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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

TROPoe retrievals from SGP C1

The TROPoe (formerly called AERIoe) algorithm (Turner and Löhnert 2014, 2021; Turner and Blumberg 2019) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. This data set was collected at the ARM SGP Central Facility (CF) during and after the MicroPulse DIAL Demonstration (MPDDEMO) field campaign. The TROPoe retrieval was run at 5-minute resolution, but retrievals can be performed at the maximum temporal resolution of the AERI instrument (order 30 s) -- if this higher temporal resolution is desired, please contact Dave Turner. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral infrared radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of radiosonde profiles collected at the ARM SGP site during the summer as our prior information in an optimal estimation framework. Surface meteorology and output from the rapid-refresh (RAP) numerical weather prediction model (above 4 km) were used as additional constraints by the retrieval. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution). There is an overall "qc_flag" field that is set when a retrieval should not be trusted. However, the logic that sets this flag did not work correctly, and it should not be used. Instead, consider all retrievals that have a value for "converged_flag" greater than 0 as valid. If you have any questions, contact Dave Turner. This is Release_2_12 of the TROPoe algorithm.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Aerosol Layer Height With Enhanced Spectral Coverage Achieved by Synergy Between VIIRS and OMPS-NM Measurements

This study presents a near production-ready algorithm to retrieve the height of biomass burning smoke and mineral dust aerosols as part of NASA’s Deep Blue aerosol data product suite. It utilizes the enhanced spectral coverage achieved by using co-located data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM), both aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. Specifically, the 412 nm top-of-atmosphere reflectance from VIIRS and the ultraviolet aerosol index from OMPS-NM are used to determine the height and single-scattering albedo of the absorbing aerosols simultaneously. Constraints on aerosol optical depth at 550 nm and surface reflectance for the 412 nm band are provided by the operational VIIRS Deep Blue aerosol product. Wildfire smoke layer heights obtained from the algorithm over North America, where smoke plumes often stretched thousands of kilometers, are shown to agree with those from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, with an uncertainty generally within 1-1.5 km. This new height data set will be included in the upcoming Version 2 VIIRS Deep Blue aerosol product.

Aerosol↗
NASA NTRS2020

Aerosol Layer Height with Enhanced Spectral Coverage Achieved by Synergy between VIIRS and OMPS-NM Measurements

This letter presents a near production-ready algorithm to retrieve the height of biomass burning smoke and mineral dust aerosols as part of National Aeronautics and Space Administration (NASA)’s Deep Blue aerosol data product suite. It utilizes the enhanced spectral coverage achieved by using colocated data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM), both aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. In particular, the 412-nm top-of-atmosphere (TOA) reflectance from VIIRS and the ultraviolet aerosol index from OMPS-NM are used to determine the height and single-scattering albedo of the absorbing aerosols simultaneously. Constraints on aerosol optical depth at 550 nm and surface reflectance for the 412-nm band are provided by the operational VIIRS Deep Blue aerosol product. Wildfire smoke layer heights obtained from the algorithm over North America, where smoke plumes often stretched thousands of kilometers, are shown to agree with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, with an uncertainty generally within 1–1.5 km. This new height data set will be included in the upcoming Version 2 VIIRS Deep Blue aerosol product.

aerosol↗
NASA NTRS1996

Retrieval of Bidirectional Reflectance Factors and Directional-Hemispherical Reflectances Using Space-Based and Airborne Multi-Angle Observations

MISR is scheduled for launch in 1998 on the EOS AM1 platform. The algorithms needed to analyze data from this instrument are currently being tested, using both simulated data and data currently available from the airborne ASAS multi-angle instrument...Results of the retrieval algorithms are presented using simulated MISR data.

MISR EOS algorithms retrieval algorithms↗
NASA NTRS2010

Direct Retrieval of Sulfur Dioxide Amount and Altitude from Spaceborne Hyperspectral UV Measurements: Theory and Application

We describe the physical processes by which a vertically localized absorber perturbs the top-of-atmosphere solar backscattered ultraviolet (UV) radiance. The distinct spectral responses to perturbations of an absorber in its column amount and layer altitude provide the basis for a practical satellite retrieval technique, the Extended Iterative Spectral Fitting (EISF) algorithm, for the simultaneous retrieval of these quantities of a SO2 plume. In addition, the EISF retrieval provides an improved UV aerosol index for quantifying the spectral contrast of apparent scene reflectance at the bottom of atmosphere bounded by the surface and/or cloud; hence it can be used for detection of the presence or absence of UV absorbing aerosols. We study the performance and characterize the uncertainties of the EISF algorithm using synthetic backscattered UV radiances, retrievals from which can be compared with those used in the simulation. Our findings indicate that the presence of aerosols (both absorbing and nonabsorbing) does not cause large errors in EISF retrievals under most observing conditions when they are located below the SO2 plume. The EISF retrievals assuming a homogeneous field of view can provide accurate column amounts for inhomogeneous scenes, but they always underestimate the plume altitudes. The EISF algorithm reduces systematic errors present in existing linear retrieval algorithms that use prescribed SO2 plume heights. Applying the EISF algorithm to Ozone Monitoring Instrument satellite observations of the recent Kasatochi volcanic eruption, we demonstrate the successful retrieval of effective plume altitude of volcanic SO2, and we also show the improvement in accuracy in the corresponding SO2 columns.

Yang, Kau↗
NASA NTRS1991

Remote sensing of earth terrain

In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach.

Yueh, Herng-Aung↗
NASA NTRS2018

Polarimetric Remote Sensing of Atmospheric Aerosols: Instruments, Methodologies, Results, and Perspectives

Polarimetry is one of the most promising types of remote sensing for improved characterization of atmospheric aerosol. Indeed, aerosol particles constitute a highly variable atmospheric component characterized by a large number of parameters describing particle sizes, morphologies (including shape and internal structure), absorption and scattering properties, amounts, horizontal and vertical distribution, etc. Reliable monitoring of all these parameters is very challenging, and therefore the aerosol effects on climate and environment are considered to be among the most uncertain factors in climate and environmental research. In this regard, observations that provide both the angular distribution of the scattered atmospheric radiation as well as its polarization state at multiple wavelengths covering the UV–SWIR spectral range carry substantial implicit information on the atmospheric composition. Therefore, high expectations in improving aerosol characterization are associated with detailed passive photopolarimetric observations. The critical need to use space-borne polarimetry for global accurate monitoring of detailed aerosol properties was first articulated in the late 1980s and early 1990s. By now, several orbital instruments have already provided polarization observations from space, and a number of advanced missions are scheduled for launch in the coming years by international and national space agencies. The first and most extensive record of polarimetric imagery was provided by POLDER-I, POLDER-II, and POLDER/PARASOL multi-angle multi-spectral polarization sensors. Polarimetric observations with the POLDER-like design intended for collecting extensive multi-angular multi-spectral measurements will be provided by several instruments, such as the MAI/TG-2, CAPI/TanSat, and DPC/GF-5 sensors recently launched by the Chinese Space Agency. Instruments such as the 3MI/MetOp-SG, MAIA, SpexOne and HARP2 on PACE, POSP, SMAC, PCF, DPC–Lidar, ScanPol and MSIP/Aerosol-UA, MAP/Copernicus CO2 Monitoring, etc. are planned to be launched by different space agencies in the coming decade. The concepts of these future instruments, their technical designs, and the accompanying algorithm development have been tested intensively and analyzed using diverse airborne prototypes. Certain polarimetric capabilities have also been implemented in such satellite sensors as GOME-2/MetOp and SGLI/GCOM-C.

Aerosols↗
NASA NTRS2018

The Next-Generation Goddard Convective-Stratiform Heating Algorithm: New Tropical and Warm-Season Retrievals for GPM

The Goddard convective-stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm's lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

Lang, Stephen E.↗
DOE OSTI2018

Retrievals of Arctic Sea-Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

We estimate the uncertainty of satellite-retrieved Arctic sea-ice thickness, sea-ice volume, and their trends stemming from the lack of reliable snow-thickness observations. To illustrate, we simulate a Cryosat2-type ice-thickness retrieval in an ocean-model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea-ice thickness using different snow climatologies and compare the resulting sea-ice thickness retrievals to each other and to the real sea-ice thickness of the reanalysis-forced simulation. We postulate that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. Moreover, we show that Arctic ice-volume trends derived from ice-thickness retrievals using any snow-depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Abstract Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Recovered supernova Ia rate from simulated LSST images

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg 2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Astronomy & Astrophysics↗
DOE OSTI2021

The Patterson function as auto-hologram and graph enables the direct solution to the phase problem for coherently illuminated atomistic structures

Abstract The coherent Patterson function, derived from the coherent scattering intensities from N atoms, can be described as an auto-holographic image, i.e., the superposition of N -holographic images in which each atom serves as the source of a different reference wave, and as a mathematical graph of the unknown structure. These insights indicate that the unknown structure is significantly over-determined by the inherent information content in a Patterson function by a factor of ( N − 1)/2 (for non-degenerate structures), independent of dimensionality. However, we also show that the ability to resolve the distinct features in the Patterson function depends strongly on details of the experimental design (data range, sampling frequency, and dimensionality). This re-interpretation suggests that the coherent Patterson function provides a natural context for describing the information content in coherent scattering, reveals that there is no inherent phase problem for coherently illuminated pointwise structures, and enables the design of an algorithm which retrieves the structure directly from the Patterson function, without using error metric minimization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Shapes and Fall Orientations of Ice Particle Aggregates

Testing the often-made assumption that ice particle aggregates (snowflakes) are well represented by oblate spheroids, ellipsoid fits are applied to aggregate images. An algorithm to retrieve both the ellipsoidal parameters and the orientations of the fitted ellipsoids is applied to Multi-Angle Snowflake Camera measurements of ice particle aggregates observed in Alaska. The resulting ellipsoids have shapes closer to prolate spheroids than the oft-assumed oblate spheroids. A robust linear relationship exists between the two characteristic aspect ratios of the ellipsoids. The most probable orientation of the maximum dimension of the retrieved ellipsoids is not in the horizontal plane, and the rotational angles of the maximum dimensions in the horizontal plane are not uniform, but instead display some correlation with the wind direction at the times of the measurements. The retrieval results can be used to improve the representation of aggregates in microphysics and/or electromagnetic radiation scattering models applicable to radar and satellite measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Final Technical Report for DE-SC0008502

This work led to the development of an algorithm to retrieve cloud microphysical properties (optical depth and effective particle size) form shortwave spectral radiometric irradiance measured at the earth surface in the wavelength interval 1000-1600 nm. Recent instruments such as the ARM Program’s Shortwave Array Spectrometer (SAS) can record irradiance spectra in cadences as short as one minute, providing valuable information about time evolution of cloud properties involved with formation, precipitation and dissipation. A primary advantage of shortwave spectral measurements over the middle-infrared is that cloud properties can be retrieved over a much larger range in liquid water path (LWP), as the instrument is measuring irradiance transmitted through cloud. In the middle infrared, a cloud behaves as a blackbody for optical depths larger than approximately five, and for larger LWP surface longwave spectral measurements are insensitive to cloud microphysics. Research supported by this award has demonstrated the viability of the retrieval technique and the utility of high time resolution spectral measurements (Lubin et al. 2018a; Wilson et al. 2018).

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

TRACER CLAMPS1 Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline XR Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project. For the TRACER campaign, the CLAMPS1 Doppler lidar collected PPI scans at 70 deg elevation every 20 minutes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER CLAMPS2 Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER SPARC Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the SPARC Halo Streamline XR Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

AWAKEN CLAMPS1 Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the AWAKEN project.

54 ENVIRONMENTAL SCIENCES↗