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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Abstract. Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

VISSTPX2DRECTG16V4MINNIS (c1)

Satellite-based retrievals of cloud and radiation properties are available in this value-added product provided by Bill Smith’s group at NASA/Langley using the VISST (Visible Infrared Solar-Infrared Split Window Technique) algorithm. This product contains VISST-derived pixel-level products from the GOES-16 satellite, rectilinear projection 2-D array.

broadband LW flux↗
NASA NTRS1984

Natural laminar flow experiments on modern airplane surfaces

Flight and wind-tunnel natural laminar flow experiments have been conducted on various lifting and nonlifting surfaces of several airplanes at unit Reynolds numbers between 0.63 x 10 to the 6th power/ft and 3.08 x 10 to the 6th power/ft, at Mach numbers from 0.1 to 0.7, and at lifting surface leading-edge sweep angles from 0 deg to 63 deg. The airplanes tested were selected to provide relatively stiff skin conditions, free from significant roughness and waviness, on smooth modern production-type airframes. The observed transition locations typically occurred downstream of the measured or calculated pressure peak locations for the test conditions involved. No discernible effects on transition due to surface waviness were observed on any of the surfaces tested. None of the measured heights of surface waviness exceeded the empirically predicted allowable surface waviness. Experimental results consistent with spanwise contamination criteria were observed. Large changes in flight-measured performance and stability and control resulted from loss of laminar flow by forced transition. Rain effects on the laminar boundary layer caused stick-fixed nose-down pitch-trim changes in two of the airplanes tested. No effect on transition was observed for flight through low-altitude liquid-phase clouds. These observations indicate the importance of fixed-transition tests as a standard flight testing procedure for modern smooth airframes.

Holmes, B. J.↗
NASA NTRS1989

Molecular and mass spectroscopic analysis of isotopically labeled organic residues

Experimental studies aimed at understanding the evolution of complex organic molecules on interstellar grains were performed. The photolysis of frozen gas mixtures of various compositions containing H2O, CO, NH3, and CH4 was studied. These species were chosen because of their astrophysical importance as deducted from observational as well as theoretical studies of ice mantles on interstellar grains. These ultraviolet photolyzed ices were warmed up in order to produce refractory organic molecules like the ones formed in molecular clouds when the icy mantles are being irradiated and warmed up either by a nearby stellar source or impulsive heating. The laboratory studies give estimates of the efficiency of production of such organic material under interstellar conditions. It is shown that the gradual carbonization of organic mantles in the diffuse cloud phase leads to higher and higher visual absorptivity - yellow residues become brown in the laboratory. The obtained results can be applied to explaining the organic components of comets and their relevance to the origin of life.

Mendoza-Gomez, Celia X.↗
NASA NTRS1999

Advanced Geosynchronous Imager

For improved understanding of chaotic processes and the diurnal cycle, an advanced GOES imager must also have the multi-spectral spectral bands used by low earth orbit (LEO) imagers, with on-orbit calibration for all bands. A synergy between GEO and LEO radiometry would enable earth system scientists to fuse the remote sensing data from all the spaceborne platforms. These additional radiometric capabilities are designed to observe important physical processes that vary rapidly and unpredicably: smoke, fires, precipitation, ozone, volcanic ash, cloud phase and height, and surface temperature. We believe the technology now exists to develop an imaging system that can meet future weather reporting and earth system science needs. To meet this need, we propose a design for a comprehensive geosynchronous atmospheric imager. This imager is envisioned to fly on a GOES-N class spacecraft, within the volume, weight and power constraints of a platform similar to GOES-N while delivering 100 times more data and radiometric quality than the GOES-N imager. The higher data rate probably requires its own ground station, which could serve as a systems prototype for NOAA's next generation of operational satellites. For operational compatibility, our proposed advanced GOES imaging system contains the GOES-R requirements as a subset, and the GOES-N imager capabilities (and the sounder's imaging channels) as a further subset.

Chesters, Dennis↗
NASA NTRS2000

Recent Advances in Interstellar Astrochemistry

This presentation will address the possibility that molecules created in the interstellar medium may play a role in the origin and evolution of life on planetary surfaces. The argument will be presented in three main parts. First, I will review the various organic and volatile compounds that are now known or suspected to exist in a variety of circumstellar and interstellar environments. This information is largely the result of the combined applications of observational infrared and radio spectroscopy, laboratory astrophysical simulations, and theoretical astrochemistry. Second, I will devote additional attention to those materials that exhibit chemical properties that are of biological interest. Finally, I will discuss the evidence, largely gathered from the laboratory study of extraterrestrial materials (meteorites and cosmic dust), that interstellar materials, including organics, can survive the transition from the dense cloud phase into forming stellar systems. Once there, some of this material can be delivered largely unaltered to planetary surfaces where it can play key roles in the origin and subsequent evolution of life. Thus, the guiding principle of this presentation will be to attempt to 'put the astro into astrobiology.'

Sandford, Scott A.↗
NASA NTRS2004

Comparison of Satellite and Aircraft Measurements of Cloud Microphysical Properties in Icing Conditions During ATREC/AIRS-II

Satellites are ideal for continuous monitoring of aircraft icing conditions in many situations over extensive areas. The satellite imager data are used to diagnose a number of cloud properties that can be used to develop icing intensity indices. Developing and validating these indices requires comparison with objective "cloud truth" data in addition to conventional pilot reports (PIREPS) of icing conditions. Minnis et al. examined the relationships between PIREPS icing and satellite-derived cloud properties. The Atlantic-THORPEX Regional Campaign (ATReC) and the second Alliance Icing Research Study (AIRS-II) field programs were conducted over the northeastern USA and southeastern Canada during late 2003 and early 2004. The aircraft and surface measurements are concerned primarily with the icing characteristics of clouds and, thus, are ideal for providing some validation information for the satellite remote sensing product. This paper starts the process of comparing cloud properties and icing indices derived from the Geostationary Operational Environmental Satellite (GOES) with the aircraft in situ measurements of several cloud properties during campaigns and some of the The comparisons include cloud phase, particle size, icing intensity, base and top altitudes, temperatures, and liquid water path. The results of this study are crucial for developing a more reliable and objective icing product from satellite data. This icing product, currently being derived from GOES data over the USA, is an important complement to more conventional products based on forecasts, and PIREPS.

Nguyen, Louis↗
NASA NTRS2014

The Atmospheric Infrared Sounder Version 6 Cloud Products

The version 6 cloud products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) instrument suite are described. The cloud top temperature, pressure, and height and effective cloud fraction are now reported at the AIRS field-of-view (FOV) resolution. Significant improvements in cloud height assignment over version 5 are shown with FOV-scale comparisons to cloud vertical structure observed by the CloudSat 94 GHz radar and the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). Cloud thermodynamic phase (ice, liquid, and unknown phase), ice cloud effective diameter D(sub e), and ice cloud optical thickness (τ) are derived using an optimal estimation methodology for AIRS FOVs, and global distributions for 2007 are presented. The largest values of tau are found in the storm tracks and near convection in the tropics, while D(sub e) is largest on the equatorial side of the midlatitude storm tracks in both hemispheres, and lowest in tropical thin cirrus and the winter polar atmosphere. Over the Maritime Continent the diurnal variability of tau is significantly larger than for the total cloud fraction, ice cloud frequency, and D(sub e), and is anchored to the island archipelago morphology. Important differences are described between northern and southern hemispheric midlatitude cyclones using storm center composites. The infrared-based cloud retrievals of AIRS provide unique, decadal-scale and global observations of clouds over portions of the diurnal and annual cycles, and capture variability within the mesoscale and synoptic scales at all latitudes.

infrared instruments↗
NASA NTRS2015

Dome Degradation Characterization of Wide-Field-of-View Nonscanner Aboard ERBE and Its Reprocessing

Earth Radiation Budget Experiment (ERBE) wide-field-of-view (WFOV) nonscanners aboard ERBS and NOAA- 9/NOAA-10 provided broadband shortwave and longwave irradiances from 1985 to 1999. The previous analysis showed dome degradation in the shortwave nonscanner instruments. The correction was performed with a constant spectral (gray assumption) degradation. We suspect that the gray assumption affected daytime longwave irradiance and led to a day-minus-night longwave flux differences (little change in night time longwave) increase over time. Based on knowledge from the CERES process, we will reprocess entire ERBE nonscanner radiation dataset by characterizing shortwave dome transmissivity with spectral dependent degradation using the solar data observed by these instruments. Once spectral dependent degradation is derived, imager derived cloud fraction and the cloud phase as well as surface type over the FOV of nonscanner instruments will be used to model unfiltering coefficients. This poster primarily explains the reprocessing techniques and includes initial comparison of several months of data processed with existing and our recent methods.

Shrestha, Alok K.↗
NASA NTRS2015

Tropical and Arctic Cloud Observations From CALIPSO Between 2008-2014 and its Implications

There has been studies (e.g., Schweiger, 2004) suggest significant increase of Arctic clouds during the last three decades, especially in the western Arctic region. Such studies are based on passive remote sensing that are not highly reliable due to the lack of contrasts in temperature and reflectance between clouds and snow/ice surfaces. Changes in the Arctic clouds can be evaluated more accurately using the space-based lidar measurements from CALIPSO during the last nine years since CALIPSO can provide much more accurate detection and classification of both water and ice clouds in the Arctic. Time series of Arctic cloud properties (e.g., cloud fraction, cloud thermodynamic phase, water cloud depolarization ratio and droplet number concentration) from CALIPSO data are analyzed in this study. This study reveals the changes in both cloud fraction and cloud microphysical properties during the last nine years when CALIPSO data are available. We will evaluate the changes in Arctic cloud fraction and cloud microphysical properties, their seasonal and spatial characteristics and the potential impact on the energy budget and the climate of the Arctic.

Hu, Yongxiang↗
NASA NTRS2016

A New, Low Braking Index for the LMC Pulsar B0540-69

We report the results of a 16-month monitoring campaign using the Swift satellite of PSR 0540, a young pulsar in the Large Magellanic Cloud. Phase connection was maintained throughout the campaign so that a reliable ephemeris could be determined, and the length of the campaign is adequate to accurately determine the spin frequency and its first and second derivatives. The braking index n is 0.031 +/- 0.013 (90% confidence), a value much lower than previously reported for 0 540 and almost all other young pulsars. We use data from the extensive monitoring campaign with RXTE to showt hat timing noise is unlikely to significantly affect the measurement. This is the first measurement of the braking index in the pulsars recently discovered high spin-down state. We discuss possible mechanisms for producing the low braking index.

pulsars individual (PSR B0540−69)↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗
NASA NTRS2017

How the Emitted Size Distribution and Mixing State of Feldspar Affect Ice Nucleating Particles in a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in climate prediction. We apply an improved dust mineral specific aerosol module in the NASA GISS Earth System ModelE, which takes into account soil aggregates and their fragmentation at emission as well as the emission of large particles. We calculate ice nucleating particle concentrations from K-feldspar abundance for an active site parameterization for a range of activation temperatures and external and internal mixing assumption. We find that the globally averaged INP concentration is reduced by a factor of two to three, compared to a simple assumption on the size distribution of emitted dust minerals. The decrease can amount to a factor of five in some geographical regions. The results vary little between external and internal mixing and different activation temperatures, except for the coldest temperatures. In the sectional size distribution, the size range 24 micrometer contributes the largest INP number.

Perlwitz, Jan P.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29931-DND

The ACE science working group satellite concept calls for a combination of high spectral resolution aerosol lidar and a polarimeter able to determine vertical profiles of aerosol extinction and clouds. Here we discuss why an HSRL lidar is required to achieve these requirements. The primary advantage of an HSRL over an elastic backscatter lidar is that the HSRL channel allows for the independent retrieval of vertically resolved aerosol extinction without constraint. Not requiring a constraint means greater availability of retrievals in polar regions, on the night side, and in broken cloud situations, plus greater accuracy particularly in the lowest part of the atmosphere and in scenes with aerosol heterogeneity. Advantages go beyond the extinction products. Unlike attenuated backscatter, which is an integrated quantity, the aerosol backscatter coefficient depends only on the aerosol at a specific level in the atmosphere, making it preferable for use in quantitative applications. The aerosol backscatter coefficient is more accurate from an HSRL system compared to the retrieval from a single-channel system. This is because errors in both extinction and backscatter from a single-channel system increase with decreasing altitude, due to the need to accurately characterize the overlying attenuation. This means that an HSRL will also have more valid data in and near the boundary layer where aerosol is of primary interest for air quality and weather applications and for many climate objectives including aerosol-cloud interaction studies. Even cloud properties that do not directly use the HSRL capability (cloud phase and thin cirrus retrievals) benefit from the more accurate calibration of an HSRL system. The improved accuracy of backscatter and extinction also lead to better capabilities for aerosol typing, improved retrievals of aerosol radiative effect, and increased information content for combined lidar + polarimeter retrievals of profiles of microphysical properties such as vertically resolved effective radius.

S P Burton↗
NASA NTRSDate not supplied

Comparison of INP Parameterizations for Dust Minerals in Climatological Simulations With a Global Model

The effect of aerosol particles on ice nucleation and, in turn, the formation of ice and mixed phase clouds is recognized as one of the largest sources of uncertainty in weather and climate prediction. We utilize an improved sectional dust module in NASA GISS Earth System ModelE2.1, which distinguishes eight different mineral species and accretions between iron oxides and the other minerals. Simulations over a period of 20 years have been carried out with this model, and the mineral fields and other model variables (temperature, relative humidity) are used to calculate the ice nucleating particle (INP) number concentration, applying time-independent and time-dependent INP parameterizations, such as active site parameterization and water activity based immersion freezing model (ABIFM). We study how the dependence of the parameterizations on different model variables affects the mean INP number concentration. The sensitivity of the INP number concentration to fundamental dust properties such as emitted mineral size distributions and mixing state between minerals is also investigated. Results show that the sensitivity of the total INP number concentration to the emitted dust size distribution is rather small, but the sensitivity over the whole size range obscures offsetting differences in the magnitude and the sign of the sensitivity between smaller and larger particles.

INP Parameterizations↗
NASA NTRSDate not supplied

Towards a more realistic representation of NASA CERES-derived surface radiative fluxes during polar night: a comparison with the MOSAiC field campaign

The Arctic is one of the most sensitive regions of Earth to climate change, and yet remains one of the more difficult regions to both observe and simulate. The high latitude of the Arctic gives it some unusual features compared with much of Earth, such as large and variable sea ice cover, extensive mixed phase cloud cover, and the polar day/night cycle. These features significantly influence the radiative environment of the Arctic as well as the ability to observe it. The remoteness of the Arctic from most of civilization makes large volumes of in situ observations difficult to collect, and so satellite observations are a critical tool for observing the region, such as those from Clouds and the Earth’s Radiant Energy System (CERES). But validating the satellite radiative flux estimates is difficult because of the lack of in situ measurements. A useful remedy is utilizing measurements from field campaigns, such as the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition during 2019-2020. The extensive high-quality surface radiative flux and meteorological measurements collected from MOSAiC provide a useful check on flux retrievals from CERES instruments. Previous work compared MOSAiC and CERES surface radiative fluxes during the 2020 melt season (April-September). Errors in CERES fluxes were related to errors in estimated atmospheric optical depth and surface albedo. We conduct a similar assessment of CERES surface flux retrievals using MOSAiC data, but for the months of October through March instead. As this time period is mostly during polar night and twilight, the focus of the study is on longwave surface fluxes. To better understand the reasons for errors in CERES fluxes, we use the large set of meteorological measurements also collected by MOSAiC, including surface temperature, thermodynamic vertical profiles, surface turbulent fluxes, and cloud properties. For example, one notable possible source of error in the CERES estimate of surface downwelling longwave flux is the estimate of cloud base height when compared with MOSAiC W-band radar measurements – an overestimate of cloud base height yields an underestimate of the downwelling flux on the order of tens of W m2.

J. Brant Dodson↗
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↗