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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Relation between West Coastal rainfall and Nimbus 6 SCAMS liquid water data over the northeastern Pacific Ocean

A research study is described in which the application to rainfall prediction of cloud liquid water data obtained from the SCAMS experiment of Nimbus 6 is explored. The study area is the Pacific Northwest coast of the United States, where rainfall is produced by extratropical storms that approach from across the Pacific Ocean. SCAMS data related to cloud liquid water over the ocean, and coastal rainfall data, are analyzed for 20 different storm systems in the northeastern Pacific Ocean; these produced significant rainfall from Washington to central California during the period October 1975-March 1976. Results show that the distribution of storm-cloud water analyzed from the SCAMS data over the ocean foreshadows the distribution of coastal rainfall accumulated from the storm at a later time. It is concluded that passive microwave sensor measurements of cloud water over the ocean, when used in conjunction with numerical and other objective guidance, can be used to enhance the accuracy of predictions of coastal rainfall distribution. Limitations in the SCAMS measurements and in the data analysis and interpretation are noted.

Viezee, W.↗
NASA NTRS1992

Lidar Studies of Extinction in Clouds in the ECLIPS Project

The Experimental Cloud Lidar Pilot Study (ECLIPS) project has now had two active phases in 1989 and 1991. A number of laboratories around the world have taken part in the study. The observations have yielded new data on cloud height and structure, and have yielded some useful new information on the retrieval of cloud optical properties, together with the uncertainties involved. Clouds have a major impact on the climate of the earth. They have the effect of reducing the mean surface temperature from 30 C for a cloudless planet to a value of about 15 C for present cloud conditions. However, it is not at all certain how clouds would react to a change in the planetary temperature in the event of climate change due to a radiative forcing from greenhouse gases. Clouds both reflect out sunlight (negative feedback) and enhance the greenhouse effect (positive feedback), but the ultimate sign of cloud feedback is unknown. Because of these uncertainties, campaigns to study clouds intensely were initiated. The International Satellite Cloud Climatology (ISCPP) and the FIRE Campaigns (cirrus and stratocumulus) are examples. The ECLIPS was set up similarly to the above experiments to obtain information specifically on cloud base, but also cloud top (where possible), optical properties, and cloud structure. ECLIPS was designed to allow as many laboratories as possible globally to take part to get the largest range of clouds. It involves observations with elastic backscatter lidar, supported by infrared fluxes at the ground and radiosonde data, as basic instrumentation. More complex experiments using beam filter radiometers, solar pyranometers, and satellite data and often associated with other campaigns were also encouraged to join ECLIPS. Two periods for observation were chosen, Sep. - Dec. 1989 and Apr. - Jul. 1992 into which investigators were requested to fit 30 days of observations. These would be either continuous, or arranged to coincide with NOAA satellite overpasses to obtain AVHRR data. The distribution of the ECLIPS international effort as in 1991 is shown. The main gaps in the global distribution are in the tropics and the Southern Hemisphere.

Martin, C.↗
DOE OSTI2014

Evaluation of convection‐permitting model simulations of cloud populations associated with the Madden‐Julian Oscillation using data collected during the AMIE/DYNAMO field campaign

Abstract Regional convection‐permitting model simulations of cloud populations observed during the 2011 Atmospheric Radiation Measurement (ARM) Madden‐Julian Oscillation Investigation Experiment/Dynamics of the Madden‐Julian Oscillation Experiment (AMIE/DYNAMO) field campaign are evaluated against ground‐based radar and ship‐based observations. Sensitivity of model simulated reflectivity, surface rain rate, and cold pool statistics to variations of raindrop breakup/self‐collection parameters in four state‐of‐the‐art two‐moment bulk microphysics schemes in the Weather Research and Forecasting (WRF) model is examined. The model simulations generally overestimate reflectivity from large and deep convective cells, and underestimate stratiform rain and the frequency of cold pools. In the sensitivity experiments, introduction of more aggressive raindrop breakup or decreasing the self‐collection efficiency increases the cold pool occurrence frequency in all of the simulations, and slightly reduces the reflectivity and precipitation statistics bias in some schemes but has little effect on the overall mean surface precipitation. Both the radar observations and model simulations of cloud populations show an approximate power law relationship between convective echo‐top height and equivalent convective cell radius.

Hagos, Samson↗
DOE OSTI2015

High Performance Data Analytics on a Commodity Private Cloud

Sandia is researching novel methods of analysis for large datasets. The term “Big Data” has been used frequently in recent years to describe a discipline of computer science that attempts to distil an increasing quantity of data into a result or meaningful subset that can be easily visualized and acted upon by human consumers. These data are frequently characterized by one or more of the following: high volume, velocity, variety, and veracity. Sandia scientists and researchers are no strangers to large datasets, which often typify the result of high-performance computing workloads. The challenge for Sandia data scientists, however, comes at the arrival of exascale computing, which has become mandated by executive order for national computing centers. Because of power and networking limitations, exascale computing precludes the movement of datasets at exascale from data production to data analysis. Such analysis must be done in-situ, to mitigate the time and power costs of network transport. Currently, many of Sandia’s data analytics environments exist as independent entities, often specialized for individual use cases, interconnected to other data analytics, high-performance computing, and storage systems. Sandia is procuring, installing, and researching future platforms for such data analytics. A memory footprint of 256 GB per node, fourteen data rate (FDR) InfiniBand networking, PCIe connected non-volatile memory (NVMe) data storage, and an object data store will characterize the coming institutional data analytics platforms. The combined systems will contain 300 compute nodes. A cloud-inspired software stack resulting from collaboration between the Sandia centers will allow the systems to support a flexible mix of user and system-facing workloads and use cases including code development and testing in R and Python, Apache Hadoop and Spark, distributed in-memory databases, graph analytics, emulytics, system log and predictive analytics, and data visualization for institutional and mission customers. These systems will arrive at the end of FY 2015 and production is planned by the spring of FY 2016.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

62°S Witnesses the Transition of Boundary Layer Marine Aerosol Pattern Over the Southern Ocean (50°S–68°S, 63°E– 150°E) During the Spring and Summer: Results From MARCUS (I)

The Atmospheric Radiation Measurement Mobile Facility-2 was installed onboard the research vessel Aurora Australis to measure aerosol properties during the 2017-2018 Measurement of Aerosols, Radiation, and CloUds over the pristine Southern ocean (MARCUS) Experiment, providing unique data on aerosols latitudinal and seasonal variation, including south of 60 degrees S where previous observations are scarce. Data from a Cloud Condensation Nuclei (CCN) counter and Ultra-High-Sensitivity Aerosol Spectrometer show that both the number concentration (N-CCN) and size distribution of CCN-active aerosols, with diameters (D) between 60 nm < D < 1,000 nm are different over the North Southern Ocean (NSO) (50 degrees S-60 degrees S) and the South Southern Ocean (SSO) (62 degrees S-68 degrees S). The average NSO N-CCN at 0.2% and 0.5% supersaturation were 28% and 49% less than that over the SSO. This increase of CCN over the SSO is caused by the increase of aerosols with 60 nm < D < 200 nm, consistent with calculations of Aerosol Scattering Angstrom Exponents derived from a nephelometer. Aerosol hygroscopicity growth factor measured by the Hygroscopic Tandem Differential Mobility Analyzer stayed close to 1.41 for aerosols with 50 nm < D < 250 nm over the SSO, but increased from 1.30 to 1.67 over the NSO, indicating different chemical compositions. Both CCN and Ice Nucleating Particles (INPs) showed a stronger variation with season than with latitude. The variation of heat-labile and presumably proteinacous INPs suggests an increase of ice nucleating-active microbes in summer.

Niu, Qing↗
DOE OSTI2014

Cloud-Enabled Microscopy and Droplet Microfluidic Platform for Specific Detection of Escherichia coli in Water

We report an all-in-one platform – ScanDrop – for the rapid and specific capture, detection, and identification of bacteria in drinking water. The ScanDrop platform integrates droplet microfluidics, a portable imaging system, and cloud-based control software and data storage. The cloud-based control software and data storage enables robotic image acquisition, remote image processing, and rapid data sharing. These features form a “cloud” network for water quality monitoring. We have demonstrated the capability of ScanDrop to perform water quality monitoring via the detection of an indicator coliform bacterium, Escherichia coli, in drinking water contaminated with feces. Magnetic beads conjugated with antibodies to E. coli antigen were used to selectively capture and isolate specific bacteria from water samples. The bead-captured bacteria were co-encapsulated in pico-liter droplets with fluorescently-labeled anti-E. coli antibodies, and imaged with an automated custom designed fluorescence microscope. The entire water quality diagnostic process required 8 hours from sample collection to online-accessible results compared with 2–4 days for other currently available standard detection methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗
DOE OSTI2017

The Modern Research Data Portal: A Design Pattern for Networked, Data-Intensive Science

Here we describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance Science DMZs and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1981

Microphysical development of a pulsating cumulus tower - A case study

In-cloud microphysical data collected within a 22-minute period during seven consecutive passes at the -13 C sampling level of a deep (base +22 C) convective cloud provide observational evidence for a secondary ice production mechanism at work in the Florida environment. The observed microphysical characteristics of the convective tower, particularly the spatial distribution and habit of the ice phase relative to the updraft, are consistent with a rime-splintering hypothesis for secondary ice production. It is shown that the cloud's updraft structure is critically important in governing the timing of the ice production by controlling the flux of graupel particles through the critical temperature zone (-3 C to -8 C). The importance of the cloud's pulsation growth dynamics on the microphysics is emphasized, particularly as it relates to rapidly glaciating cumuli.

Keller, V. W.↗
NASA NTRS2002

Tropical Convection's Roles in Tropical Tropopause Cirrus

Remote sensing observations reveal the frequent occurrence of tropopause cirrus, thin cirrus layers located near the tropical cold-point tropopause. Here, we present a theory in which tropical convection plays several important roles in tropopause cirrus formation. First, tropical convection is the primary means by which the moisture required for tropopause cirrus formation is transported into the upper troposphere. However, previous studies suggest that this convection rarely penetrates to the altitudes at which tropopause cirrus layers are observed, suggesting that additional vertical moisture transport is required to explain tropopause cirrus formation. We propose a mechanism for explaining this transport in which tropical convection plays the key role. According to this hypothesis, the transport is accomplished by meridional circulations that develop within the tropopause transition layer (TTL) in response to momentum transport by Rossby waves generated by tropical convection. Results of a series of global scale model runs designed to test this hypothesis will be presented. In addition, reanalyses vertical velocity data will be examined for evidence of the expected correlation between large-scale rising motion within the TTL and tropical convection. Once moisture is present near the cold-point tropopause, large-scale cooling is required to initiate tropopause cirrus formation. One source of this cooling is stratospheric tropical waves induced by tropical convection, as we will show using a time series of radiosonde temperature data superimposed with data on cloud occurrence from the DOE ARM Nauru99 field experiment. Observations of the global characteristics of these waves from a longer time series of reanalysis data will also be presented.

Boehm, Matthew T.↗
NASA NTRS2003

The Aura Mission and Its Application to Climate and Air Quality

NASA's Aura satellite is scheduled to launch in the second quarter of 2004 into a polar orbit. The Aura mission is designed to collect data to address three high priority environmental science questions: (1) Is the ozone layer recovering as expected? (2) What are the sources and processes that control tropospheric pollutants? And (3) what is the quantitative impact of constituents on climate change? Aura will answer these questions by globally measuring a comprehensive set of trace gases and aerosols in the troposphere and stratosphere. Aura data will also have applications for monitoring and predicting climate and air quality parameters. Aura s observations will continue the TOMS ozone trend record and provide an assessment as to whether the Montreal Protocol is achieving its objective. Aura will measure gases and aerosols in the upper troposphere and lower stratosphere that contribute to climate forcing. These data will be of sufficient coverage, vertical resolution, and accuracy to help constrain climate models. In addition, Aura observations of tropospheric ozone and its precursors will have regional as well as intercontinental coverage, which could improve emission inventories. Near real time data will tested for local air quality forecasts in collaboration with the US's Environmental Protection UV-B forecasts from Aura ozone and cloud cover data. An overview of Aura s instruments, data products, validation, and examples of data applications will be presented.

Hilsenrath, Ernest↗
DOE OSTI2017

FRIEDA: Flexible Robust Intelligent Elastic Data Management Framework

Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance, cost trade-offs, complex application choices and complexity associated with elasticity, failure rates in these environments. The different data access patterns for data-intensive scientific applications require a more flexible and robust data management solution than the ones currently in existence. FRIEDA is a Flexible Robust Intelligent Elastic Data Management framework that employs a range of data management strategies in cloud environments. FRIEDA can manage storage and data lifecycle of applications in cloud environments. There are four different stages in the data management lifecycle of FRIEDA – (i) storage planning, (ii) provisioning and preparation, (iii) data placement, and (iv) execution. FRIEDA defines a data control plane and an execution plane. The data control plane defines the data partition and distribution strategy, whereas the execution plane manages the execution of the application using a master-worker paradigm. FRIEDA also provides different data management strategies, either to partition the data in real-time, or predetermine the data partitions prior to application execution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

What You Need to Know About the OMI NO2 Data Product for Air Quality Studies

The standard nitrogen dioxide (NO2) data product, produced from measurements by the Ozone Monitoring Instrument (OMI), are publicly available online from the NASA GESDISC facility. Important data fields include total and tropospheric column densities, as well as collocated data for cloud fraction and cloud top height, surface albedo and snow/ice coverage, at the resolution of the OMI instrument (12 km x 26 km, at nadir). The retrieved NO2 data have been validated, principally under clear-sky conditions. The first public-release version has been available since September 2006. An improved version of the data product, which includes a number of new data fields, and improved estimates of the retrieval uncertainties will be released by the end of 2007. This talk will describe the standard NO2 data product, including details that are essential for the use of the data for air quality studies. We will also describe the principal improvements with the new version of the data product.

Celarier, E. A.↗
NASA NTRS2014

Climate Analytics as a Service

Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.

big data↗
DOE OSTI2023

Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84°N, 24.29°E), North Slope of Alaska (NSA; 71.32°N, 156.61°W), Ny-Ålesund (Ny-Å; 78.92°N, 11.93°E), and Summit (72.59°N, 38.42°W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Cloud Height Retrieval with Oxygen A and B Bands for the Deep Space Climate Observatory (DSCOVR) Mission

Planned to fly in 2014, the Deep Space Climate Observatory (DSCOVR) would see the whole sunlit half of the Earth from the L 1 Lagrangian point and would provide simultaneous data on cloud and aerosol properties with its Earth Polychromatic Imaging Camera (EPIC). EPIC images the Earth on a 2Kx2K CCD array, which gives a horizontal resolution of about 10 km at nadir. A filter-wheel provides consecutive images in 10 spectral channels ranging from the UV to the near-IR, including the oxygen A and B bands. This paper presents a study of retrieving cloud height with EPIC's oxygen A and B bands. As the first step, we analyzed the effect of cloud optical and geometrical properties, sun-view geometry, and surface type on the cloud height determination. Second, we developed two cloud height retrieval algorithms that are based on the Mixed Lambertian-Equivalent Reflectivity (MLER) concept: one utilizes the absolute radiances at the Oxygen A and B bands and the other uses the radiance ratios between the absorption and reference channels of the two bands. Third, we applied the algorithms to the simulated EPIC data and to the data from SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) observations. Results show that oxygen A and B bands complement each other: A band is better suited for retrievals over ocean, while B band is better over vegetated land due to a much darker surface. Improvements to the MLER model, including corrections to surface contribution and photon path inside clouds, will also be discussed.

Yang, Yuekui↗
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.↗