyan; Tong, Jizhou; Zou, Ziming

Through independent and co-operational science missions, Strategic Pioneer Program (SPP) on Space Science, the new initiative of space science program in China which was approved by CAS and implemented by National Space Science Center (NSSC), dedicates to seek new discoveries and new breakthroughs in space science, thus deepen the understanding of universe and planet earth. In the framework of this program, in order to support the operations of space science missions and satisfy the demand of related research activities for e-Science, NSSC is developing a virtual space science research platform based on cloud model, namely the Space Science Cloud (SSC). In order to support mission demonstration, SSC integrates interactive satellite orbit design tool, satellite structure and payloads layout design tool, payload observation coverage analysis tool, etc., to help scientists analyze and verify space science mission designs. Another important function of SSC is supporting the mission operations, which runs through the space satellite data pipelines. Mission operators can acquire and process observation data, then distribute the data products to other systems or issue the data and archives with the services of SSC. In addition, SSC provides useful data, tools and models for space researchers. Several databases in the field of space science are integrated and an efficient retrieve system is developing. Common tools for data visualization, deep processing (e.g., smoothing and filtering tools), analysis (e.g., FFT analysis tool and minimum variance analysis tool) and mining (e.g., proton event correlation analysis tool) are also integrated to help the researchers to better utilize the data. The space weather models on SSC include magnetic storm forecast model, multi-station middle and upper atmospheric climate model, solar energetic particle propagation model and so on. All the services above-mentioned are based on the e-Science infrastructures of CAS e.g. cloud storage and

Response to marine cloud brightening in a multi-model ensemble

Directory of Open Access Journals (Sweden)

C. W. Stjern

2018-01-01

Full Text Available Here we show results from Earth system model simulations from the marine cloud brightening experiment G4cdnc of the Geoengineering Model Intercomparison Project (GeoMIP. The nine contributing models prescribe a 50 % increase in the cloud droplet number concentration (CDNC of low clouds over the global oceans in an experiment dubbed G4cdnc, with the purpose of counteracting the radiative forcing due to anthropogenic greenhouse gases under the RCP4.5 scenario. The model ensemble median effective radiative forcing (ERF amounts to −1.9 W m−2, with a substantial inter-model spread of −0.6 to −2.5 W m−2. The large spread is partly related to the considerable differences in clouds and their representation between the models, with an underestimation of low clouds in several of the models. All models predict a statistically significant temperature decrease with a median of (for years 2020–2069 −0.96 [−0.17 to −1.21] K relative to the RCP4.5 scenario, with particularly strong cooling over low-latitude continents. Globally averaged there is a weak but significant precipitation decrease of −2.35 [−0.57 to −2.96] % due to a colder climate, but at low latitudes there is a 1.19 % increase over land. This increase is part of a circulation change where a strong negative top-of-atmosphere (TOA shortwave forcing over subtropical oceans, caused by increased albedo associated with the increasing CDNC, is compensated for by rising motion and positive TOA longwave signals over adjacent land regions.

RACLOUDS - Model for Clouds Risk Analysis in the Information Assets Context

Directory of Open Access Journals (Sweden)

SILVA, P. F.

2016-06-01

Full Text Available Cloud computing offers benefits in terms of availability and cost, but transfers the responsibility of information security management for the cloud service provider. Thus the consumer loses control over the security of their information and services. This factor has prevented the migration to cloud computing in many businesses. This paper proposes a model where the cloud consumer can perform risk analysis on providers before and after contracting the service. The proposed model establishes the responsibilities of three actors: Consumer, Provider and Security Labs. The inclusion of actor Security Labs provides more credibility to risk analysis making the results more consistent for the consumer.

The Invigoration of Deep Convective Clouds Over the Atlantic: Aerosol Effect, Meteorology or Retrieval Artifact?

Science.gov (United States)

Koren, Ilan; Feingold, Graham; Remer, Lorraine A.

2010-01-01

Associations between cloud properties and aerosol loading are frequently observed in products derived from satellite measurements. These observed trends between clouds and aerosol optical depth suggest aerosol modification of cloud dynamics, yet there are uncertainties involved in satellite retrievals that have the potential to lead to incorrect conclusions. Two of the most challenging problems are addressed here: the potential for retrieved aerosol optical depth to be cloud-contaminated, and as a result, artificially correlated with cloud parameters; and the potential for correlations between aerosol and cloud parameters to be erroneously considered to be causal. Here these issues are tackled directly by studying the effects of the aerosol on convective clouds in the tropical Atlantic Ocean using satellite remote sensing, a chemical transport model, and a reanalysis of meteorological fields. Results show that there is a robust positive correlation between cloud fraction or cloud top height and the aerosol optical depth, regardless of whether a stringent filtering of aerosol measurements in the vicinity of clouds is applied, or not. These same positive correlations emerge when replacing the observed aerosol field with that derived from a chemical transport model. Model-reanalysis data is used to address the causality question by providing meteorological context for the satellite observations. A correlation exercise between the full suite of meteorological fields derived from model reanalysis and satellite-derived cloud fields shows that observed cloud top height and cloud fraction correlate best with model pressure updraft velocity and relative humidity. Observed aerosol optical depth does correlate with meteorological parameters but usually different parameters from those that correlate with observed cloud fields. The result is a near-orthogonal influence of aerosol and meteorological fields on cloud top height and cloud fraction. The results strengthen the case

Icecube: Spaceflight Validation of an 874-GHz Submillimeter Wave Radiometer for Ice Cloud Remote Sensing

Science.gov (United States)

Wu, D. L.; Esper, J.; Ehsan, N.; Piepmeier, J. R.; Racette, P.

2014-12-01

Ice clouds play a key role in the Earth's radiation budget, mostly through their strong regulation of infrared radiation exchange. Submillimeter wave remote sensing offers a unique capability to improve cloud ice measurements from space. At 874 GHz cloud scattering produces a larger brightness temperature depression from cirrus than lower frequencies, which can be used to retrieve vertically-integrated cloud ice water path (IWP) and ice particle size. The objective of the IceCube project is to retire risks of 874-GHz receiver technology by raising its TRL from 5 to 7. The project will demonstrate, on a 3-U CubeSat in a low Earth orbit (LEO) environment, the 874-GHz receiver system with noise equivalent differential temperature (NEDT) of ~0.2 K for 1-second integration and calibration error of 2.0 K or less as measured from deep-space observations. The Goddard Space Flight Center (GSFC) is partnering with Virginia Diodes, Inc (VDI) to qualify commercially available 874-GHz receiver technology for spaceflight, and demonstrate the radiometer performance. The instrument (submm-wave cloud radiometer, or SCR), along with the CubeSat system developed and integrated by GSFC, will be ready for launch in two years. The instrument subsystem includes a reflector antenna, sub-millimeter wave mixer, frequency multipliers and stable local oscillator, an intermediate frequency (IF) circuit with noise injection, and data-power boards. The mixer and frequency multipliers are procured from VDI with GSFC insight into fabrication and testing processes to ensure scalability to spaceflight beyond TRL 7. The remaining components are a combination of GSFC-designed and commercial off-the-shelf (COTS) at TRLs of 5 or higher. The spacecraft system is specified by GSFC and comprises COTS components including three-axis stabilizer and sun sensor, GPS receiver, deployable solar arrays, UHF radio, and 2 GB of on-board storage. The spacecraft and instrument are integrated and flight qualified

Personal lifelong user model clouds

DEFF Research Database (Denmark)

Dolog, Peter; Kay, Judy; Kummerfeld, Bob

This paper explores an architecture for very long term user modelling, based upon personal user model clouds. These ensure that the individual's applications can access their model whenever it is needed. At the same time, the user can control the use of their user model. So, they can ensure...... which combines both. Finally we discuss implications of our approach for consistency and freshness of the user model information....... it is accessed only when and where they wish, by applications that they wish. We consider the challenges of representing user models so that they can be reused by multiple applications. We indicate potential synergies between distributed and centralised user modelling architectures, proposing an architecture...

Security Issues Model on Cloud Computing: A Case of Malaysia

OpenAIRE

Komeil Raisian; Jamaiah Yahaya

2015-01-01

By developing the cloud computing, viewpoint of many people regarding the infrastructure architectures, software distribution and improvement model changed significantly. Cloud computing associates with the pioneering deployment architecture, which could be done through grid calculating, effectiveness calculating and autonomic calculating. The fast transition towards that, has increased the worries regarding a critical issue for the effective transition of cloud computing. From the security v...

Integrating Cloud-Computing-Specific Model into Aircraft Design

Science.gov (United States)

Zhimin, Tian; Qi, Lin; Guangwen, Yang

Cloud Computing is becoming increasingly relevant, as it will enable companies involved in spreading this technology to open the door to Web 3.0. In the paper, the new categories of services introduced will slowly replace many types of computational resources currently used. In this perspective, grid computing, the basic element for the large scale supply of cloud services, will play a fundamental role in defining how those services will be provided. The paper tries to integrate cloud computing specific model into aircraft design. This work has acquired good results in sharing licenses of large scale and expensive software, such as CFD (Computational Fluid Dynamics), UG, CATIA, and so on.

Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms

Science.gov (United States)

Hamann, U.; Walther, A.; Baum, B.; Bennartz, R.; Bugliaro, L.; Derrien, M.; Francis, P. N.; Heidinger, A.; Joro, S.; Kniffka, A.;

A business model for a South African government public cloud platform

CSIR Research Space (South Africa)

Mvelase, P

2014-05-01

Full Text Available of public services is conducted. This paper designs a cloud business model that suits South Africa’s perspective. The idea is to model a government public cloud which does not interfere with the secured business functions of the government but find a...

Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Directory of Open Access Journals (Sweden)

Xicheng Tan

2015-10-01

Full Text Available Since the Open Geospatial Consortium (OGC proposed the geospatial Web Processing Service (WPS, standard OGC Web Service (OWS-based geospatial processing has become the major type of distributed geospatial application. However, improving the performance and sustainability of the distributed geospatial applications has become the dominant challenge for OWSs. This paper presents the construction of an elastic parallel OGC WPS service on a cloud-based cluster and the designs of a high-performance, cloud-based WPS service architecture, the scalability scheme of the cloud, and the algorithm of the elastic parallel geoprocessing. Experiments of the remote sensing data processing service demonstrate that our proposed method can provide a higher-performance WPS service that uses less computing resources. Our proposed method can also help institutions reduce hardware costs, raise the rate of hardware usage, and conserve energy, which is important in building green and sustainable geospatial services or applications.

Cloud diagnosis impact on deposition modelling applied to the Fukushima accident

Science.gov (United States)

Quérel, Arnaud; Quélo, Denis; Roustan, Yelva; Mathieu, Anne

2017-04-01

The accident at the Fukushima Daiichi Nuclear Power Plant in Japan in March 2011 resulted in the release of several hundred PBq of activity into the environment. Most of the radioactivity was released in a time period of about 40 days. Radioactivity was dispersed in the atmosphere and the ocean and subsequently traces of radionuclides were detected all over Japan. At the Fukushima airport for instance, a deposit as large as 36 kBq/m2 of Cs-137 was measured resulting of an atmospheric deposition of the plume. Both dry and wet deposition were probably involved since a raining event occurred on the 15th of March when the plume was passing nearby. The accident scenario have given rise to a number of scientific investigations. Atmospheric deposition, for example, was studied by utilizing atmospheric transport models. In atmospheric transport models, some parameters, such as cloud diagnosis, are derived from meteorological data. This cloud diagnosis is a key issue for wet deposition modelling since it allows to distinguish between two processes: in-cloud scavenging which corresponds to the collection of radioactive particles into the cloud and below-cloud scavenging consequent to the removal of radioactive material due to the falling drops. Several parametrizations of cloud diagnosis exist in the literature, using different input data: relative humidity, liquid water content, also. All these diagnosis return a large range of cloud base heights and cloud top heights. In this study, computed cloud diagnostics are compared to the observations at the Fukushima airport. Atmospheric dispersion simulations at Japan scale are then performed utilizing the most reliable ones. Impact on results are discussed.

Cloud Computing Platform for an Online Model Library System

Directory of Open Access Journals (Sweden)

Mingang Chen

2013-01-01

Full Text Available The rapid developing of digital content industry calls for online model libraries. For the efficiency, user experience, and reliability merits of the model library, this paper designs a Web 3D model library system based on a cloud computing platform. Taking into account complex models, which cause difficulties in real-time 3D interaction, we adopt the model simplification and size adaptive adjustment methods to make the system with more efficient interaction. Meanwhile, a cloud-based architecture is developed to ensure the reliability and scalability of the system. The 3D model library system is intended to be accessible by online users with good interactive experiences. The feasibility of the solution has been tested by experiments.

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

Science.gov (United States)

Krueger, Steven K.

1998-01-01

The interactions between sea ice, open ocean, atmospheric radiation, and clouds over the Arctic Ocean exert a strong influence on global climate. Uncertainties in the formulation of interactive air-sea-ice processes in global climate models (GCMs) result in large differences between the Arctic, and global, climates simulated by different models. Arctic stratus clouds are not well-simulated by GCMs, yet exert a strong influence on the surface energy budget of the Arctic. Leads (channels of open water in sea ice) have significant impacts on the large-scale budgets during the Arctic winter, when they contribute about 50 percent of the surface fluxes over the Arctic Ocean, but cover only 1 to 2 percent of its area. Convective plumes generated by wide leads may penetrate the surface inversion and produce condensate that spreads up to 250 km downwind of the lead, and may significantly affect the longwave radiative fluxes at the surface and thereby the sea ice thickness. The effects of leads and boundary layer clouds must be accurately represented in climate models to allow possible feedbacks between them and the sea ice thickness. The FIRE III Arctic boundary layer clouds field program, in conjunction with the SHEBA ice camp and the ARM North Slope of Alaska and Adjacent Arctic Ocean site, will offer an unprecedented opportunity to greatly improve our ability to parameterize the important effects of leads and boundary layer clouds in GCMs.

3D Cloud Radiative Effects on Polarized Reflectances

Science.gov (United States)

Cornet, C.; Matar, C.; C-Labonnote, L.; Szczap, F.; Waquet, F.; Parol, F.; Riedi, J.

2017-12-01

As recognized in the last IPCC report, clouds have a major importance in the climate budget and need to be better characterized. Remote sensing observations are a way to obtain either global observations of cloud from satellites or a very fine description of clouds from airborne measurements. An increasing numbers of radiometers plan to measure polarized reflectances in addition to total reflectances, since this information is very helpful to obtain aerosol or cloud properties. In a near future, for example, the Multi-viewing, Multi-channel, Multi-polarization Imager (3MI) will be part the EPS-SG Eumetsat-ESA mission. It will achieve multi-angular polarimetric measurements from visible to shortwave infrared wavelengths. An airborne prototype, OSIRIS (Observing System Including Polarization in the Solar Infrared Spectrum), is also presently developed at the Laboratoire d'Optique Atmospherique and had already participated to several measurements campaigns. In order to analyze suitably the measured signal, it it necessary to have realistic and accurate models able to simulate polarized reflectances. The 3DCLOUD model (Szczap et al., 2014) was used to generate three-dimensional synthetic cloud and the 3D radiative transfer model, 3DMCPOL (Cornet et al., 2010) to compute realistic polarized reflectances. From these simulations, we investigate the effects of 3D cloud structures and heterogeneity on the polarized angular signature often used to retrieve cloud or aerosol properties. We show that 3D effects are weak for flat clouds but become quite significant for fractional clouds above ocean. The 3D effects are quite different according to the observation scale. For the airborne scale (few tens of meter), solar illumination effects can lead to polarized cloud reflectance values higher than the saturation limit predicted by the homogeneous cloud assumption. In the cloud gaps, corresponding to shadowed areas of the total reflectances, polarized signal can also be enhanced

Challenges for Cloud Modeling in the Context of Aerosol–Cloud–Precipitation Interactions

Energy Technology Data Exchange (ETDEWEB)

Lebo, Zachary J. [Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming; Shipway, Ben J. [Met Office, Exeter, United Kingdom; Fan, Jiwen [Pacific Northwest National Laboratory, Richland, Washington; Geresdi, Istvan [Faculty of Science, University of Pécs, Pécs, Hungary; Hill, Adrian [Met Office, Exeter, United Kingdom; Miltenberger, Annette [School of Earth and Environment, University of Leeds, Leeds, United Kingdom; Morrison, Hugh [Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, Colorado; Rosenberg, Phil [School of Earth and Environment, University of Leeds, Leeds, United Kingdom; Varble, Adam [Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah; Xue, Lulin [Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

2017-08-01

The International Cloud Modeling Workshop (CMW) has been a longstanding tradition in the cloud microphysics modeling community and is typically held the week prior to the International Conference on Clouds and Precipitation (ICCP). For the Ninth CMW, more than 40 participants from 10 countries convened at the Met Office in Exeter, United Kingdom. The workshop included 4 detailed case studies (described in more detail below) rooted in recent field campaigns. The overarching objective of these cases was to utilize new observations to better understand inter-model differences and model deficiencies, explore new modeling techniques, and gain physical insight into the behavior of clouds. As was the case at the Eighth CMW, there was a general theme of understanding the role of aerosol impacts in the context of cloud-precipitation interactions. However, an additional objective was the focal point of several cases at the most recent workshop: microphysical-dynamical interactions. Many of the cases focused less on idealized small-domain simulations (as was the general focus of previous workshops) and more on large-scale nested configurations examining effects at various scales.

Modeled Impact of Cirrus Cloud Increases Along Aircraft Flight Paths

Science.gov (United States)

Rind, David; Lonergan, P.; Shah, K.

1999-01-01

The potential impact of contrails and alterations in the lifetime of background cirrus due to subsonic airplane water and aerosol emissions has been investigated in a set of experiments using the GISS GCM connected to a q-flux ocean. Cirrus clouds at a height of 12-15km, with an optical thickness of 0.33, were input to the model "x" percentage of clear-sky occasions along subsonic aircraft flight paths, where x is varied from .05% to 6%. Two types of experiments were performed: one with the percentage cirrus cloud increase independent of flight density, as long as a certain minimum density was exceeded; the other with the percentage related to the density of fuel expenditure. The overall climate impact was similar with the two approaches, due to the feedbacks of the climate system. Fifty years were run for eight such experiments, with the following conclusions based on the stable results from years 30-50 for each. The experiments show that adding cirrus to the upper troposphere results in a stabilization of the atmosphere, which leads to some decrease in cloud cover at levels below the insertion altitude. Considering then the total effect on upper level cloud cover (above 5 km altitude), the equilibrium global mean temperature response shows that altering high level clouds by 1% changes the global mean temperature by 0.43C. The response is highly linear (linear correlation coefficient of 0.996) for high cloud cover changes between 0. 1% and 5%. The effect is amplified in the Northern Hemisphere, more so with greater cloud cover change. The temperature effect maximizes around 10 km (at greater than 40C warming with a 4.8% increase in upper level clouds), again more so with greater warming. The high cloud cover change shows the flight path influence most clearly with the smallest warming magnitudes; with greater warming, the model feedbacks introduce a strong tropical response. Similarly, the surface temperature response is dominated by the feedbacks, and shows

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

Directory of Open Access Journals (Sweden)

Alireza Taravat

2015-02-01

Full Text Available A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

An assessment of aerosol optical properties from remote-sensing observations and regional chemistry-climate coupled models over Europe

Science.gov (United States)

Palacios-Peña, Laura; Baró, Rocío; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; María López-Romero, José; Montávez, Juan Pedro; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro

2018-04-01

Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry-climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol-radiation (ARI) or/and aerosol-cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality-climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in

Modeling the Cloud to Enhance Capabilities for Crises and Catastrophe Management

Science.gov (United States)

2016-11-16

through support by a prior DOD grant, and in this project, we focused on how to effectively adapt this for the cloud catastrophe environment. The...the effects of varying cloud resources and the cloud architecture on L, o, and g values, we will be able to formulate realistic analytical models of...variation in computing and communication costs of test problems due to varying loads in the cloud environment. We used the parallel matrix multiplication

Testing remote sensing on artificial observations: impact of drizzle and 3-D cloud structure on effective radius retrievals

Directory of Open Access Journals (Sweden)

T. Zinner

2010-10-01

Full Text Available Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation.

For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics.

We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1 a typical daytime stratocumulus deck at two times in the diurnal cycle and (2 one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

Science.gov (United States)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature

Science.gov (United States)

Holmes, Thomas R. H.; Hain, Christopher R.; Anderson, Martha C.; Crow, Wade T.

2016-01-01

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared(TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave(MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

A feasibility study of using remotely sensed data for water resource models

Science.gov (United States)

Ruff, J. F.

1973-01-01

Remotely sensed data were collected to demonstrate the feasibility of applying the results to water resource problems. Photographs of the Wolf Creek watershed in southwestern Colorado were collected over a one year period. Cloud top temperatures were measured using a radiometer. Thermal imagery of the Wolf Creek Pass area was obtained during one pre-dawn flight. Remote sensing studies of water resource problems for user agencies were also conducted. The results indicated that: (1) remote sensing techniques could be used to assist in the solution of water resource problems; (2) photogrammetric determination of snow depths is feasible; (3) changes in turbidity or suspended material concentration can be observed; and (4) surface turbulence can be related to bed scour; and (5) thermal effluents into rivers can be monitored.

Cloud model construct for transaction-based cooperative systems ...

African Journals Online (AJOL)

Cloud model construct for transaction-based cooperative systems. ... procure cutting edge Information Technology infrastructure are some of the problems faced ... Results also reveal that credit cooperatives will benefit from the model by taking ...

Ascension Island: The Layered Atlantic Smoke Interactions with Clouds (LASIC) Campaign

Energy Technology Data Exchange (ETDEWEB)

Nitschke, Kim [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-30

The ARM Climate Research Facility, a DOE scientific user facility, provides the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.

The Radiative Properties of Small Clouds: Multi-Scale Observations and Modeling

Energy Technology Data Exchange (ETDEWEB)

Feingold, Graham [NOAA ESRL; McComiskey, Allison [CIRES, University of Colorado

2013-09-25

Warm, liquid clouds and their representation in climate models continue to represent one of the most significant unknowns in climate sensitivity and climate change. Our project combines ARM observations, LES modeling, and satellite imagery to characterize shallow clouds and the role of aerosol in modifying their radiative effects.

Intercomparison between CMIP5 model and MODIS satellite-retrieved data of aerosol optical depth, cloud fraction, and cloud-aerosol interactions

Science.gov (United States)

Sockol, Alyssa; Small Griswold, Jennifer D.

2017-08-01

Aerosols are a critical component of the Earth's atmosphere and can affect the climate of the Earth through their interactions with solar radiation and clouds. Cloud fraction (CF) and aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used with analogous cloud and aerosol properties from Historical Phase 5 of the Coupled Model Intercomparison Project (CMIP5) model runs that explicitly include anthropogenic aerosols and parameterized cloud-aerosol interactions. The models underestimate AOD by approximately 15% and underestimate CF by approximately 10% overall on a global scale. A regional analysis is then used to evaluate model performance in two regions with known biomass burning activity and absorbing aerosol (South America (SAM) and South Africa (SAF)). In SAM, the models overestimate AOD by 4.8% and underestimate CF by 14%. In SAF, the models underestimate AOD by 35% and overestimate CF by 13.4%. Average annual cycles show that the monthly timing of AOD peaks closely match satellite data in both SAM and SAF for all except the Community Atmosphere Model 5 and Geophysical Fluid Dynamics Laboratory (GFDL) models. Monthly timing of CF peaks closely match for all models (except GFDL) for SAM and SAF. Sorting monthly averaged 2° × 2.5° model or MODIS CF as a function of AOD does not result in the previously observed "boomerang"-shaped CF versus AOD relationship characteristic of regions with absorbing aerosols from biomass burning. Cloud-aerosol interactions, as observed using daily (or higher) temporal resolution data, are not reproducible at the spatial or temporal resolution provided by the CMIP5 models.

Cloud Computing Adoption Model for Universities to Increase ICT Proficiency

Directory of Open Access Journals (Sweden)

Safiya Okai

2014-08-01

Full Text Available Universities around the world especially those in developing countries are faced with the problem of delivering the level of information and communications technology (ICT needed to facilitate teaching, learning, research, and development activities ideal in a typical university, which is needed to meet educational needs in-line with advancement in technology and the growing dependence on IT. This is mainly due to the high cost involved in providing and maintaining the needed hardware and software. A technology such as cloud computing that delivers on demand provisioning of IT resources on a pay per use basis can be used to address this problem. Cloud computing promises better delivery of IT services as well as availability whenever and wherever needed at reduced costs with users paying only as much as they consume through the services of cloud service providers. The cloud technology reduces complexity while increasing speed and quality of IT services provided; however, despite these benefits the challenges that come with its adoption have left many sectors especially the higher education skeptical in committing to this technology. This article identifies the reasons for the slow rate of adoption of cloud computing at university level, discusses the challenges faced and proposes a cloud computing adoption model that contains strategic guidelines to overcome the major challenges identified and a roadmap for the successful adoption of cloud computing by universities. The model was tested in one of the universities and found to be both useful and appropriate for adopting cloud computing at university level.

Integrated Model to Assess Cloud Deployment Effectiveness When Developing an IT-strategy

Science.gov (United States)

Razumnikov, S.; Prankevich, D.

2016-04-01

Developing an IT-strategy of cloud deployment is a complex issue since even the stage of its formation necessitates revealing what applications will be the best possible to meet the requirements of a company business-strategy, evaluate reliability and safety of cloud providers and analyze staff satisfaction. A system of criteria, as well an integrated model to assess cloud deployment effectiveness is offered. The model makes it possible to identify what applications being at the disposal of a company, as well as new tools to be deployed are reliable and safe enough for implementation in the cloud environment. The data on practical use of the procedure to assess cloud deployment effectiveness by a provider of telecommunication services is presented. The model was used to calculate values of integral indexes of services to be assessed, then, ones, meeting the criteria and answering the business-strategy of a company, were selected.

Cloud Processing of Gases and Aerosols in Air Quality Modeling

Directory of Open Access Journals (Sweden)

Leiming Zhang

2011-10-01

Full Text Available The representations of cloud processing of gases and aerosols in some of the current state-of-the-art regional air quality models in North America and Europe are reviewed. Key processes reviewed include aerosol activation (or nucleation scavenging of aerosols, aqueous-phase chemistry, and wet deposition/removal of atmospheric tracers. It was found that models vary considerably in the parameterizations or algorithms used in representing these processes. As an emerging area of research, the current understanding of the uptake of water soluble organics by cloud droplets and the potential aqueous-phase reaction pathways leading to the atmospheric secondary organic aerosol (SOA formation is also reviewed. Sensitivity tests using the AURAMS model have been conducted in order to assess the impact on modeled regional particulate matter (PM from: (1 the different aerosol activation schemes, (2 the different below-cloud particle scavenging algorithms, and (3 the inclusion of cloud processing of water soluble organics as a potential pathway for the formation of atmospheric SOA. It was found that the modeled droplet number concentrations and ambient PM size distributions were strongly affected by the use of different aerosol activation schemes. The impact on the modeled average ambient PM mass concentration was found to be limited in terms of averaged PM2.5 concentration (~a few percents but more significant in terms of PM1.0 (up to 10 percents. The modeled ambient PM was found to be moderately sensitive to the below-cloud particle scavenging algorithms, with relative differences up to 10% and 20% in terms of PM2.5 and PM10, respectively, when using the two different algorithms for the scavenging coefficient (Λ corresponding to the lower and upper bounds in the parameterization for Λ. The model simulation with the additional cloud uptake and processing of water-soluble organic gases was shown to improve the evaluation statistics for modeled PM2.5 OA

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

OpenAIRE

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 (TM...

Information Recovery Algorithm for Ground Objects in Thin Cloud Images by Fusing Guide Filter and Transfer Learning

Directory of Open Access Journals (Sweden)

HU Gensheng

2018-03-01

Full Text Available Ground object information of remote sensing images covered with thin clouds is obscure. An information recovery algorithm for ground objects in thin cloud images is proposed by fusing guide filter and transfer learning. Firstly, multi-resolution decomposition of thin cloud target images and cloud-free guidance images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform. Then the decomposed low frequency subbands are processed by using support vector guided filter and transfer learning respectively. The decomposed high frequency subbands are enhanced by using modified Laine enhancement function. The low frequency subbands output by guided filter and those predicted by transfer learning model are fused by the method of selection and weighting based on regional energy. Finally, the enhanced high frequency subbands and the fused low frequency subbands are reconstructed by using inverse multi-directional nonsubsampled dual-tree complex wavelet transform to obtain the ground object information recovery images. Experimental results of Landsat-8 OLI multispectral images show that, support vector guided filter can effectively preserve the detail information of the target images, domain adaptive transfer learning can effectively extend the range of available multi-source and multi-temporal remote sensing images, and good effects for ground object information recover are obtained by fusing guide filter and transfer learning to remove thin cloud on the remote sensing images.

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

Science.gov (United States)

Bony, Sandrine; Stevens, Bjorn; Ament, Felix; Bigorre, Sebastien; Chazette, Patrick; Crewell, Susanne; Delanoë, Julien; Emanuel, Kerry; Farrell, David; Flamant, Cyrille; Gross, Silke; Hirsch, Lutz; Karstensen, Johannes; Mayer, Bernhard; Nuijens, Louise; Ruppert, James H.; Sandu, Irina; Siebesma, Pier; Speich, Sabrina; Szczap, Frédéric; Totems, Julien; Vogel, Raphaela; Wendisch, Manfred; Wirth, Martin

2017-11-01

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation

Science.gov (United States)

Bony, Sandrine; Stevens, Bjorn; Ament, Felix; Bigorre, Sebastien; Chazette, Patrick; Crewell, Susanne; Delanoë, Julien; Emanuel, Kerry; Farrell, David; Flamant, Cyrille; Gross, Silke; Hirsch, Lutz; Karstensen, Johannes; Mayer, Bernhard; Nuijens, Louise; Ruppert, James H.; Sandu, Irina; Siebesma, Pier; Speich, Sabrina; Szczap, Frédéric; Totems, Julien; Vogel, Raphaela; Wendisch, Manfred; Wirth, Martin

Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of tradecumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of tradecumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air-sea interactions and convective organization.

Raman lidar measurements of water vapor and aerosols during the atmospheric radiation measurement (ARM) remote clouds sensing (RCS) intensive observation period (IOP)

Energy Technology Data Exchange (ETDEWEB)

Melfi, S.H.; Starr, D.O`C.; Whiteman, D. [NASA Goddard Space Flight Center, Greenbelt, MD (United States)] [and others

1996-04-01

The first Atmospheric Radiation Measurement (ARM) remote Cloud Study (RCS) Intensive Operations Period (IOP) was held during April 1994 at the Southern Great Plains (SGP) site. This experiment was conducted to evaluate and calibrate state-of-the-art, ground based remote sensing instruments and to use the data acquired by these instruments to validate retrieval algorithms developed under the ARM program.

Establishing a Cloud Computing Success Model for Hospitals in Taiwan

OpenAIRE

Lian, Jiunn-Woei

2017-01-01

The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality...

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis.

Science.gov (United States)

Pan, Yuchen; Ding, Shuai; Fan, Wenjuan; Li, Jing; Yang, Shanlin

2015-01-01

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard's Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.

Numerical modeling and experimental research on the movement of the explosion clouds

International Nuclear Information System (INIS)

Li Xiaoli; Zheng Yi; Liu Wei; Wu Guansheng

2011-01-01

It presents the experimental research and numerical modeling on the movement of explosion clouds. The experiment was performed under two kinds of recorder, one is high speed CCD recorder which was mainly used to record the process of the fireball when the TNT was detonated, and the other is SONY vidicon that was mainly used to record the movement of the clouds. Based on the assumption that the effects on the clouds were gravity and buoyancy, the numerical model on the thermal was established. The initial condition of the thermal that was to say the initial cloud dimension was gained through the results of the recording of the highly CCD recorder. Followed this, the results of the numerical simulation were presented. And the computational results of the rising cloud are reasonable compared to that of the experiment. Thus, it can be seen that the numerical modeling and experimental research methods presented in this paper are reasonable and it can be serve as a reference to related person. Finally, the problems about the experiment and the model are pointed to establish a more accurate model. (authors)

3D Cloud Tomography, Followed by Mean Optical and Microphysical Properties, with Multi-Angle/Multi-Pixel Data

Science.gov (United States)

Davis, A. B.; von Allmen, P. A.; Marshak, A.; Bal, G.

2010-12-01

The geometrical assumption in all operational cloud remote sensing algorithms is that clouds are plane-parallel slabs, which applies relatively well to the most uniform stratus layers. Its benefit is to justify using classic 1D radiative transfer (RT) theory, where angular details (solar, viewing, azimuthal) are fully accounted for and precise phase functions can be used, to generate the look-up tables used in the retrievals. Unsurprisingly, these algorithms catastrophically fail when applied to cumulus-type clouds, which are highly 3D. This is unfortunate for the cloud-process modeling community that may thrive on in situ airborne data, but would very much like to use satellite data for more than illustrations in their presentations and publications. So, how can we obtain quantitative information from space-based observations of finite aspect ratio clouds? Cloud base/top heights, vertically projected area, mean liquid water content (LWC), and volume-averaged droplet size would be a good start. Motivated by this science need, we present a new approach suitable for sparse cumulus fields where we turn the tables on the standard procedure in cloud remote sensing. We make no a priori assumption about cloud shape, save an approximately flat base, but use brutal approximations about the RT that is necessarily 3D. Indeed, the first order of business is to roughly determine the cloud's outer shape in one of two ways, which we will frame as competing initial guesses for the next phase of shape refinement and volume-averaged microphysical parameter estimation. Both steps use multi-pixel/multi-angle techniques amenable to MISR data, the latter adding a bi-spectral dimension using collocated MODIS data. One approach to rough cloud shape determination is to fit the multi-pixel/multi-angle data with a geometric primitive such as a scalene hemi-ellipsoid with 7 parameters (translation in 3D space, 3 semi-axes, 1 azimuthal orientation); for the radiometry, a simple radiosity

Final Report for 'Modeling Electron Cloud Diagnostics for High-Intensity Proton Accelerators'

International Nuclear Information System (INIS)

Veitzer, Seth A.

2009-01-01

Electron clouds in accelerators such as the ILC degrade beam quality and limit operating efficiency. The need to mitigate electron clouds has a direct impact on the design and operation of these accelerators, translating into increased cost and reduced performance. Diagnostic techniques for measuring electron clouds in accelerating cavities are needed to provide an assessment of electron cloud evolution and mitigation. Accurate numerical modeling of these diagnostics is needed to validate the experimental techniques. In this Phase I, we developed detailed numerical models of microwave propagation through electron clouds in accelerating cavities with geometries relevant to existing and future high-intensity proton accelerators such as Project X and the ILC. Our numerical techniques and simulation results from the Phase I showed that there was a high probability of success in measuring both the evolution of electron clouds and the effects of non-uniform electron density distributions in Phase II.

Development of a Cloud Resolving Model for Heterogeneous Supercomputers

Science.gov (United States)

Sreepathi, S.; Norman, M. R.; Pal, A.; Hannah, W.; Ponder, C.

2017-12-01

A cloud resolving climate model is needed to reduce major systematic errors in climate simulations due to structural uncertainty in numerical treatments of convection - such as convective storm systems. This research describes the porting effort to enable SAM (System for Atmosphere Modeling) cloud resolving model on heterogeneous supercomputers using GPUs (Graphical Processing Units). We have isolated a standalone configuration of SAM that is targeted to be integrated into the DOE ACME (Accelerated Climate Modeling for Energy) Earth System model. We have identified key computational kernels from the model and offloaded them to a GPU using the OpenACC programming model. Furthermore, we are investigating various optimization strategies intended to enhance GPU utilization including loop fusion/fission, coalesced data access and loop refactoring to a higher abstraction level. We will present early performance results, lessons learned as well as optimization strategies. The computational platform used in this study is the Summitdev system, an early testbed that is one generation removed from Summit, the next leadership class supercomputer at Oak Ridge National Laboratory. The system contains 54 nodes wherein each node has 2 IBM POWER8 CPUs and 4 NVIDIA Tesla P100 GPUs. This work is part of a larger project, ACME-MMF component of the U.S. Department of Energy(DOE) Exascale Computing Project. The ACME-MMF approach addresses structural uncertainty in cloud processes by replacing traditional parameterizations with cloud resolving "superparameterization" within each grid cell of global climate model. Super-parameterization dramatically increases arithmetic intensity, making the MMF approach an ideal strategy to achieve good performance on emerging exascale computing architectures. The goal of the project is to integrate superparameterization into ACME, and explore its full potential to scientifically and computationally advance climate simulation and prediction.

Model-as-a-service (MaaS) using the cloud service innovation platform (CSIP)

Science.gov (United States)

Cloud infrastructures for modelling activities such as data processing, performing environmental simulations, or conducting model calibrations/optimizations provide a cost effective alternative to traditional high performance computing approaches. Cloud-based modelling examples emerged into the more...

Prototype methodology for obtaining cloud seeding guidance from HRRR model data

Science.gov (United States)

Dawson, N.; Blestrud, D.; Kunkel, M. L.; Waller, B.; Ceratto, J.

2017-12-01

Weather model data, along with real time observations, are critical to determine whether atmospheric conditions are prime for super-cooled liquid water during cloud seeding operations. Cloud seeding groups can either use operational forecast models, or run their own model on a computer cluster. A custom weather model provides the most flexibility, but is also expensive. For programs with smaller budgets, openly-available operational forecasting models are the de facto method for obtaining forecast data. The new High-Resolution Rapid Refresh (HRRR) model (3 x 3 km grid size), developed by the Earth System Research Laboratory (ESRL), provides hourly model runs with 18 forecast hours per run. While the model cannot be fine-tuned for a specific area or edited to provide cloud-seeding-specific output, model output is openly available on a near-real-time basis. This presentation focuses on a prototype methodology for using HRRR model data to create maps which aid in near-real-time cloud seeding decision making. The R programming language is utilized to run a script on a Windows® desktop/laptop computer either on a schedule (such as every half hour) or manually. The latest HRRR model run is downloaded from NOAA's Operational Model Archive and Distribution System (NOMADS). A GRIB-filter service, provided by NOMADS, is used to obtain surface and mandatory pressure level data for a subset domain which greatly cuts down on the amount of data transfer. Then, a set of criteria, identified by the Idaho Power Atmospheric Science Group, is used to create guidance maps. These criteria include atmospheric stability (lapse rates), dew point depression, air temperature, and wet bulb temperature. The maps highlight potential areas where super-cooled liquid water may exist, reasons as to why cloud seeding should not be attempted, and wind speed at flight level.

Evaluation of a stratiform cloud parameterization for general circulation models

Energy Technology Data Exchange (ETDEWEB)

Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States); McCaa, J. [Univ. of Washington, Seattle, WA (United States)

1996-04-01

To evaluate the relative importance of horizontal advection of cloud versus cloud formation within the grid cell of a single column model (SCM), we have performed a series of simulations with our SCM driven by a fixed vertical velocity and various rates of horizontal advection.

Do clouds save the great barrier reef? satellite imagery elucidates the cloud-SST relationship at the local scale.

Directory of Open Access Journals (Sweden)

Susannah M Leahy

Full Text Available Evidence of global climate change and rising sea surface temperatures (SSTs is now well documented in the scientific literature. With corals already living close to their thermal maxima, increases in SSTs are of great concern for the survival of coral reefs. Cloud feedback processes may have the potential to constrain SSTs, serving to enforce an "ocean thermostat" and promoting the survival of coral reefs. In this study, it was hypothesized that cloud cover can affect summer SSTs in the tropics. Detailed direct and lagged relationships between cloud cover and SST across the central Great Barrier Reef (GBR shelf were investigated using data from satellite imagery and in situ temperature and light loggers during two relatively hot summers (2005 and 2006 and two relatively cool summers (2007 and 2008. Across all study summers and shelf positions, SSTs exhibited distinct drops during periods of high cloud cover, and conversely, SST increases during periods of low cloud cover, with a three-day temporal lag between a change in cloud cover and a subsequent change in SST. Cloud cover alone was responsible for up to 32.1% of the variation in SSTs three days later. The relationship was strongest in both El Niño (2005 and La Niña (2008 study summers and at the inner-shelf position in those summers. SST effects on subsequent cloud cover were weaker and more variable among study summers, with rising SSTs explaining up to 21.6% of the increase in cloud cover three days later. This work quantifies the often observed cloud cooling effect on coral reefs. It highlights the importance of incorporating local-scale processes into bleaching forecasting models, and encourages the use of remote sensing imagery to value-add to coral bleaching field studies and to more accurately predict risks to coral reefs.

Modeling study of cloud droplet nucleation and in-cloud sulfate production during the Sanitation of the Atmosphere (SANA) 2 campaign

Science.gov (United States)

Liu, Xiaohong; Seidl, Winfried

1998-01-01

Based upon the measurements of vertical profiles of gaseous SO2, H2O2, O3, and meteorological parameters from aircraft and of the aerosol chemical composition and gaseous NH3, HNO3, and SO2 at the surface in southeastern Germany (Melpitz) during the Sanitation of the Atmosphere (SANA) 2 campaign, realistic modeling of cloud droplet nucleation and in-cloud sulfate production was performed with an explicit microphysical cloud model with size-resolved chemistry and cloud top entrainment. For the fair weather cumulus observed during the measurements, the calculated cloud droplet number concentrations could be as high as 2000 cm-3 (and precloud aerosol sulfate up to 9.1 μg m-3), indicating strong sulfur pollution at Melpitz during the campaign. The in-cloud sulfate production is within 1.5-5.0 μg m-3, depending on the initial gaseous NH3 concentration in the parcel. This result shows the necessity of gaseous NH3 vertical profile measurements. Entrainment can reduce the cloud droplet number concentration and cause the distribution of in-cloud produced sulfate to shift toward larger particle sizes. Under the cases we studied, we do not find a significant effect of cloud top gaseous H2O2 entrainment on the in-cloud sulfate production. For the adiabatic cases the departure of bulk water H2O2 from the Henry's law equilibrium is very small. When entrainment included, however, bulk water H2O2 concentrations could be clearly less than the equilibrium values, and the deficiencies are higher (>20%) for droplets larger than 10 μm radius. Our results suggest that entrainment could be one of the important factors to account for the measured H2O2 deficiency in cloud water.

Analytic Closed-Form Solution of a Mixed Layer Model for Stratocumulus Clouds

Science.gov (United States)

Akyurek, Bengu Ozge

Stratocumulus clouds play an important role in climate cooling and are hard to predict using global climate and weather forecast models. Thus, previous studies in the literature use observations and numerical simulation tools, such as large-eddy simulation (LES), to solve the governing equations for the evolution of stratocumulus clouds. In contrast to the previous works, this work provides an analytic closed-form solution to the cloud thickness evolution of stratocumulus clouds in a mixed-layer model framework. With a focus on application over coastal lands, the diurnal cycle of cloud thickness and whether or not clouds dissipate are of particular interest. An analytic solution enables the sensitivity analysis of implicitly interdependent variables and extrema analysis of cloud variables that are hard to achieve using numerical solutions. In this work, the sensitivity of inversion height, cloud-base height, and cloud thickness with respect to initial and boundary conditions, such as Bowen ratio, subsidence, surface temperature, and initial inversion height, are studied. A critical initial cloud thickness value that can be dissipated pre- and post-sunrise is provided. Furthermore, an extrema analysis is provided to obtain the minima and maxima of the inversion height and cloud thickness within 24 h. The proposed solution is validated against LES results under the same initial and boundary conditions. Then, the proposed analytic framework is extended to incorporate multiple vertical columns that are coupled by advection through wind flow. This enables a bridge between the micro-scale and the mesoscale relations. The effect of advection on cloud evolution is studied and a sensitivity analysis is provided.

PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS

Directory of Open Access Journals (Sweden)

V. Petras

2016-06-01

Full Text Available 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.

Midlatitude Continental Convective Clouds Experiment (MC3E)

Energy Technology Data Exchange (ETDEWEB)

Jensen, MP; Petersen, WA; Del Genio, AD; Giangrande, SE; Heymsfield, A; Heymsfield, G; Hou, AY; Kollias, P; Orr, B; Rutledge, SA; Schwaller, MR; Zipser, E

2010-04-01

Convective processes play a critical role in the Earth’s energy balance through the redistribution of heat and moisture in the atmosphere and subsequent impacts on the hydrologic cycle. Global observation and accurate representation of these processes in numerical models is vital to improving our current understanding and future simulations of Earth’s climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales that are associated with convective and stratiform precipitation processes; therefore, they must turn to parameterization schemes to represent these processes. In turn, the physical basis for these parameterization schemes needs to be evaluated for general application under a variety of atmospheric conditions. Analogously, space-based remote sensing algorithms designed to retrieve related cloud and precipitation information for use in hydrological, climate, and numerical weather prediction applications often rely on physical “parameterizations” that reliably translate indirectly related instrument measurements to the physical quantity of interest (e.g., precipitation rate). Importantly, both spaceborne retrieval algorithms and model convective parameterization schemes traditionally rely on field campaign data sets as a basis for evaluating and improving the physics of their respective approaches. The Midlatitude Continental Convective Clouds Experiment (MC3E) will take place in central Oklahoma during the April–May 2011 period. The experiment is a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration’s (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leverages the unprecedented observing infrastructure currently available in the central United States

Hydrologic scales, cloud variability, remote sensing, and models: Implications for forecasting snowmelt and streamflow

Science.gov (United States)

Simpson, James J.; Dettinger, M.D.; Gehrke, F.; McIntire, T.J.; Hufford, Gary L.

2004-01-01

Accurate prediction of available water supply from snowmelt is needed if the myriad of human, environmental, agricultural, and industrial demands for water are to be satisfied, especially given legislatively imposed conditions on its allocation. Robust retrievals of hydrologic basin model variables (e.g., insolation or areal extent of snow cover) provide several advantages over the current operational use of either point measurements or parameterizations to help to meet this requirement. Insolation can be provided at hourly time scales (or better if needed during rapid melt events associated with flooding) and at 1-km spatial resolution. These satellite-based retrievals incorporate the effects of highly variable (both in space and time) and unpredictable cloud cover on estimates of insolation. The insolation estimates are further adjusted for the effects of basin topography using a high-resolution digital elevation model prior to model input. Simulations of two Sierra Nevada rivers in the snowmelt seasons of 1998 and 1999 indicate that even the simplest improvements in modeled insolation can improve snowmelt simulations, with 10%-20% reductions in root-mean-square errors. Direct retrieval of the areal extent of snow cover may mitigate the need to rely entirely on internal calculations of this variable, a reliance that can yield large errors that are difficult to correct until long after the season is complete and that often leads to persistent underestimates or overestimates of the volumes of the water to operational reservoirs. Agencies responsible for accurately predicting available water resources from the melt of snowpack [e.g., both federal (the National Weather Service River Forecast Centers) and state (the California Department of Water Resources)] can benefit by incorporating concepts developed herein into their operational forecasting procedures. ?? 2004 American Meteorological Society.

A QoS aware services mashup model for cloud computing applications

Directory of Open Access Journals (Sweden)

Yee Ming Chen

2012-12-01

Full Text Available Purpose: With the popularity of cloud computing, cloud services have become to be application programming platform where users can create new applications mashup(composing the functionality offered byothers.By composing of distributed, cloud services dynamicallyto provide more complex tasks, services mashup provides an attractive way for building large-scale Internetapplications.Oneof the challenging issues of cloud services mashup is how to find service paths to route the service instances provider through whilemeeting the applications’ resource requirements so that the QoS constraints are satisfied. However, QoS aware service routing problem istypically NP-hard.The purpose of this paper is to propose a QoS Aware Services Mashup(QASM model to solve this problem more effectively.Design/methodology/approach: In this paper, we focus on the QoS aware services selection problem in cloud services mashup, for example, given the user service composition requirements and their QoS constraint descriptions, how to select the required serviceinstances and route the data flows through these instances so that the QoS requirements are satisfied. We design a heuristic algorithm to find service paths to route the data flows through whilemeeting the applications’ resource requirements and specific QoS constraints.Findings: This study propose a QoS Aware Services Mashup(QASM model to solve this problem more effectively. Simulations show that QASM can achieve desired QoS assurances as well as load balancing in cloud services environment.Originality/value: This paperpresent a QASM model for providing high performance distributedapplications in the cloud computing systems.

Factors influencing the parameterization of anvil clouds within general circulation models

International Nuclear Information System (INIS)

Leone, J.M. Jr.; Chin, H.N.

1994-01-01

The overall goal of this project is to improve the representation of clouds and their effects within global climate models (GCMs). We have concentrated on a small portion of the overall goal, the evolution of convectively generated cirrus clouds and their effects on the large-scale environment. Because of the large range of time and length scales involved, we have been using a multi-scale attack. For the early time generation and development of the cirrus anvil, we are using a cloud-scale model with horizontal resolution of 1 to 2 kilometers; for the larger scale transport by the larger scale flow, we are using a mesoscale model with a horizontal resolution of 20 to 60 kilometers. The eventual goal is to use the information obtained from these simulations, together with available observations, to derive improved cloud parameterizations for use in GCMs. This paper presents a new tool, a cirrus generator, that we have developed to aid in our mesoscale studies

Impact of entrainment on cloud droplet spectra: theory, observations, and modeling

Science.gov (United States)

Grabowski, W.

2016-12-01

Understanding the impact of entrainment and mixing on microphysical properties of warm boundary layer clouds is an important aspect of the representation of such clouds in large-scale models of weather and climate. Entrainment leads to a reduction of the liquid water content in agreement with the fundamental thermodynamics, but its impact on the droplet spectrum is difficult to quantify in observations and modeling. For in-situ (e.g., aircraft) observations, it is impossible to follow air parcels and observe processes that lead to changes of the droplet spectrum in different regions of a cloud. For similar reasons traditional modeling methodologies (e.g., the Eulerian large eddy simulation) are not useful either. Moreover, both observations and modeling can resolve only relatively narrow range of spatial scales. Theory, typically focusing on differences between idealized concepts of homogeneous and inhomogeneous mixing, is also of a limited use for the multiscale turbulent mixing between a cloud and its environment. This presentation will illustrate the above points and argue that the Lagrangian large-eddy simulation with appropriate subgrid-scale scheme may provide key insights and eventually lead to novel parameterizations for large-scale models.

A PROFICIENT MODEL FOR HIGH END SECURITY IN CLOUD COMPUTING

Directory of Open Access Journals (Sweden)

R. Bala Chandar

2014-01-01

Full Text Available Cloud computing is an inspiring technology due to its abilities like ensuring scalable services, reducing the anxiety of local hardware and software management associated with computing while increasing flexibility and scalability. A key trait of the cloud services is remotely processing of data. Even though this technology had offered a lot of services, there are a few concerns such as misbehavior of server side stored data , out of control of data owner's data and cloud computing does not control the access of outsourced data desired by the data owner. To handle these issues, we propose a new model to ensure the data correctness for assurance of stored data, distributed accountability for authentication and efficient access control of outsourced data for authorization. This model strengthens the correctness of data and helps to achieve the cloud data integrity, supports data owner to have control on their own data through tracking and improves the access control of outsourced data.

Remote sensing of contrails and aircraft altered cirrus clouds

Energy Technology Data Exchange (ETDEWEB)

Palikonda, R.; Nguyen, L.; Garber, D.P.; Smith, W.L. Jr [Analytical Services and Materials, Inc., Hampton, VA (United States); Minnis, P.; Young, D.F. [National Aeronautics and Space Administration, Hampton, VA (United States). Langley Research Center

1997-12-31

Analyses of satellite imagery are used to show that contrails can develop into fully extended cirrus cloud systems. Contrails can be advective on great distances, but would appear to observers as natural cirrus clouds. The conversion of simple contrails into cirrus may help explain the apparent increase of cloudiness over populated areas since the beginning of commercial jet air travel. Statistics describing the typical growth, advection, and lifetime of contrail cirrus is needed to evaluate their effects on climate. (author) 4 refs.

Remote sensing of contrails and aircraft altered cirrus clouds

Energy Technology Data Exchange (ETDEWEB)

Palikonda, R; Nguyen, L; Garber, D P; Smith, Jr, W L [Analytical Services and Materials, Inc., Hampton, VA (United States); Minnis, P; Young, D F [National Aeronautics and Space Administration, Hampton, VA (United States). Langley Research Center

1998-12-31

Analyses of satellite imagery are used to show that contrails can develop into fully extended cirrus cloud systems. Contrails can be advective on great distances, but would appear to observers as natural cirrus clouds. The conversion of simple contrails into cirrus may help explain the apparent increase of cloudiness over populated areas since the beginning of commercial jet air travel. Statistics describing the typical growth, advection, and lifetime of contrail cirrus is needed to evaluate their effects on climate. (author) 4 refs.

The Kimball Free-Cloud Model: A Failed Innovation in Chemical Education?

Science.gov (United States)

Jensen, William B.

2014-01-01

This historical review traces the origins of the Kimball free-cloud model of the chemical bond, otherwise known as the charge-cloud or tangent-sphere model, and the central role it played in attempts to reform the introductory chemical curriculum at both the high school and college levels in the 1960s. It also critically evaluates the limitations…

EDITORIAL: Aerosol cloud interactions—a challenge for measurements and modeling at the cutting edge of cloud climate interactions

Science.gov (United States)

Spichtinger, Peter; Cziczo, Daniel J.

2008-04-01

Research in aerosol properties and cloud characteristics have historically been considered two separate disciplines within the field of atmospheric science. As such, it has been uncommon for a single researcher, or even research group, to have considerable expertise in both subject areas. The recent attention paid to global climate change has shown that clouds can have a considerable effect on the Earth's climate and that one of the most uncertain aspects in their formation, persistence, and ultimate dissipation is the role played by aerosols. This highlights the need for researchers in both disciplines to interact more closely than they have in the past. This is the vision behind this focus issue of Environmental Research Letters. Certain interactions between aerosols and clouds are relatively well studied and understood. For example, it is known that an increase in the aerosol concentration will increase the number of droplets in warm clouds, decrease their average size, reduce the rate of precipitation, and extend the lifetime. Other effects are not as well known. For example, persistent ice super-saturated conditions are observed in the upper troposphere that appear to exceed our understanding of the conditions required for cirrus cloud formation. Further, the interplay of dynamics versus effects purely attributed to aerosols remains highly uncertain. The purpose of this focus issue is to consider the current state of knowledge of aerosol/cloud interactions, to define the contemporary uncertainties, and to outline research foci as we strive to better understand the Earth's climate system. This focus issue brings together laboratory experiments, field data, and model studies. The authors address issues associated with warm liquid water, cold ice, and intermediate temperature mixed-phase clouds. The topics include the uncertainty associated with the effect of black carbon and organics, aerosol types of anthropogenic interest, on droplet and ice formation. Phases

An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment

Directory of Open Access Journals (Sweden)

Vinothkumar Muthurajan

2016-01-01

Full Text Available Cloud computing requires the security upgrade in data transmission approaches. In general, key-based encryption/decryption (symmetric and asymmetric mechanisms ensure the secure data transfer between the devices. The symmetric key mechanisms (pseudorandom function provide minimum protection level compared to asymmetric key (RSA, AES, and ECC schemes. The presence of expired content and the irrelevant resources cause unauthorized data access adversely. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve based Schnorr scheme. This paper proposes a virtual machine based cloud model with Hybrid Cloud Security Algorithm (HCSA to remove the expired content. The HCSA-based auditing improves the malicious activity prediction during the data transfer. The duplication in the cloud server degrades the performance of EC-Schnorr based encryption schemes. This paper utilizes the blooming filter concept to avoid the cloud server duplication. The combination of EC-Schnorr and blooming filter efficiently improves the security performance. The comparative analysis between proposed HCSA and the existing Distributed Hash Table (DHT regarding execution time, computational overhead, and auditing time with auditing requests and servers confirms the effectiveness of HCSA in the cloud security model creation.

An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment.

Science.gov (United States)

Muthurajan, Vinothkumar; Narayanasamy, Balaji

2016-01-01

Cloud computing requires the security upgrade in data transmission approaches. In general, key-based encryption/decryption (symmetric and asymmetric) mechanisms ensure the secure data transfer between the devices. The symmetric key mechanisms (pseudorandom function) provide minimum protection level compared to asymmetric key (RSA, AES, and ECC) schemes. The presence of expired content and the irrelevant resources cause unauthorized data access adversely. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve based Schnorr scheme. This paper proposes a virtual machine based cloud model with Hybrid Cloud Security Algorithm (HCSA) to remove the expired content. The HCSA-based auditing improves the malicious activity prediction during the data transfer. The duplication in the cloud server degrades the performance of EC-Schnorr based encryption schemes. This paper utilizes the blooming filter concept to avoid the cloud server duplication. The combination of EC-Schnorr and blooming filter efficiently improves the security performance. The comparative analysis between proposed HCSA and the existing Distributed Hash Table (DHT) regarding execution time, computational overhead, and auditing time with auditing requests and servers confirms the effectiveness of HCSA in the cloud security model creation.

Cloud Computing Value Chains: Understanding Businesses and Value Creation in the Cloud

Science.gov (United States)

Mohammed, Ashraf Bany; Altmann, Jörn; Hwang, Junseok

Based on the promising developments in Cloud Computing technologies in recent years, commercial computing resource services (e.g. Amazon EC2) or software-as-a-service offerings (e.g. Salesforce. com) came into existence. However, the relatively weak business exploitation, participation, and adoption of other Cloud Computing services remain the main challenges. The vague value structures seem to be hindering business adoption and the creation of sustainable business models around its technology. Using an extensive analyze of existing Cloud business models, Cloud services, stakeholder relations, market configurations and value structures, this Chapter develops a reference model for value chains in the Cloud. Although this model is theoretically based on porter's value chain theory, the proposed Cloud value chain model is upgraded to fit the diversity of business service scenarios in the Cloud computing markets. Using this model, different service scenarios are explained. Our findings suggest new services, business opportunities, and policy practices for realizing more adoption and value creation paths in the Cloud.

Using long-term ARM observations to evaluate Arctic mixed-phased cloud representation in the GISS ModelE GCM

Science.gov (United States)

Lamer, K.; Fridlind, A. M.; Luke, E. P.; Tselioudis, G.; Ackerman, A. S.; Kollias, P.; Clothiaux, E. E.

2016-12-01

The presence of supercooled liquid in clouds affects surface radiative and hydrological budgets, especially at high latitudes. Capturing these effects is crucial to properly quantifying climate sensitivity. Currently, a number of CGMs disagree on the distribution of cloud phase. Adding to the challenge is a general lack of observations on the continuum of clouds, from high to low-level and from warm to cold. In the current study, continuous observations from 2011 to 2014 are used to evaluate all clouds produced by the GISS ModelE GCM over the ARM North Slope of Alaska site. The International Satellite Cloud Climatology Project (ISCCP) Global Weather State (GWS) approach reveals that fair-weather (GWS 7, 32% occurrence rate), as well as mid-level storm related (GWS 5, 28%) and polar (GWS 4, 14%) clouds, dominate the large-scale cloud patterns at this high latitude site. At higher spatial and temporal resolutions, ground-based cloud radar observations reveal a majority of single layer cloud vertical structures (CVS). While clear sky and low-level clouds dominate (each with 30% occurrence rate) a fair amount of shallow ( 10%) to deep ( 5%) convection are observed. Cloud radar Doppler spectra are used along with depolarization lidar observations in a neural network approach to detect the presence, layering and inhomogeneity of supercooled liquid layers. Preliminary analyses indicate that most of the low-level clouds sampled contain one or more supercooled liquid layers. Furthermore, the relationship between CVS and the presence of supercooled liquid is established, as is the relationship between the presence of supercool liquid and precipitation susceptibility. Two approaches are explored to bridge the gap between large footprint GCM simulations and high-resolution ground-based observations. The first approach consists of comparing model output and ground-based observations that exhibit the same column CVS type (i.e. same cloud depth, height and layering

Sensitivity of PARASOL multi-angle photopolarimetric aerosol retrievals to cloud contamination

NARCIS (Netherlands)

Stap, F. A.; Hasekamp, O. P.; Roeckmann, Thomas

2015-01-01

An important problem in satellite remote sensing of aerosols is related to the need to perform an adequate cloud screening. If a cloud screening is applied that is not strict enough, the ground scene has the probability of residual cloud cover which causes large errors on the retrieved aerosol

Added value of far-infrared radiometry for remote sensing of ice clouds

Science.gov (United States)

Libois, Quentin; Blanchet, Jean-Pierre

2017-06-01

Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection-based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Directory of Open Access Journals (Sweden)

Y. Tulunay

2008-12-01

Full Text Available Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M. The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS, a data driven Neural Network module (METU-FNN of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC and cloud top temperatures (CTT are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

Cloud data centers and cost modeling a complete guide to planning, designing and building a cloud data center

CERN Document Server

Wu, Caesar

2015-01-01

Cloud Data Centers and Cost Modeling establishes a framework for strategic decision-makers to facilitate the development of cloud data centers. Just as building a house requires a clear understanding of the blueprints, architecture, and costs of the project; building a cloud-based data center requires similar knowledge. The authors take a theoretical and practical approach, starting with the key questions to help uncover needs and clarify project scope. They then demonstrate probability tools to test and support decisions, and provide processes that resolve key issues. After laying a foundati

Model Infrastruktur dan Manajemen Platform Server Berbasis Cloud Computing

Directory of Open Access Journals (Sweden)

Mulki Indana Zulfa

2017-11-01

Full Text Available Cloud computing is a new technology that is still very rapidly growing. This technology makes the Internet as the main media for the management of data and applications remotely. Cloud computing allows users to run an application without having to think about infrastructure and its platforms. Other technical aspects such as memory, storage, backup and restore, can be done very easily. This research is intended to modeling the infrastructure and management of computer platform in computer network of Faculty of Engineering, University of Jenderal Soedirman. The first stage in this research is literature study, by finding out the implementation model in previous research. Then the result will be combined with a new approach to existing resources and try to implement directly on the existing server network. The results showed that the implementation of cloud computing technology is able to replace the existing platform network.

Cloud condensation nuclei in Western Colorado: Observations and model predictions

Science.gov (United States)

Ward, Daniel Stewart

Variations in the warm cloud-active portion of atmospheric aerosols, or cloud condensation nuclei (CCN), have been shown to impact cloud droplet number concentration and subsequently cloud and precipitation processes. This issue carries special significance in western Colorado where a significant portion of the region's water resources is supplied by precipitation from winter season, orographic clouds, which are particularly sensitive to variations in CCN. Temporal and spatial variations in CCN in western Colorado were investigated using a combination of observations and a new method for modeling CCN. As part of the Inhibition of Snowfall by Pollution Aerosols (ISPA-III) field campaign, total particle and CCN number concentration were measured for a 24-day period in Mesa Verde National Park, climatologically upwind of the San Juan Mountains. These data were combined with CCN observations from Storm Peak Lab (SPL) in northwestern Colorado and from the King Air platform, flying north to south along the Western Slope. Altogether, the sampled aerosols were characteristic of a rural continental environment and the cloud-active portion varied slowly in time, and little in space. Estimates of the is hygroscopicity parameter indicated consistently low aerosol hygroscopicity typical of organic aerosol species. The modeling approach included the addition of prognostic CCN to the Regional Atmospheric Modeling System (RAMS). The RAMS droplet activation scheme was altered using parcel model simulations to include variations in aerosol hygroscopicity, represented by K. Analysis of the parcel model output and a supplemental sensitivity study showed that model CCN will be sensitive to changes in aerosol hygroscopicity, but only for conditions of low supersaturation or small particle sizes. Aerosol number, size distribution median radius, and hygroscopicity (represented by the K parameter) in RAMS were constrained by nudging to forecasts of these quantities from the Weather

Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia

Energy Technology Data Exchange (ETDEWEB)

Protat, Alain; Young, Stuart; McFarlane, Sally A.; L' Ecuyer, Tristan; Mace, Gerald G.; Comstock, Jennifer M.; Long, Charles N.; Berry, Elizabeth; Delanoe, Julien

2014-02-01

The objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar-lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (due to limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar - Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2 km height, due to instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar-lidar instruments and RT calculations are also found above 10 km (up to 0.35 Kday-1 for the shortwave and 0.8 Kday-1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud-radiation interactions in large-scale models and limitations of each set of instrumentation should be considered when interpreting model-observations differences.

flexCloud: Deployment of the FLEXPART Atmospheric Transport Model as a Cloud SaaS Environment

Science.gov (United States)

Morton, Don; Arnold, Dèlia

2014-05-01

FLEXPART (FLEXible PARTicle dispersion model) is a Lagrangian transport and dispersion model used by a growing international community. We have used it to simulate and forecast the atmospheric transport of wildfire smoke, volcanic ash and radionuclides. Additionally, FLEXPART may be run in backwards mode to provide information for the determination of emission sources such as nuclear emissions and greenhouse gases. This open source software is distributed in source code form, and has several compiler and library dependencies that users need to address. Although well-documented, getting it compiled, set up, running, and post-processed is often tedious, making it difficult for the inexperienced user. Our interest is in moving scientific modeling and simulation activities from site-specific clusters and supercomputers to a cloud model as a service paradigm. Choosing FLEXPART for our prototyping, our vision is to construct customised IaaS images containing fully-compiled and configured FLEXPART codes, including pre-processing, execution and postprocessing components. In addition, with the inclusion of a small web server in the image, we introduce a web-accessible graphical user interface that drives the system. A further initiative being pursued is the deployment of multiple, simultaneous FLEXPART ensembles in the cloud. A single front-end web interface is used to define the ensemble members, and separate cloud instances are launched, on-demand, to run the individual models and to conglomerate the outputs into a unified display. The outcome of this work is a Software as a Service (Saas) deployment whereby the details of the underlying modeling systems are hidden, allowing modelers to perform their science activities without the burden of considering implementation details.

Longitudinal Control for Mengshi Autonomous Vehicle via Gauss Cloud Model

Directory of Open Access Journals (Sweden)

Hongbo Gao

2017-12-01

Full Text Available Dynamic robustness and stability control is a requirement for self-driving of autonomous vehicle. Longitudinal control technique of autonomous vehicle is basic theory and one key complex technique which must have the reliability and precision of vehicle controller. The longitudinal control technique is one of the foundations of the safety and stability of autonomous vehicle control. In our paper, we present a longitudinal control algorithm based on cloud model for Mengshi autonomous vehicle to ensure the dynamic stability and tracking performance of Mengshi autonomous vehicle. The longitudinal control algorithm mainly uses cloud model generator to control the acceleration of the autonomous vehicle to achieve the goal that controls the speed of Mengshi autonomous vehicle. The proposed longitudinal control algorithm based on cloud model is verified by real experiments on Highway driving scene. The experiments results of the acceleration and speed show that the algorithm is validity and stability.

The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

Science.gov (United States)

Kuçak, R. A.; Özdemir, E.; Erol, S.

2017-05-01

Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK

Directory of Open Access Journals (Sweden)

R. A. Kuçak

2017-05-01

Full Text Available Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM which is a type of ANN (Artificial Neural Network segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

The Role of Aerosols on Precipitation Processes: Cloud Resolving Model Simulations

Science.gov (United States)

Tao, Wei-Kuo; Li, X.; Matsui, T.

2012-01-01

Cloud microphysics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distributions below the clouds. Therefore, size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e. pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions. The model is tested by studying the evolution of deep cloud systems in the west Pacific warm pool region, the sub-tropics (Florida) and midlatitudes using identical thermodynamic conditions but with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. Results indicate that the low CCN concentration case produces rainfall at the surface sooner than the high CeN case but has less cloud water mass aloft. Because the spectral-bin model explicitly calculates and allows for the examination of both the mass and number concentration of species in each size category, a detailed analysis of the instantaneous size spectrum can be obtained for these cases. It is shown that since the low (CN case produces fewer droplets, larger sizes develop due to greater condensational and collection growth, leading to a broader size spectrum in comparison to the high CCN case. Sensitivity tests were performed to

Business models and business model innovation in a “Secure and Distributed Cloud Clustering (DISC) Society”

DEFF Research Database (Denmark)

Lindgren, Peter; Taran, Yariv

2011-01-01

of secure business models and how business models can be operated and innovated in a secure context have intensified tremendously. The development of new mobile and wireless security technologies gives hopes to really realize a secure cloud clustering society where business models can act and be innovated......The development and innovation of business models to a secure distributed cloud clustering society (DISC)—is indeed still a complex venture and has not been widely researched yet. Numerous types of security technologies are in these years proposed and in the “slip stream” of these the study...... secure—but we still have some steps to go before we reach the final destination. The paper gives a conceptual futuristic outlook on behalf of the input from SW2010 and state of the art business model research to what we can expect of business Model and business model innovation in a future secure cloud...

Processing Terrain Point Cloud Data

KAUST Repository

DeVore, Ronald; Petrova, Guergana; Hielsberg, Matthew; Owens, Luke; Clack, Billy; Sood, Alok

2013-01-01

Terrain point cloud data are typically acquired through some form of Light Detection And Ranging sensing. They form a rich resource that is important in a variety of applications including navigation, line of sight, and terrain visualization

A PATCHY CLOUD MODEL FOR THE L TO T DWARF TRANSITION

International Nuclear Information System (INIS)

Marley, Mark S.; Saumon, Didier; Goldblatt, Colin

2010-01-01

One mechanism suggested for the L to T dwarf spectral type transition is the appearance of relatively cloud-free regions across the disk of brown dwarfs as they cool. The existence of partly cloudy regions has been supported by evidence for variability in dwarfs in the late L to early T spectral range, but no self-consistent atmosphere models of such partly cloudy objects have yet been constructed. Here, we present a new approach for consistently modeling partly cloudy brown dwarfs and giant planets. We find that even a small fraction of cloud holes dramatically alter the atmospheric thermal profile, spectra, and photometric colors of a given object. With decreasing cloudiness objects briskly become bluer in J - K and brighten in J band, as is observed at the L/T transition. Model spectra of partly cloudy objects are similar to our models with globally homogenous, but thinner, clouds. Hence, spectra alone may not be sufficient to distinguish partial cloudiness although variability and polarization measurements are potential observational signatures. Finally, we note that partial cloud cover may be an alternative explanation for the blue L dwarfs.

Influence of 3D effects on 1D aerosol retrievals in synthetic, partially clouded scenes

International Nuclear Information System (INIS)

Stap, F.A.; Hasekamp, O.P.; Emde, C.; Röckmann, T.

2016-01-01

An important challenge in aerosol remote sensing is to retrieve aerosol properties in the vicinity of clouds and in cloud contaminated scenes. Satellite based multi-wavelength, multi-angular, photo-polarimetric instruments are particularly suited for this task as they have the ability to separate scattering by aerosol and cloud particles. Simultaneous aerosol/cloud retrievals using 1D radiative transfer codes cannot account for 3D effects such as shadows, cloud induced enhancements and darkening of cloud edges. In this study we investigate what errors are introduced on the retrieved optical and micro-physical aerosol properties, when these 3D effects are neglected in retrievals where the partial cloud cover is modeled using the Independent Pixel Approximation. To this end a generic, synthetic data set of PARASOL like observations for 3D scenes with partial, liquid water cloud cover is created. It is found that in scenes with random cloud distributions (i.e. broken cloud fields) and either low cloud optical thickness or low cloud fraction, the inversion algorithm can fit the observations and retrieve optical and micro-physical aerosol properties with sufficient accuracy. In scenes with non-random cloud distributions (e.g. at the edge of a cloud field) the inversion algorithm can fit the observations, however, here the retrieved real part of the refractive indices of both modes is biased. - Highlights: • An algorithm for retrieval of both aerosol and cloud properties is presented. • Radiative transfer models of 3D, partially clouded scenes are simulated. • Errors introduced in the retrieved aerosol properties are discussed.

Towards a government public cloud model: The case of South Africa

CSIR Research Space (South Africa)

Mvelase, PS

2013-06-01

Full Text Available the government to benefit from other cloud computing advantages. However, modelling a multidimensional social problem as complex as the public cloud for a national government requires time, knowledge and experience from a wide range of specialization disciplines...

Evaluation of stratocumulus cloud prediction in the Met Office forecast model during VOCALS-REx

Directory of Open Access Journals (Sweden)

S. J. Abel

2010-11-01

Full Text Available Observations in the subtropical southeast Pacific obtained during the VOCALS-REx field experiment are used to evaluate the representation of stratocumulus cloud in the Met Office forecast model and to identify key areas where model biases exist. Marked variations in the large scale structure of the cloud field were observed during the experiment on both day-to-day and on diurnal timescales. In the remote maritime region the model is shown to have a good representation of synoptically induced variability in both cloud cover and marine boundary layer depth. Satellite observations show a strong diurnal cycle in cloud fraction and liquid water path in the stratocumulus with enhanced clearances of the cloud deck along the Chilean and Peruvian coasts on certain days. The model accurately simulates the phase of the diurnal cycle but is unable to capture the coastal clearing of cloud. Observations along the 20° S latitude line show a gradual increase in the depth of the boundary layer away from the coast. This trend is well captured by the model (typical low bias of 200 m although significant errors exist at the coast where the model marine boundary layer is too shallow and moist. Drizzle in the model responds to changes in liquid water path in a manner that is consistent with previous ship-borne observations in the region although the intensity of this drizzle is likely to be too high, particularly in the more polluted coastal region where higher cloud droplet number concentrations are typical. Another mode of variability in the cloud field that the model is unable to capture are regions of pockets of open cellular convection embedded in the overcast stratocumulus deck and an example of such a feature that was sampled during VOCALS-REx is shown.