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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The Advanced Topographic Laser Altimeter System (ATLAS) for NASA's Ice Cloud and Land Elevation Satellite-2 (ICESat-2): Design, Development, and On-Orbit Performance

Conceived in response to the 2007 Earth Science Decadal Survey, ATLAS/ICESat-2 was launched on September 15, 2018 to a polar orbit and has been operating continuously since. The ATLAS instrument is a continuously-operating direct-detection pulsed laser altimeter utilizing a solid-state 532-nm laser source operating at a 10-kHz pulse repetition rate along with a diffractive optic to illuminate six 15-meter spots on the Earth's surface with each laser pulse. An 80-cm receiver telescope fiber-coupled to segmented photomultipliers provides single-photon detection capability. This presentationr will present an overview of the ATLAS instrument design, some challenges encountered during development, and initial results from orbit.

Lidar↗
NASA NTRS1990

Surface radiation budget in the Clouds and the Earth's Radiant Energy System (CERES) effort and in the Global Energy and Water Cycle Experiment (GEWEX)

The surface radiation budget (SRB) and the atmospheric radiative flux divergence (ARD) are vital components of the weather and climate system. The importance of radiation in a complex international scientific endeavor, the GEWEX of the World Climate Research Programme is explained. The radiative transfer techniques and satellite instrumentation that will be used to retrieve the SRB and ARD later in this decade with the CERES are discussed; CERES is a component of the Earth Observing System satellite program. Examples of consistent SRB and ARD retrievals made with Nimbus-7 and International Satellite Cloud Climatology Project data from July 1983 are presented.

Charlock, Thomas P.↗
NASA NTRS2013

Orbiting Rainbows: Optical Manipulation of Aerosols and the Beginnings of Future Space Construction

Our objective is to investigate the conditions to manipulate and maintain the shape of an orbiting cloud of dust-like matter so that it can function as an ultra-lightweight surface with useful and adaptable electromagnetic characteristics, for instance, in the optical, RF, or microwave bands. Inspired by the light scattering and focusing properties of distributed optical assemblies in Nature, such as rainbows and aerosols, and by recent laboratory successes in optical trapping and manipulation, we propose a unique combination of space optics and autonomous robotic system technology, to enable a new vision of space system architecture with applications to ultra-lightweight space optics and, ultimately, in-situ space system fabrication. Typically, the cost of an optical system is driven by the size and mass of the primary aperture. The ideal system is a cloud of spatially disordered dust-like objects that can be optically manipulated: it is highly reconfigurable, fault-tolerant, and allows very large aperture sizes at low cost. See Figure 1 for a scenario of application of this concept. The solution that we propose is to construct an optical system in space in which the nonlinear optical properties of a cloud of micron-sized particles are shaped into a specific surface by light pressure, allowing it to form a very large and lightweight aperture of an optical system, hence reducing overall mass and cost. Other potential advantages offered by the cloud properties as optical system involve possible combination of properties (combined transmit/receive), variable focal length, combined refractive and reflective lens designs, and hyper-spectral imaging. A cloud of highly reflective particles of micron-size acting coherently in a specific electromagnetic band, just like an aerosol in suspension in the atmosphere, would reflect the Sun's light much like a rainbow. The only difference with an atmospheric or industrial aerosol is the absence of the supporting fluid medium. This new concept is based on recent understandings in the physics of optical manipulation of small particles in the laboratory and the engineering of distributed ensembles of spacecraft clouds to shape an orbiting cloud of micron-sized objects. In the same way that optical tweezers have revolutionized micro- and nano-manipulation of objects, our breakthrough concept will enable new large scale NASA mission applications and develop new technology in the areas of Astrophysical Imaging Systems and Remote Sensing because the cloud can operate as an adaptive optical imaging sensor. While achieving the feasibility of constructing one single aperture out of the cloud is the main topic of this work, it is clear that multiple orbiting aerosol lenses could also combine their power to synthesize a much larger aperture in space to enable challenging goals such as exoplanet detection. Furthermore, this effort could establish feasibility of key issues related to material properties, remote manipulation, and autonomy characteristics of cloud in orbit. There are several types of endeavors (science missions) that could be enabled by this type of approach, i.e. it can enable new astrophysical imaging systems, exoplanet search, large apertures allow for unprecedented high resolution to discern continents and important features of other planets, hyperspectral imaging, adaptive systems, spectroscopy imaging through limb, and stable optical systems from Lagrange-points. Future micro-miniaturization might hold promise of a further extension of our dust aperture concept to other more exciting smart dust concepts with other associated capabilities.

Space Construction↗
NASA NTRS1990

Plume mechanics and stratocumulus convection

The First ISCCP Regional Experiment (FIRE) marine stratocumulus Intensive Field Observation (IFO) held in July, 1987 produced a data set that is far more comprehensive than data sets from previous stratocumulus experiments. One exciting new development was the use of the 10.6 micron lidar system for cloud-top mapping that was available on the National Center for Atmospheric Research (NCAR) Electra. This system provided a unique look at the small scales of the turbulence in the clouds, images of the turbulent structures that are quantitatively revealed by conditional sampling (e.g., Khalsa and Greenhut, 1987). The behavior of these updrafts and downdrafts is central to the dynamics of the stratocumulus-topped marine boundary layer. FIRE's objectives of understanding cloud dynamics and how they affect the cloud optical depth - which, in turn, is the crucial factor in determining the clouds' albedo - therefore require the investigation of these drafts. Given here are initial results from a simple model capable of simulating moist, entraining plumes that are subject to water phase changes and radiative heating and cooling. These results correspond, therefore, to the 'dry cloud' case discussed by Lilly and Schubert (1980). The model's simplicity limits the realism of the results - the plumes are assumed not to interact, for example - but the role of radiative processes in influencing the plume dynamics is clear. Also revealed is the role the plumes play is maintaining the cloud-top inversion. The model equations and methodology used, and qualitative results are given.

Hanson, Howard P.↗
NASA NTRSDate not supplied

Transcribing Air Traffic Control System Command Center Planning Telecons Using Cloud-Based Automatic Speech Recognition

This paper addresses the challenge of using Automatic Speech Recognition (ASR) technology to transcribe regular teleconferences that happen between FAA Air Traffic Control System Command Center (ATCSCC) planners, stakeholders and air users. These planning teleconferences (aka telecons or planning webinars) are an integral part of managing air traffic in the U.S. National Airspace System (NAS). In particular, the meetings facilitate the creation and modification of various traffic management initiatives (TMIs), that are used to regulate the flow of air traffic. This is typically a human intensive process, requiring specialists to listen to the entire meeting audio (10-20 minutes duration) and inferring the state of the NAS (e.g., weather phenomenon) that was discussed. It would be advantageous to have digital transcripts of the audio and have useful information (e.g., related to TMIs) automatically extracted from the transcripts. In this regard, we are exploring the adoption of state-of-the-art speech to text and Natural Language Processing (NLP) tools that will achieve our objective of digitizing the webinar audio. Unfortunately, the highly technical phraseology present in the audio and limited data availability for model building make ASR difficult. To overcome this challenge, we have taken the critical first step in creating a human transcription dataset from ~20 hours of speech in the ATCSCC audio with the help of subject matter experts. A novelty of our work is the creation of a ground truth transcription dataset for ATCSCC teleconference webinars, which is particularly important for Aviation domain-specific NLP tasks. Using Microsoft Speech Studio, a cloud-based ASR platform, we have fine-tuned the English pre-trained ASR models (available in speech studio) and achieved an average word error rate (WER) of 6.81%. The baseline ASR also provides a digital version of each planning webinar, making it accessible and text-searchable for future references. Additionally, the transcriptions can serve as a bridge between raw audio data and a range of text-based NLP tasks, such as named entity recognition (NER) and intent classification, potentially enhancing the digital footprint of the webinars and other connected data sources. Our work has several potential applications. Firstly, the transcriptions can be analyzed to understand the complex decision process of creating, implementing and modifying TMIs and may also contribute to TMI prediction services. Secondly, our dataset and model can be used to develop more accurate ASR systems for aviation-specific language, which can bring about digital communication in the aviation industry (and aid current “voice only” communications, which are inherently error-prone). Lastly, the transcriptions themselves can be used as a valuable resource for training other NLP models.

Stephen S. B. Clarke↗
NASA NTRS1999

Large Eddy Simulation of Cirrus Clouds

The Regional Atmospheric Modeling System (RAMS) with mesoscale interactive nested-grids and a Large-Eddy Simulation (LES) version of RAMS, coupled to two-moment microphysics and a new two-stream radiative code were used to investigate the dynamic, microphysical, and radiative aspects of the November 26, 1991 cirrus event. Wu (1998) describes the results of that research in full detail and is enclosed as Appendix 1. The mesoscale nested grid simulation successfully reproduced the large scale circulation as compared to the Mesoscale Analysis and Prediction System's (MAPS) analyses and other observations. Three cloud bands which match nicely to the three cloud lines identified in an observational study (Mace et al., 1995) are predicted on Grid #2 of the nested grids, even though the mesoscale simulation predicts a larger west-east cloud width than what was observed. Large-eddy simulations (LES) were performed to study the dynamical, microphysical, and radiative processes in the 26 November 1991 FIRE 11 cirrus event. The LES model is based on the RAMS version 3b developed at Colorado State University. It includes a new radiation scheme developed by Harrington (1997) and a new subgrid scale model developed by Kosovic (1996). The LES model simulated a single cloud layer for Case 1 and a two-layer cloud structure for Case 2. The simulations demonstrated that latent heat release can play a significant role in the formation and development of cirrus clouds. For the thin cirrus in Case 1, the latent heat release was insufficient for the cirrus clouds to become positively buoyant. However, in some special cases such as Case 2, positively buoyant cells can be embedded within the cirrus layers. These cells were so active that the rising updraft induced its own pressure perturbations that affected the cloud evolution. Vertical profiles of the total radiative and latent heating rates indicated that for well developed, deep, and active cirrus clouds, radiative cooling and latent heating could be comparable in magnitude in the cloudy layer. This implies that latent heating cannot be neglected in the construction of a cirrus cloud model. The probability density function (PDF) of w was analyzed to assist in the parameterization of cloud-scale velocities in large-scale models. For the more radiatively-driven, thin cirrus case, the PDFs are approximately Gaussian. However, in the interior of the deep, convectively unstable case, the PDFs of w are multi-modal and very broad, indicating that parameterizing cloud-scale motions for such clouds can be very challenging. The results of this research are described in detail in a paper submitted to the Journal of Atmospheric Science (Wu and Cotton, 1999), which is enclosed as Appendix 2. Using soundings extracted from a mesoscale simulation of the November 26, 1991 cirrus event, the radiative effects on vapor deposition/sublimation of ice crystals was studied using a two-dimensional cloud-resolving model (CRM) version of RAMS, coupled to an explicit bin-resolving microphysics. The CRM simulations of the November 26, 1991 cirrus event demonstrate that the radiative impact on the diffusional growth (or sublimation) of ice crystals is significant. In this case, the ice particles experienced radiative warming. Model results show that radiative feedbacks in the diffusional growth of ice particles can be very complex. Radiative warming of an ice particle will restrict the particle's diffusional growth. In the case of radiative warming, ice particles larger than a certain size will experience so much radiative warming that surface ice saturation vapor pressures become large enough to cause sublimation of the larger crystals, while smaller crystals are growing by vapor deposition. However, ice mass production can be enhanced in the case of radiative cooling of an ice particle. For the November 26, 1991 cirrus event, radiative feedback results in significant reduction in the total ice mass, especially in the production of large ice crystals, and consequently, both radiative and dynamic properties of the cirrus cloud are significantly affected. A complete description of this research has been submitted as a paper to the Journal of Atmospheric Science (Wu et al., 1999), and included as Appendix 3.

Wu, Ting↗
NASA NTRS1988

Lidar observations of the fine-scale variability of marine stratocumulus clouds

An Nd:YAG lidar system was flown aboard NASA's ER-2 high altitude aircraft. Observations of cloud top height were made with 70 m along-track and 7.5 m vertical-height resolution. The lidar data observed from an East Pacific stratocumulus cloud height deck revealed large cloud variability on 1-5 km scales. The cloud deck sloped upward from 700 to 1000 m in a northeast-southwest direction over a distance of 120 km. Vertical cloud top distributions were negatively skewed indicating flat-topped clouds. The dominant spectral peak of the cloud top variations was found at 4.5 km, which is 5 to 7 times the depth of the local boundary layer. No other peaks were significant in the average spectrum. The cloud layer was stable with respect to cloud top entrainment instability. The southwestern region of the study area was more prone to shear instability at cloud top than the northeastern region. The results of this study show that a lidar system is ideal to provide the topography of clouds and local boundary layer depth. This information is useful in the study of cloud top radiation and parameterization of clouds in numerical models.

Boers, Reinout↗
NASA NTRS2021

ICESat-2 Constraint Analysis and Monitoring System (CAMS)

The Ice, Cloud and land Elevation Satellite-2 was launched on 15 September 2018 with mission goals of collecting surface elevation data that can precisely measure ice sheet topography, cloud and aerosol heights, land topography, as well as vegetation height for ecosystems studies. The Constraint Analysis and Monitoring System (CAMS) was implemented as an ICESat-2 ground system element to perform Mission Planning and Spacecraft Safety Monitoring. Mission Planning requires CAMS to serve as an interface between the Project Science Office, the Instrument Support Facility and the Mission Operation Control Center. By ingesting inputs received from all three groups, the CAMS builds an optimized and deconflicted timeline of science and instrument activities. Based upon the timeline of activities, the CAMS utilizes a sophisticated set of algorithms to model and predict the location and pointing of the spacecraft instrument relative to the Earth and Sun. From the predicted position and pointing, the CAMS provides precise monitoring of instrument health, and performs space asset laser conjunction detection. Furthermore, the CAMS determines alternative plans to prevent detected health constraint violations or mitigate potential laser conjunctions. This paper provides an overview of the CAMS and presents the operational performance of planning science and instrument activities, the monitoring of instrument constraints for health and safety, and the space asset laser conjunction screening and mitigation process.

ICESat-2↗
DOE OSTI2026

Atmospheric System Research Workshop Report: New Directions in Atmospheric Ice Processes Research

Atmospheric ice processes are critical for precipitation production, cloud dynamics, and radiative properties and contribute to uncertainties in Earth’s energy budget and hydrological cycle, yet they remain poorly understood. To address this, ASR convened a 2.5-day workshop with 28 experts in laboratory measurements, field observations, and cloud modeling. The primary goal was to identify key knowledge gaps and prioritize future directions in atmospheric ice processes research. The outcomes of this workshop are expected to inform ASR and prompt improvements in cloud and Earth system models (ESMs) by advancing the fundamental understanding of ice processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

A Convolutional Neural Network for Removing GOES-17 Image Anomalies to Improve CERES Broadband Flux Measurement

Background - CERES provides satellite-based global climate data record of Earth's radiation budget and clouds - CERES = Clouds and the Earth's Radiant Energy System - Measurement anomalies impact cloud retrieval - Incorrect Cloud Phase = Incorrect Flux - Unmitigated bad scanlines will impact climate data records - GOES-17 ABI cooling system anomaly = many bad scanlines at night (~10:30 - 16:30 UTC) - Cleaning imagery of bad scanlines is laborious but necessary - A convolution neural network (CNN) can identify and clean bad scanlines as effectively as a human

Benjamin Scarino↗
NASA NTRS1993

Gas and dust in the pre-main-sequence multiple system GG Tauri

We present 1.4 and 2.7 mm aperture synthesis maps of the gas and dust continuum emission around GG Tauri, a very young component of a premain-sequence multiple star system; both GG Tau and its apparent companion, GG Tau/c, at 1500 AU separation, are themselves binaries. At 1.4 mm, dust continuum emission of about 750 AU in extent is associated with GG Tau, and a secondary peak is near GG Tau/c. Spectral line images reveal gaseous structure around GG Tau, elongated along the GG Tau-GG Tau/c axis. There is some suggestion that the gas associated with GG Tau/c alone is extended in a different direction. Marked changes in the morphology and velocity structure of the molecular emission near GG Tau/c also indicate that this system is differently oriented. Clumps between the two systems may be vestiges of a connecting bar. GG Tau and GG Tau/c appear to have originated in a common cloud; their different systemic orientations suggest that they formed from an initially prolate cloud rather than from an extensive and highly flattened disk.

Koerner, D. W.↗
NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗
NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗