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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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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

VARIABILITY OF WATER AND OXYGEN ABSORPTION BANDS IN THE DISK-INTEGRATED SPECTRA OF EARTH

We study the variability of major atmospheric absorption features in the disk-integrated spectra of Earth with future application to Earth-analogs in mind, concentrating on the diurnal timescale. We first analyze observations of Earth provided by the EPOXI mission, and find 5%-20% fractional variation of the absorption depths of H{sub 2}O and O{sub 2} bands, two molecules that have major signatures in the observed range. From a correlation analysis with the cloud map data from the Earth Observing Satellite (EOS), we find that their variation pattern is primarily due to the uneven cloud cover distribution. In order to account for the observed variation quantitatively, we consider a simple opaque cloud model, which assumes that the clouds totally block the spectral influence of the atmosphere below the cloud layer, equivalent to assuming that the incident light is completely scattered at the cloud top level. The model is reasonably successful, and reproduces the EPOXI data from the pixel-level EOS cloud/water vapor data. A difference in the diurnal variability patterns of H{sub 2}O and O{sub 2} bands is ascribed to the differing vertical and horizontal distribution of those molecular species in the atmosphere. On Earth, the inhomogeneous distribution of atmospheric water vapor is due to the existence of its exchange with liquid and solid phases of H{sub 2}O on the planet's surface on a timescale short compared with atmospheric mixing times. If such differences in variability patterns were detected in spectra of Earth-analogs, it would provide the information on the inhomogeneous composition of their atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2018

Earthdata User Interface (EUI) Library

As more remote sensing data moves to the cloud, the design tools we use to build user experiences and visualizations need to change and adapt to make the best use this new data reality. EUI 2.0, the next major iteration of the Earthdata User Interface Library, aims to make creating rich user experiences around NASA's Earth Observation System Data and Information System (EOSDIS) data easy and user-friendly. Building on a solid framework of design components, EUI 2.0 will be open source and have off-the-shelf integration with the Common Metadata Repository (CMR), basic mapping and visualization tools, a refreshed design toolkit for building apps and websites that fit the Earthdata design theme and guidelines. EUI 2.0 will allow for the rapid development of websites and applications based on EOSDIS tools and data holdings. It will also make use of cloud data availability to enable data visualization and analysis without the need to download and sync data.

Siarto, Jeff↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
DOE OSTI2013

Atmospheric State, Cloud Microphysics & Radiative Flux

These data contain atmospheric thermodynamics, cloud properties, radiative fluxes and radiative heating rates. The data represent a characterization of the physical state of the atmospheric column compiled on a five-minute temporal and 90m vertical grid. Sources for this information include raw measurements, cloud property and radiative retrievals, retrievals and derived variables from other third-party sources, and radiative calculations using the derived quantities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Cloud microphysical relationships and their implication on entrainment and mixing mechanism for the stratocumulus clouds measured during the VOCALS project

Cloud microphysical data obtained from G-1 aircraft flights over the southeastern pacific during the VOCALS-Rex field campaign were analyzed for evidence of entrainment mixing of dry air from above cloud top. Mixing diagram analysis was made for the horizontal flight data recorded at 1 Hz and 40 Hz. The dominant observed feature, a positive relationship between cloud droplet mean volume (V) and liquid water content (L), suggested occurrence of homogeneous mixing. On the other hand, estimation of the relevant scale parameters (i.e., transition length scale and transition scale number) consistently indicated inhomogeneous mixing. Importantly, the flight altitudes of the measurements were significantly below cloud top. We speculate that mixing of the entrained air near the cloud top may have indeed been inhomogeneous; but due to vertical circulation mixing, the correlation between V and L became positive at the measurement altitudes in mid-level of clouds, because during their descent, cloud droplets evaporate, faster in more diluted cloud parcels, leading to a positive correlation between V and L regardless of the mixing mechanism near the cloud top.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

A zero-error operational video data compression system

A data compression system has been operating since February 1972, using ATS spin-scan cloud cover data. With the launch of ITOS 3 in October 1972, this data compression system has become the only source of near-realtime very high resolution radiometer image data at the data processing facility. The VHRR image data are compressed and transmitted over a 50 kilobit per second wideband ground link. The goal of the data compression experiment was to send data quantized to six bits at twice the rate possible when no compression is used, while maintaining zero error between the transmitted and reconstructed data. All objectives of the data compression experiment were met, and thus a capability of doubling the data throughput of the system has been achieved.

Kutz, R. L.↗
NASA NTRS1984

GASP cloud encounter statistics - Implications for laminar flow control flight

The cloud observation archive from the NASA Global Atmospheric Sampling Program (GASP) is analyzed in order to derive the probability of cloud encounter at altitudes normally flown by commercial airliners, for application to a determination of the feasability of Laminar Flow Control (LFC) on long-range routes. The probability of cloud encounter is found to vary significantly with season. Several meteorological circulation features are apparent in the latitudinal distribution of cloud cover. The cloud encounter data are shown to be consistent with the classical midlatitude cyclone model with more clouds encountered in highs than in lows. Aircraft measurements of route-averaged time-in-clouds fit a gamma probability distribution model which is applied to estimate the probability of extended cloud encounter, and the associated loss of LFC effectiveness along seven high-density routes. The probability is demonstrated to be low.

Jasperson, W. H.↗
NASA NTRS1993

The OB associations of 30 Doradus in the Large Magellanic Cloud. I - Stellar observations and data reductions

UBV photometry is presented for 2400 stars in the OB associations of 30 Doradus in the Large Magellanic Cloud, and I discuss new spectroscopic observations of 54 stars including 24 O stars. The CCD fields cover an area of 50 square arcminutes in the central region. The entire catalog (exclusive of the dense core cluster R 136) is photometrically complete to V = B = 18 mag and U = 17 mag, although the completeness magnitudes are fainter for regions with less nebular contamination.

Parker, Joel W.↗
NASA NTRS2018

PACE Technical Report Series, Volume 3: Polarimetry in the PACE Mission: Science Team Consensus Document

The first goal of PACE mission science is to open new vistas in aquatic bio geochemistry by measuring non-chlorophyll pigments, separate chlorophyll and colored dissolved organic matter (CDOM) and characterize phytoplankton taxonomy. PACE science will follow aquatic biochemistry into ecosystems in coastal regions, estuaries, tidal wetlands and lakes. PACE's second science goal is to extend aerosoland cloud data-records begun by the passive EOS-era instruments, as an aerosol- cloud-climate continuation mission. Besides PACE, NASA has no plans for multi-angle radiometry to continue the MISR record nor for multi-angle polarimetry to continue the PARASOL record. A multi-angle polarimeter on PACE will reduce risk towards meeting the first goal and enable the realization of the second.

Cetinic, Ivona↗
NASA NTRS2000

Radiative Transfer and Satellite Remote Sensing of Cirrus Clouds Using FIRE-2-IFO Data

Under the support of the NASA grant, we have developed a new geometric-optics model (GOM2) for the calculation of the single-scattering and polarization properties for arbitrarily oriented hexagonal ice crystals. From comparisons with the results computed by the finite difference time domain (FDTD) method, we show that the novel geometric-optics can be applied to the computation of the extinction cross section and single-scattering albedo for ice crystals with size parameters along the minimum dimension as small as approximately 6. We demonstrate that the present model converges to the conventional ray tracing method for large size parameters and produces single-scattering results close to those computed by the FDTD method for size parameters along the minimum dimension smaller than approximately 20. We demonstrate that neither the conventional geometric optics method nor the Lorenz-Mie theory can be used to approximate the scattering, absorption, and polarization features for hexagonal ice crystals with size parameters from approximately 5 to 20. On the satellite remote sensing algorithm development and validation, we have developed a numerical scheme to identify multilayer cirrus cloud systems using AVHRR data. We have applied this scheme to the satellite data collected over the FIRE-2-IFO area during nine overpasses within seven observation dates. Determination of the threshold values used in the detection scheme are based on statistical analyses of these satellite data.

Source record↗
NASA NTRS1983

Satellite data - Analysis techniques and applications to meteorology

The history of meteorological satellites is traced, together with current instrumental and data analysis capabilities. Photographs taken from rockets launched in the late 1950s encouraged placing spacecraft such as the Nimbus I in sunsynchronous orbit beginning in 1964, and eventually the GOES, GMS, TIROS and Meteosat spacecraft into GEO in the 1970s. Visible, UV, IR and microwave sensors provide data on cloud patterns, wind fields, vertical temperature and humidity profiles and the surface temperature. Corrections have been developed in the data analyses programs to account for the atmospheric effects on upwelling radiation, fractional cloudiness, cloud-top heights and cloud contamination factors.

Szejwach, G.↗
NASA NTRSDate not supplied

An Update on the Hydrological Land Surface Data and Services at NASA GES DISC

"The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth Observing System data centers that document, process, archive and distribute data from Earth science missions and related projects. The GES DISC hosts many hydrological land surface data products including North American Land Data Assimilation System (NLDAS), Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and assimilated-Gravity Recovery and Climate Experiment (GRACE). The Land Surface Model (LSM) products contain model output, including heat fluxes, rain, snow, soil temperature, soil moisture and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at the GES DISC ranges from hourly to monthly and the spatial resolution 0.1 degree to 1.0 degree. The variability of Earth’s water and energy cycles can be characterized with these spatially and temporally consistent, and quality controlled hydrological land surface data. These data could support ongoing modeling activities, and help improve our understanding of land-surface-atmosphere interactions and their impact on climate. Over the years, new data is added, and older data is reprocessed and updated. This presentation provides a summary table of the hydrological data and discusses recent updates to the data and data services at the GES DISC. New data products include (1) GRACE Global Version 3.0 drought indicators and (2) FLDAS monthly global and Central Asia daily data. Reprocessed and updated products include (1) NLDAS Version 2.0 and (2) GRACE United States Version 4.0 drought indicators. Lastly, this presentation will discuss the opportunities for accessing some of our hydrology data from the cloud. As the hydrology land surface data is moved to the cloud, users can expect faster service for longer time series, notably the hydrology data rods time series service."

Ashley Heath↗
NASA NTRS1993

A model for the separation of cloud and aerosol in SAGE II occultation data

The Stratospheric Aerosol and Gas Experiment (SAGE) II satellite experiment measures the extinction due to aerosols and thin cloud, at wavelengths of 0.525 and 1.02 micrometers, down to an altitude of 6 km. The wavelength dependence of the extinction due to aerosols differs from that of the extinction due to cloud and is used as the basis of a model for separating these two components. The model is presented and its validation using airborne lidar data, obtained coincident with SAGE II observations, is described. This comparison shows that smaller SAGE II cloud extinction values correspond to the presence of subvisible cirrus cloud in the lidar record. Examples of aerosol and cloud data products obtained using this model to interpret SAGE II upper tropospheric and lower stratospheric data are also shown.

Kent, G. S.↗
NASA NTRSDate not supplied

The Plankton, Aerosol, Cloud, Ocean Ecosystem (Pace) Mission Overview: From Concept to Launch

Abstract for Technical Review: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robert H Estep, Jr↗
NASA NTRSDate not supplied

Mission Overview and Status from Concept to Launch

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). NASA Headquarters directed the mission development and implementation to the NASA Goddard Space Flight Center (GSFC) with guidance to utilize a Design-to-Cost (DTC) process. Mission studies were conducted to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. The PACE project office at NASA’s GSFC was responsible for the satellite development, launch and commissioning. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which includes field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The PACE observatory is comprised of three instruments, the Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone) and the PACE spacecraft. The observatory has been commissioned and is performing on-orbit science data collection operations. Summary for Program: The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Launched in February 2024 under the leadership of the NASA Goddard Space Flight Center (GSFC), the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s. The PACE observatory is comprised of the Ocean Color Instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone), along with the PACE spacecraft.

Robby Estep↗
NASA NTRS2006

Evaluating Clouds in Long-Term Cloud-Resolving Model Simulations with Observational Data

Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and compared to Atmospheric Radiation Measurement (ARM) data. This evaluation of long-term cloud-resolving model simulations focuses on the evaluation of clouds and surface fluxes. All numerical experiments, as compared to observations, simulate surface precipitation well but over-predict clouds, especially in the upper troposphere. The sensitivity of cloud properties to dimensionality and other factors is studied to isolate the origins of the over prediction of clouds. Due to the difference in buoyancy damping between 2D and 3D models, surface precipitation fluctuates rapidly with time, and spurious dehumidification occurs near the tropopause in the 2D CRM. Surface fluxes from a land data assimilation system are compared with ARM observations. They are used in place of the ARM surface fluxes to test the sensitivity of simulated clouds to surface fluxes. Summertime simulations show that surface fluxes from the assimilation system bring about a better simulation of diurnal cloud variation in the lower troposphere.

Zeng, Xiping↗
NASA NTRS2018

Earthdata Cloud Analytics Project

This presentation describes a nascent project in NASA to develop a framework to support end-user analytics of NASA's Earth science data in the cloud. The chief benefit of migrating EOSDIS (Earth Observation System Data and Information Systems) data to the cloud is to position the data next to enormous computing capacity to allow end users to process data at scale. The Earthdata Cloud Analytics project will user a service-based approach to facilitate the infusion of evolving analytics technology and the integration with non-NASA analytics or other complementary functionality at other agencies and in other nations.

data services↗
NASA NTRS2016

Construction of a Matched Global Cloud and Radiance Product from LEO/GEO and EPIC Observations to Estimate Daytime Earth Radiation Budget from DSCOVR

With the launch of the Deep Space Climate Observatory (DSCOVR), new estimates of the daytime Earth radiation budget can be computed from a combination of measurements from the two Earth-observing sensors onboard the spacecraft, the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). Although these instruments can provide accurate top-of-atmosphere (TOA) radiance measurements, they lack sufficient resolution to provide details on small-scale surface and cloud properties. Previous studies have shown that these properties have a strong influence on the anisotropy of the radiation at the TOA, and ignoring such effects can result in large TOA-flux errors. To overcome these effects, high-resolution scene identification is needed for accurate Earth radiation budget estimation. Selected radiance and cloud property data measured and derived from several low earth orbit (LEO, including NASA Terra and Aqua MODIS, NOAA AVHRR) and geosynchronous (GEO, including GOES (east and west), METEOSAT, INSAT-3D, MTSAT-2, and HIMAWARI-8) satellite imagers were collected to create hourly 5-km resolution global composites of data necessary to compute angular distribution models (ADM) for reflected shortwave (SW) and longwave (LW) radiation. The satellite data provide an independent source of radiance measurements and scene identification information necessary to construct ADMs that are used to determine the daytime Earth radiation budget. To optimize spatial matching between EPIC measurements and the high-resolution composite cloud properties, LEO/GEO retrievals within the EPIC fields of view (FOV) are convolved to the EPIC point spread function (PSF) in a similar manner to the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product. Examples of the merged LEO/GEO/EPIC product will be presented, describing the chosen radiance and cloud properties and details of how data from the multi-satellite measurements are selected.

Duda, David P.↗