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NASA NTRS2019

Cloud Governance at Scale

Operating and maintaining a large multi-tenant ecosystem in the cloud requires scalable solutions to unique technical and process challenges. The Cloud Computing model grants significant permissions to development teams that traditionally were reserved for Data-Center Administrators and Supply-Chain Managers. Earthdata Cloud has worked to re-cast traditional data-center management into a sensible cloud-first model. This talk discusses some of our challenges, solutions, and way ahead.

financial controls↗

NASA NTRS2015

Advanced Computing Support at NASA Advanced Supercomputing (NAS) Division

NAS (NASA Advanced Supercomputing) Division: Advanced Computing: High-End Computing Capability (HECC) Project; Quantum Artificial Intelligence Lab (QuAIL); NASA Earth Exchange (NEX); Data Analytics Infrastructure for NASA; Cloud Services for Science & Engineering Projects.

Advanced Computing↗

NASA NTRS1982

Characteristics of middle and upper tropospheric clouds as deduced from rawinsonde data

The static environment of middle and upper tropospheric clouds is characterized. Computed relative humidity with respect to ice is used to diagnose the presence of cloud layer. The deduced seasonal mean cloud cover estimates based on this technique are shown to be reasonable. The cases are stratified by season and pressure thickness, and the dry static stability, vertical wind speed shear, and Richardson number are computed for three layers for each case. Mean values for each parameter are presented for each stratification and layer. The relative frequency of occurrence of various structures is presented for each stratification. The observed values of each parameter and the observed structure of each parameter are quite variable. Structures corresponding to any of a number of different conceptual models may be found. Moist adiabatic conditions are not commonly observed and the stratification based on thickness yields substantially different results for each group.

Starr, D. D. O.↗

NASA NTRSDate not supplied

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗

NASA NTRS2019

"Sensor Web Evolution - Webs of Webs for NASA Science - Focus on small Uninhabited Aerial Systems (sUAS)"

This paper will describe the evolution of information collection, derivation and delivery mechanisms in webs of NASA sensor webs, with a focus on recent advancements in small Uninhabited Aerial Systems (sUAS). I will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, sUASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary sUASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. I will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing sUASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to sUAS development. This paper will focus on application, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

Sensor Web↗

NASA NTRS1984

Statistical analysis and use of the VAS radiance data

Special radiosonde soundings at 75 km spacings and 3 hour intervals provided an opportunity to learn more about mesoscale data and storm-environment interactions. Relatively small areas of intense convection produce major changes in surrounding fields of thermodynamic, kinematic, and energy variables. The Red River Valley tornado outbreak was studied. Satellite imagery and surface data were used to specify cloud information needed in the radiative heating/cooling calculations. A feasibility study for computing boundary layer winds from satellite-derived thermal data was completed. Winds obtained from TIROS-N retrievals compared very favorably with corresponding values from concurrent rawisonde thermal data, and both sets of thermally-derived winds showed good agreements with observed values.

Fuelberg, H. E.↗

NASA NTRS1975

Extension of four-dimensional atmospheric models

The cloud data bank, the 4-D atmospheric model, and a set of computer programs designed to simulate meteorological conditions for any location above the earth are described in turns of space vehicle design and simulation of vehicle reentry trajectories. Topics discussed include: the relationship between satellite and surface observed cloud cover using LANDSAT 1 photographs and including the effects of cloud shadows; extension of the 4-D model to the altitude of 52 km; and addition of the u and v wind components to the 4-D model of means and variances at 1 km levels from the surface to 25 km. Results of the cloud cover analysis are presented along with the stratospheric model and the tropospheric wind profiles.

Fowler, M. G.↗

NASA NTRS1971

Corkscrews and singularities in fruitflies - Resetting behavior of the circadian eclosion rhythm.

Description of experiments undertaken to define the phase-resetting behavior of the circadian rhythm of pupal eclosion in populations of fruitflies. An attempt is made to determine how and why the resetting response depends on the duration of a standard perturbation as well as on the time at which it is given. Plotting a three-dimensional graph of the measured emergence centroids vs the stimulus variables, the data are found to spiral up around a vertical rotation axis. Using a computer, a smooth surface, called the resetting surface, which approximately fits the helicoidal cloud of data points, is obtained and is shown to be best described as a vertical corkscrew linking together tilted planes. This corkscrew feature of the resetting surface is taken to indicate that there is an isolated perturbation following which there is either no circadian rhythm of emergence in the steady state, or one of unpredictable phase. A hypothesis concerning the clock dynamics underlying the eclosion rhythm is briefly sketched which encompasses the main features of known resetting data using single discrete pulses of any perturbing agent.

Winfree, A. T.↗

NASA NTRS1986

Turbulent dispersion of the icing cloud from spray nozzles used in icing tunnels

To correctly simulate flight in natural icing conditions, the turbulence in an icing simulator must be as low as possible. But some turbulence is required to mix the droplets from the spray nozzles and achieve an icing cloud of uniform liquid water content. The goal for any spray system is to obtain the widest possible spray cloud with the lowest possible turbulence in the test section of a icing tunnel. This investigation reports the measurement of turbulence and the three-dimensional spread of the cloud from a single spray nozzle. The task was to determine how the air turbulence and cloud width are affected by spray bars of quite different drag coefficients, by changes in the turbulence upstream of the spray, the droplet size, and the atomizing air. An ice accretion grid, located 6.3 m downstream of the single spray nozzle, was used to measure cloud spread. Both the spray bar and the grid were located in the constant velocity test section. Three spray bar shapes were tested: the short blunt spray bar used in the NASA Lewis Icing Research Tunnel, a thin 14.6 cm chord airfoil, and a 53 cm chord NACA 0012 airfoil. At the low airspeed (56 km/hr) the ice accretion pattern was axisymmetric and was not affected by the shape of the spray bar. At the high airspeed (169 km/hr) the spread was 30 percent smaller than at the low airspeed. For the widest cloud the spray bars should be located as far upstream in the low velocity plenum of the icing tunnel. Good comparison is obtained between the cloud spread data and predicitons from a two-dimensional cloud mixing computer code using the two equation turbulence (k epsilon g) model.

Marek, C. J.↗

NASA NTRS1991

Spatial and temporal variability of global surface solar irradiance

Consideration is given to a fast scheme for computing surface solar irradiance using data from the International Satellite Cloud Climatology Project (ISCCP). Daily mean solar irradiances from the fast scheme reproduce the detailed global results from full radiative transfer model calculations to within 6 and 10 W/sq m over the ocean and land, respectively. Comparison of calculated monthly mean results using 5 m of ISCCP data (July 1983-July 1984) with climatology from the 1970s at six temperature-latitude ocean weather stations shows agreement within published estimates of interannual variability of monthly means at the individual stations. A further test against a 17-day time series at a continental site, where ground and satellite data were spatially and temporally coincident, showed an accuracy of better than 9 W/sq m on a daily basis and less than 4 percent bias in the 17-day mean. Frequently used bulk formulas for solar irradiance are also evaluated in each of these tests.

Bishop, James K. B.↗

NASA NTRS2015

Technical Challenges and Opportunities of Centralizing Space Science Mission Operations (SSMO) at NASA Goddard Space Flight Center

The NASA Goddard Space Science Mission Operations project (SSMO) is performing a technical cost-benefit analysis for centralizing and consolidating operations of a diverse set of missions into a unified and integrated technical infrastructure. The presentation will focus on the notion of normalizing spacecraft operations processes, workflows, and tools. It will also show the processes of creating a standardized open architecture, creating common security models and implementations, interfaces, services, automations, notifications, alerts, logging, publish, subscribe and middleware capabilities. The presentation will also discuss how to leverage traditional capabilities, along with virtualization, cloud computing services, control groups and containers, and possibly Big Data concepts.

Science↗

NASA NTRS2016

NASA Briefing for Unidata

The NASA representative to the Unidata Strategic Committee presented a semiannual update on NASAs work with and use of Unidata technologies. The talk covered the program of cloud computing prototypes being undertaken for the Earth Observing System Data and Information System (EOSDIS). Also discussed were dataset interoperability recommendations ratified via the EOSDIS Standards Office and the HDF Product Designer tool with respect to its possible applicability to data in network Common Data Form (NetCDF) version 4.

data integration↗

NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗

NASA NTRSDate not supplied

TPSAS-NF1676L-11082-DND

Analysis of one decade of radar-lidar and Geostationary Operational Environmental Satellite (GOES) observations at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site reveals that there is excellent agreement in the long-term mean cloud fractions (CF) derived from the surface and GOES data, and the CF is independent of temporal resolution and spatial scales for grid boxes of size 0.5o to 2.5Â°. When computed over a 0.5-hr (4-hr) period, cloud frequency of occurrence (FREQ) and amount when present (AWP) derived from the point surface data agree very well with the same quantities determined from GOES for a 0.5o (2.5o) region centered on the ARM SGP site. The values of FREQ (AWP) derived from the radar-lidar observations at a given altitude increase (decrease) as the averaging period increases from 5 min to 6 hours. Similarly, CF at a given altitude increases as the vertical resolution increases from 90 to 1000 m. The profiles of CF have distinct bimodal vertical distributions with a lower peak between 1 and 2 km and a higher one between 8 and 11 km. The 10-year mean total CF, 46.9%, varies seasonally from a summer minimum of 39.8% to a maximum of 54.6% during the winter. The annual mean CF is 1-2% less than that from previous studies, ~48-49%, because fewer clouds occurred during 2005 and 2006, especially during winter. The differences in single- and multi-layered CFs between this study and an earlier analysis can be explained by the different temporal resolutions used in the two studies where single-layered CFs decrease but multilayered CFs increase from a 5-min resolution to a 1-hr resolution. The vertical distribution of nighttime GOES high-cloud tops agrees well with surface observations, but during daytime, fewer high clouds are retrieved by the GOES analysis than seen from the surface observations. The frequencies of occurrence for both daytime and nighttime GOES low-cloud tops are significantly higher than surface observations, but the cloud fractions are in good agreement.

B Xi↗

NASA NTRS2011

Hyperspectral Remote Sensing of Atmosphere and Surface Properties

Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS) are all hyper-spectral satellite sensors with thousands of spectral channels. Top of atmospheric radiance spectra measured by these sensors contain high information content on atmospheric, cloud, and surface properties. Exploring high information content contained in these high spectral resolution spectra is a challenging task due to computation e ort involved in modeling thousands of spectral channels. Usually, only very small fractions (4{10 percent) of the available channels are included in physical retrieval systems or numerical weather forecast (NWP) satellite data assimilations. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Because the EOFs are orthogonal to each other, about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. There is no need to perform EOF transformations to convert super channels back to spectral space at each iteration step for a one-dimensional variational retrieval or a NWP data assimilation system. The PCRTM forward model is also capable of calculating radiative contributions due to multiple-layer clouds. The multiple scattering effects of the clouds are efficiently parameterized. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in super channel domain directly therefore both reducing the computational need and preserving the information content of the IASI measurements. The inversion algorithm is based on a non-linear Levenberg-Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the hyper-spectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a sub-set of the channels.

Liu, Xu↗

NASA NTRS2011

Exploring the Universe with WISE and Cloud Computing

WISE is a recently-completed astronomical survey mission that has imaged the entire sky in four infrared wavelength bands. The large quantity of science images returned consists of 2,776,922 individual snapshots in various locations in each band which, along with ancillary data, totals around 110TB of raw, uncompressed data. Making the most use of this data requires advanced computing resources. I will discuss some initial attempts in the use of cloud computing to make this large problem tractable.

Benford, Dominic J.↗

NASA NTRSDate not supplied

UASs in the VOG/Edge/FOG Sensor Web Environment

This paper will describe the evolution of information collection, derivation and delivery mechanisms in sensor webs utilizing Uninhabited Aerial Systems (UAS).We will discuss the movement to "Fog Computing", also known as Edge Computing. Fog Computing facilitates the distribution of common operations and networking between edge devices and cloud computing facilities, optimizing the production of actionable intelligence. Initially, UASs utilized onboard data collection as standard, with minimal data downloaded directly. Information products were derived in conventional computational environments, generally desk top computers, and information products made available to the Science Community in weeks or months. With the increased availability, and increasingly lower costs, of beyond line of sight (BLOS) satellite based communication, transmission rates and data volumes increased, and processing migrated to Cloud based services. Contemporary UASs are moving some of that information product derivation to on vehicle services, and are creating a distributed Cloud/Fog environment. The Author will describe the technological advances that have made this possible, including low power multi-core Central Processing Units (CPU), and, more recently, the availability of high end Graphical Processing Units (GPU) that consume only a few watts. Intelligent system software, leveraging these hardware advances, finally allows for information product generation on-board, rather than simple data collection. Additionally, intelligent flight control systems now support mutual vehicle to vehicle collaboration, allowing UASs to create ad-hoc sensor webs on demand, as required. Also discussed will be the lessons learned by the Authors' development of data systems for NASA's large High Altitude Long Endurance (HALE) UASs like Predator and Global Hawk, and how those lessons are being applied to other UAS development This paper will focus on applications, rather a deep dive into the technology, and will highlight improving data management through these new technologies.

UAS↗

NASA NTRS2001

GOES-Microburst Products Performance Analysis In The Cape Canaveral And Kennedy Space Center Areas

In the past few years, the capabilities of NOAA Geostationary Operational Environmental Satellites (GOES) have increased dramatically. Hourly vertical sounder data is now generally available, but may be unavailable depending upon cloud conditions, satellite operations, and computer system problems at NOAA's National Environmental Satellite Display and Information Service (NESDIS). Meteorologists at NESDIS have used vertical sounder data to develop experimental products for forecasting the probability of convective downbursts. The two products of interest are the Microburst Day Predictive Index (MDPI), which provides an indication of microburst potential and the WINDEX which is a forecast of maximum winds assuming a microburst does occur. Data analyses were made for the central Florida convective season, that is, the period beginning May I and ending September 30. The MDPI showed significant potential as an aid in forecasting convective downbursts. MDPI calculated from GOES soundings were well correlated with those calculated from Cape Canaveral RAOBs.

Witiw, Michael R.↗