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FreeCompilerCamp.org: Training for OpenMP Compiler Development from Cloud
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Why NASA Earthdata Went to the Cloud
NASA earth science data will be easily accessible to all in an environment that allows the leverage of compute capabilities required for earth science visualization and analysis today and for the foreseeable future.
Climate Sensitivity and the Direct Effect of Carbon Dioxide in a Limited-Area Cloud-Resolving Model
Even in a small domain, it can be prohibitively expensive to run cloud-resolving greenhouse gas warming experiments due to the long equilibration time. Here, a technique is introduced that reduces the computational cost of these experiments by an order of magnitude: instead of fixing the carbon dioxide concentration and equilibrating the sea surface temperature (SST), this technique fixes the SST and equilibrates the carbon dioxide concentration. Using this approach in a cloud-resolving model of radiative–convective equilibrium (RCE), the equilibrated SST is obtained as a continuous function of carbon dioxide concentrations spanning 1 ppmv to nearly 10 000 ppmv, revealing a dramatic increase in equilibrium climate sensitivity (ECS) at higher temperatures. This increase in ECS is due to both an increase in forcing and a decrease in the feedback parameter. In addition, the technique is used to obtain the direct effects of carbon dioxide (i.e., the rapid adjustments) over a wide range of SSTs. Overall, the direct effect of carbon dioxide offsets a quarter of the increase in precipitation from warming, reduces the shallow cloud fraction by a small amount, and has no impact on convective available potential energy (CAPE).
Contribution of National near Real Time MODIS Forest Maximum Percentage NDVI Change Products to the U.S. ForWarn System
This presentation reviews the development, integration, and testing of Near Real Time (NRT) MODIS forest % maximum NDVI change products resident to the USDA Forest Service (USFS) ForWarn System. ForWarn is an Early Warning System (EWS) tool for detection and tracking of regionally evident forest change, which includes the U.S. Forest Change Assessment Viewer (FCAV) (a publically available on-line geospatial data viewer for visualizing and assessing the context of this apparent forest change). NASA Stennis Space Center (SSC) is working collaboratively with the USFS, ORNL, and USGS to contribute MODIS forest change products to ForWarn. These change products compare current NDVI derived from expedited eMODIS data, to historical NDVI products derived from MODIS MOD13 data. A new suite of forest change products are computed every 8 days and posted to the ForWarn system; this includes three different forest change products computed using three different historical baselines: 1) previous year; 2) previous three years; and 3) all previous years in the MODIS record going back to 2000. The change product inputs are maximum value NDVI that are composited across a 24 day interval and refreshed every 8 days so that resulting images for the conterminous U.S. are predominantly cloud-free yet still retain temporally relevant fresh information on changes in forest canopy greenness. These forest change products are computed at the native nominal resolution of the input reflectance bands at 231.66 meters, which equates to approx 5.4 hectares or 13.3 acres per pixel. The Time Series Product Tool, a MATLAB-based software package developed at NASA SSC, is used to temporally process, fuse, reduce noise, interpolate data voids, and re-aggregate the historical NDVI into 24 day composites, and then custom MATLAB scripts are used to temporally process the eMODIS NDVIs so that they are in synch with the historical NDVI products. Prior to posting, an in-house snow mask classification product is computed for the current compositing period and integrated into the change images to account for snow related NDVI drops. The supplemental snow classification product was needed because other available QA cloud/snow mask typically underestimates snow cover. MODIS true and false color composites were also computed from eMODIS reflectance data and the true color RGBs are also posted on ForWarn?s FCAV; this data is used for assessing apparent occasional quality issues on the change products due to residual unmasked cloud cover. New forest change products are posted with typical latencies of 1-2 days after the last input eMODIS data collection date for a given 24 day compositing period.
Zero-Trust Architecture for Autonomous Edge Computing
We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.
Effect of the Inhomogeneity of Ice Crystals on Retrieving Ice Cloud Optical Thickness and Effective Particle Size
Spherical or spheroidal air bubbles are generally trapped in the formation of rapidly growing ice crystals. In this study the single-scattering properties of inhomogeneous ice crystals containing air bubbles are investigated. Specifically, a computational model based on an improved geometric-optics method (IGOM) has been developed to simulate the scattering of light by randomly oriented hexagonal ice crystals containing spherical or spheroidal air bubbles. A combination of the ray-tracing technique and the Monte Carlo method is used. The effect of the air bubbles within ice crystals is to smooth the phase functions, diminish the 22deg and 46deg halo peaks, and substantially reduce the backscatter relative to bubble-free particles. These features vary with the number, sizes, locations and shapes of the air bubbles within ice crystals. Moreover, the asymmetry factors of inhomogeneous ice crystals decrease as the volume of air bubbles increases. Cloud reflectance lookup tables were generated at wavelengths 0.65 m and 2.13 m with different air-bubble conditions to examine the impact of the bubbles on retrieving ice cloud optical thickness and effective particle size. The reflectances simulated for inhomogeneous ice crystals are slightly larger than those computed for homogenous ice crystals at a wavelength of 0.65 microns. Thus, the retrieved cloud optical thicknesses are reduced by employing inhomogeneous ice cloud models. At a wavelength of 2.13 microns, including air bubbles in ice cloud models may also increase the reflectance. This effect implies that the retrieved effective particle sizes for inhomogeneous ice crystals are larger than those retrieved for homogeneous ice crystals, particularly, in the case of large air bubbles.
Size and density distribution of very small dust grains in the Barnard 5 cloud
The effects of the temperature fluctuations in small graphite grains on the energy spectrum and the IR surface brightness of an isolated dust cloud heated externally by the interstellar radiation field were investigated using a series of models based on a radiation transport computer code. This code treats self-consistently the thermal coupling between the transient heating of very small dust grains and the equilibrium heating of conventional large grains. The model results were compared with the IRAS observations of the Barnard 5 (B5) cloud, showing that the 25-micron emission of the cloud must be produced by small grains with a 6-10 A radius, which also contribute about 50 percent to the observed 12-micron emission. The remaining 12 micron flux may be produced by the polycyclic aromatic hydrocarbons. The 60-and 100-micron radiation is dominated by emission from large grains heated under equilibrium conditions.
Enhanced Monte Carlo Simulation of the Voxel Phantom Lattice Submersed in a Contaminated Air Environment
External dose coefficient calculations reported for submersions in semi-infinite clouds, as in the Environmental Protection Agency: Federal Guidance Report 12, assume a person standing on the ground submerged in a radioactive cloud. In the scope of the work presented here, Oak Ridge National Laboratory's Center for Radiation Protection Knowledge has computed dose coefficients for monoenergetic photons, electrons, and positrons for worker exposures in three rooms of finite sizes representing an office, laboratory, and warehouse. In the process of simulating these exposures, International Commission on Radiological Protection (ICRP) Publication 110 reference adult male and female voxel phantom lattices were placed in the rooms filled with contaminated air. It was apparent that additional simulations were need to treat the non-tissue voxels surrounding the phantoms as containing the contaminated air. Dose coefficients for three room sizes representing an office, laboratory, and warehouse were computed. The coefficients for electrons exhibit little dependence on room size for most energies. However, for organs within the range of electron transport, the dose from source activity within the non-tissue voxels of the lattice surrounding the phantom becomes increasingly important. The photon dose coefficients vary based on room size because of longer mean free paths. It is important to include the air surrounding the voxel phantom within the voxel lattice in the calculations, when simulating a voxel phantom submersed in air for source particle energies below 200-300 keV, otherwise, the organ dose coefficient would be underestimated. Organ and effective dose coefficients for each gender, energy, and room size will be tabulated on the Oak Ridge National Laboratory - Center for Radiation Protection Knowledge website: http://ornl.gov/crpk.
Ionospheric and satellite remote sensing of tornadic storms
Gravity waves associated with tornadoes were studied by using a ground-based ionospheric sounding system and satellite visible and infrared photographs of clouds associated with storms. Examples were chosen from gravity waves associated with a group of tornadoes and with isolated tornadic storms. Ray tracing computation was carried out to identify the location of the wave source. It is found that the wave sources of gravity waves associated with tornadoes were located on the squall line and/or isolated clouds where the tornado touched down one hour after the gravity waves were excited.
A flux-limited treatment for the conductive evaporation of spherical interstellar gas clouds
In this work, we present and analyze a new analytic solution for the saturated (flux-limited) thermal evaporation of a spherical cloud. This work is distinguished from earlier analytic studies by allowing the thermal conductivity to change continuously from a diffusive to a saturated form, in a manner usually employed only in numerical calculations. This closed form solution will be of interest as a computational benchmark. Using our calculated temperature profiles and mass-loss rates, we model the thermal evaporation of such a cloud under typical interstellar medium (ISM) conditions, with some restrictions. We examine the ionization structure of the cloud-ISM interface and evaluate column densities of carbon, nitrogen, oxygen, neon, and silicon ions toward the cloud. In accord with other investigations, we find that ionization equilibrium is far from satisfied under the assumed conditions. Since the inclusion of saturation effects in the heat flux narrows the thermal interface relative to its classical structure, we also find that saturation effects tend to lower predicted column densities.
Estimation of the cloud transmittance from radiometric measurements at the ground level
The extinction of solar radiation due to the clouds is more significant than due to any other atmospheric constituent, but it is always difficult to be modeled because of the random distribution of clouds on the sky. Moreover, the transmittance of a layer of clouds is in a very complex relation with their type and depth. A method for estimating cloud transmittance was proposed in Paulescu et al. (Energ. Convers. Manage, 75 690–697, 2014). The approach is based on the hypothesis that the structure of the cloud covering the sun at a time moment does not change significantly in a short time interval (several minutes). Thus, the cloud transmittance can be calculated as the estimated coefficient of a simple linear regression for the computed versus measured solar irradiance in a time interval Δt. The aim of this paper is to optimize the length of the time interval Δt. Radiometric data measured on the Solar Platform of the West University of Timisoara during 2010 at a frequency of 1/15 seconds are used in this study.
An Optimal Relation of Radar Reflectivity to Lightning Rate
Thunderstorms separate charge. Most places they lift positive charge or lower negative, a few places they lift negative or lower positive. The electrical generator is stronger in some parts of the cloud than in others. Our long term goal is to map this generator. Cloud physicists tell us that uncharged ice and water particles become charged by collision, and that the charge transferred depends on size, temperature and humidity. There is still some disagreement about exactly how the charge transferred depends on size, temperature, and humidity. In principle, if we knew this ice physics, and also knew the distribution of particles everywhere in the storm, and the winds everywhere and the temperature and humidity everywhere, then we could compute everywhere the electrical power of the thunderstorm generator. In practice it is difficult to know all these things, particularly the distribution of particles, so it is difficult to use real thunderstorms to falsify cloud electrification theories. We here take one small step towards computing that map of electrical generator power, by relating radar reflectivity profiles of 2000 storms to lightning flash rates of those storms. This small step by itself doesn't falsify any existing electrification theories; it merely places weak constraints on the relation of electric generator power to cloud ice.
The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques
The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.
Progress in fast, accurate multi-scale climate simulations
We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.
High Resolution Measurements and Modeling in the Arctic
This report details the activity of the project, "High Resolution Modeling and Measurements in the Arctic" spanning Fiscal Years 2016 - 2018 supported by the Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program. The project's primary goal was to test the hypothesis that global climate model bias of low boundary layer clouds lacking liquid water in the Arctic could be improved by increasing horizontal resolution in the model. As model resolution is constrained by computational resources, four different model types were explored and compared to test the project's primary theory. Given the Arctic is a data-sparse region lacking robust data sets of liquid water in clouds, this project also obtained in situ measurements of low clouds with sensors on a tethered balloon system to constrain and compare with the models. Although other model biases remained, the liquid water path generally increased with resolution, supporting the original hypothesis.
Evaluation of autoconversion and accretion enhancement factors in general circulation model warm-rain parameterizations using ground-based measurements over the Azores
A great challenge in climate modeling is how to parameterize subgrid cloud processes, such as autoconversion and accretion in warm-rain formation. In this study, we use ground-based observations and retrievals over the Azores to investigate the so-called enhancement factors, E auto and E accr , which are often used in climate models to account for the influence of subgrid variance of cloud and precipitation water on the autoconversion and accretion processes. E auto and E accr are computed for different equivalent model grid sizes. The calculated E auto values increase from 1.96 (30 km) to 3.2 (180 km), and the calculated E accr values increase from 1.53 (30 km) to 1.76 (180 km). Comparing the prescribed enhancement factors in Morrison and Gettleman (2008, MG08) to the observed ones, we found that a higher E auto (3.2) at small grids and lower E accr (1.07) are used in MG08, which might explain why most of the general circulation models (GCMs) produce too-frequent precipitation events but with too-light precipitation intensity. The ratios of the rain to cloud water mixing ratio ( q r / q c ) at E accr =1.07 and E accr =2.0 are 0.063 and 0.142, respectively, from observations, further suggesting that the prescribed value of E accr =1.07 used in MG08 is too small to simulate precipitation intensity correctly. Both E auto and E accr increase when the boundary layer becomes less stable, and the values are larger in precipitating clouds ( CLWP>75 gm -2 ) than those in non-precipitating clouds ( CLWP<75 gm -2 ). Therefore, the selection of E auto and E accr values in GCMs should be regime- and resolution-dependent.
Aerosol Direct Radiative Effect at the Top of the Atmosphere Over Cloud Free Ocean Derived from Four Years of MODIS Data
A four year record of MODIS spaceborne data provides a new measurement tool to assess the aerosol direct radiative effect at the top of the atmosphere. MODIS derives the aerosol optical thickness and microphysical properties from the scattered sunlight at 0.55-2.1 microns. The monthly MODIS data used here are accumulated measurements across a wide range of view and scattering angles and represent the aerosol s spectrally resolved angular properties. We use these data consistently to compute with estimated accuracy of +/-0.6W/sq m the reflected sunlight by the aerosol over global oceans in cloud free conditions. The MODIS high spatial resolution (0.5 km) allows observation of the aerosol impact between clouds that can be missed by other sensors with larger footprints. We found that over the clear-sky global ocean the aerosol reflected 5.3+/-0.6W/sq m with an average radiative efficiency of 49+/-2W/sq m per unit optical thickness. The seasonal and regional distribution of the aerosol radiative effects are discussed. The analysis adds a new measurement perspective to a climate change problem dominated so far by models.