Get your applications ready for the cloud: Application Rationalization
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Silva, Valerie Renee↗
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Observing the Energetic Effects of High Clouds: Applications to “Hot Towers”, Convective Lifecycles, and Cloud Feedbacks
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high clouds↗
Radiative diffusivity factors in cirrus and stratocumulus clouds: Application to two-stream models
A diffusion-like description of radiative transfer in clouds and the free atmosphere is often used. The two stream model is probably the best known example of such a description. The main idea behind the approach is that only the first few moments of radiance are needed to describe the radiative field correctly. Integration smooths details of the angular distribution of specific intensity and it is assumed that the closure parameters of the theory (diffusivity factors) are only weakly dependent on the distribution. The diffusivity factors are investigated using the results obtained from both Stratocumulus and Cirrus phases of FIRE experiment. A new theoretical framework is described in which two (upwards and downwards) diffusivity factors are used and a detailed multistream model is used to provide further insight about both the diffusivity factors and their dependence on scattering properties of clouds.
Stephens, Graeme L.↗
A general model for Io's neutral gas clouds. II - Application to the sodium cloud
A previously developed neutral cloud model is applied to the neutral sodium cloud produced by Io. Past models for the cloud are reviewed and the motivation for developing better models is discussed. The interactions of sodium in the plasma torus are discussed. It is shown how the new model can be used to provide a consistent physical explanation for the basic physical characteristics of the spatially extended Io sodium cloud and for the east-west intensity asymmetry observed very near Io. This leads to definite conclusions regarding the flux distribution and density of sodium in Io's exosphere, the level of the collisional exobase in the atmosphere, the nature of the interaction of the corotating plasma torus and the satellite atmosphere/corona, and the presence of an east-west electric field in the planetary system.
Smyth, W. H.↗
Using spatial coherence to retrieve cloud properties - ERBE experience and CERES applications
Cloud properties for individual ERBE scanner fields of view are obtained from simultaneous, collocated imagery data. The cloud properties are used to assess the performance of the ERBE maximum likelihood estimate of cloud conditions which ultimately influences estimates of the radiative fluxes at the top of the atmosphere. During EOS the analysis of MODIS observations within the fields of view of the CERES instruments promises to yield, in addition, estimates of surface radiative fluxes as well as the vertical distribution of radiative heating within the atmosphere.
Coakley, James A., Jr.↗
The effect of moonlight on observation of cloud cover at night, and application to cloud climatology
Ten years of nighttime weather observations from the Northern Hemisphere in December were classified according to the illuminance of moonlight or twilight on the cloud tops, and a threshold level of illuminance was determined, above which the clouds are apparently detected adequately. This threshold corresponds to light from a full moon at an elevation angle of 6 deg, light from a partial moon at higher elevation, or twilight from the sun less than 9 deg bvelow the horizon. It permits the use of about 38% of the observations made with the sun below the horizon. The computed diurnal cycles of total cloud cover are altered considerably when this moonlight criterion is imposed. Maximum cloud cover over much of the ocean is now found to be at night or in the morning, whereas computations obtained without benefit of the moonlight criterion, as in our published atlases, showed the time of maximum to be noon or early afternoon in many regions. The diurnal cycles of total cloud cover we obtain are compared with those of the International Satellite Cloud Climatology Project (ISCCP) for a few regions; they are generally in better agreement if the moonlight criterion is imposed on the surface observations. Using the moonlight criterion, we have analyzed 10 years (1982-91) of surface weather observations over land and ocean, worldwide, for total cloud cover and for the frequency of occurrence of clear sky, fog, and precipitation. The global average cloud cover (average of day and night) is about 2% higher if the moonlight criterion is imposed than if all observations are used. The difference is greater in winter than in summer, because of the fewer hours of darkness in summer. The amplitude of the annual cycle of total cloud cover over the Arctic Ocean and at the South Pole is diminished by a few percent when the moonlight criterion is imposed. The average cloud cover for 1982-91 is found to be 55% for Northern Hemisphere land, 53% for Southern Hemisphere land, 66% for Northern Hemisphere ocean, and 70% for Southern Hemisphere ocean, giving a global average of 64%. The global average for daytime is 64.6%; for nighttime 63.3%.
Hahn, Carole J.↗
Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications
Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-‐real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-‐real time globally from both geostationary (GEO) and low-‐earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.
Minnis, Patrick↗
Online monitoring using cloud-based applications.
Online monitoring using cloud-based applications presentation. Enabling online monitoring using cloud services will require additional sensors, systems and choices.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Microphysical Timescales in Clouds and their Application in Cloud-Resolving Modeling
Computational phenomena (i.e., spurious supersaturation and negative mixing ratio of cloud water) usually exist in cloud-resolving models when the time step for explicit integration is larger than a microphysical timescale in clouds. In this paper, the microphysical timescales in clouds are studied, showing that the timescale of water vapor condensation (or cloud water evaporation) is smaller than 10 s - the order of a typical time step for cloud-resolving models. To avoid spurious computational phenomena in cloud-resolving modeling, it is suggested that moist entropy be used as a prognostic thermodynamic variable, and temperature be diagnosed from that and other prognostic variables. A simple numerical model with moist entropy as a prognostic variable, for example, is presented to show that spurious computational phenomena are removed when moist entropy is used as a prognostic variable.
Zeng, Xi-Ping↗
Microphysical Timescales in Clouds and their Application in Cloud-Resolving Modeling
Independent prognostic variables in cloud-resolving modeling are chosen on the basis of the analysis of microphysical timescales in clouds versus a time step for numerical integration. Two of them are the moist entropy and the total mixing ratio of airborne water with no contributions from precipitating particles. As a result, temperature can be diagnosed easily from those prognostic variables, and cloud microphysics be separated (or modularized) from moist thermodynamics. Numerical comparison experiments show that those prognostic variables can work well while a large time step (e.g., 10 s) is used for numerical integration.
Zeng, Xiping↗
Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars
At millimeter wavelengths, attenuation by hydrometeors, such as liquid droplets or large snowflakes, is generally not negligible. When using multifrequency ground-based radar measurements, it is common practice to use the Rayleigh targets at cloud top as a reference in order to derive attenuation-corrected reflectivities and meaningful dual-frequency ratios (DFRs). By capitalizing on this idea, this study describes a new quality-controlled approach that aims at identifying regions of cloud where particle growth is negligible. The core of the method is the identification of a “Rayleigh plateau”, i.e., a large enough region near cloud top where the vertical gradient of DFR remains small. By analyzing co-located Ka–W band radar and microwave radiometer (MWR) observations taken at two European sites under various meteorological conditions, it is shown how the resulting estimates of differential path-integrated attenuation (ΔPIA) can be used to characterize hydrometeor properties. When the ΔPIA is predominantly produced by cloud liquid droplets, this technique alone can provide accurate estimates of the liquid water path. When combined with MWR observations, this methodology paves the way towards profiling the cloud liquid water, quality-flagging the MWR retrieval for rain and drizzle contamination, and/or estimating the snow differential attenuation.
54 ENVIRONMENTAL SCIENCES↗
A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications
Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information
Silber, Israel↗
Polarization Difference - The Scientific Story Behind One Variable and Its Practical Applications
Cloud ice and snow microphysical characteristics play an important role in determining cloud radiative effect. They are also closely connected to the details of surface precipitation characteristics. Ice orientation is one microphysical property that is traditionally believed to have trivial impact on the weather systems/climate, and it is hard to measure from space. In this presentation, I will show how many scientific stories can be told from one variable – the brightness temperature difference between vertically-polarized and horizontally-polarized 166 GHz measurements from the Global Precipitation Measurement Microwave Imager (GPM-GMI). Through scrutinizing collocated CloudSat, GMI and GPM radar observations together with the aid of radiative transfer model simulations, we can not only tell apart the particle size, shape and orientation, but also understand the precipitation regime as well as the life stage of a a precipitation system. In the second half of my talk, I will showcase how this variable can be used for predicting precipitation flags and separating mixed-phase cloud from liquid and ice with the powerful machine learning/artificial intelligence (ML/AI) technique.
cloud ice and snow microphysical characteristics↗
Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy
The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.
azure↗
Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants
Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Accelerating Parallel Applications in Cloud Platforms via Adaptive Time-Slice Control
Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation. Existing methods optimize parallel applications by promoting the priorities of involved VMs. They cannot fully explore the performance of parallel applications, because they ignore the time-slice requirements of different phases of parallel applications. Furthermore, non-parallel applications experience unsatisfied performance because of low scheduling priorities. Given empirical analysis on time-slices of virtual machines (VMs), we find that shortening time-slices can mitigate synchronization overhead which incurs during communication phases, while over-short time-slices cause frequent cache misses in computation phases. Accordingly, we propose an Adaptive Time-slice Control (ATC) mechanism. ATC first detects the phases of parallel applications based on lock latency or cache misses. Then, ATC shortens time-slices during communication phases and prolongs time-slices during computation phases for parallel applications, and sets a uniform time-slice for non-parallel applications. Finally, we evaluate ATC using seven well-known benchmarks with 25+ applications. Experiments show that ATC obtains 1.5-75x performance gain for running parallel applications than state-of-the-art solutions, with nearly unaffected impact on non-parallel applications.
97 MATHEMATICS AND COMPUTING↗
Evaluation and Applications of Cloud Climatologies from CALIOP
Clouds have a major impact on the Earth radiation budget and differences in the representation of clouds in global climate models are responsible for much of the spread in predicted climate sensitivity. Existing cloud climatologies, against which these models can be tested, have many limitations. The CALIOP lidar, carried on the CALIPSO satellite, has now acquired over two years of nearly continuous cloud and aerosol observations. This dataset provides an improved basis for the characterization of 3-D global cloudiness. Global average cloud cover measured by CALIOP is about 75%, significantly higher than for existing cloud climatologies due to the sensitivity of CALIOP to optically thin cloud. Day/night biases in cloud detection appear to be small. This presentation will discuss detection sensitivity and other issues associated with producing a cloud climatology, characteristics of cloud cover statistics derived from CALIOP data, and applications of those statistics.
Winker, David↗
Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center
Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.
Molthan, Andrew L.↗