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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Remote sensing of precipitation structures using combined microwave radar and radiometric techniques

After reviewing published multiparameter radar observations and their interpretation for microphysical retrieval, the paper presents radiative transfer model simulations performed using radar measurements and a cloud model. The qualitative and quantitative aspects of the remote sensing of precipitation are discussed with particular attention given to the potential of estimating scattering-based ice water path.

Vivekanandan, J.↗
DOE OSTI2024

Estimation of wintertime cloud radiative effects in the Western Arctic, a function of cloud-moisture-coupling and sea ice conditions

Cloud radiative effects are estimated using up- and downward ground-based observations of broadband thermal infrared sensors in cloudy conditions which are provided by the ARM data repository. The ARM Surface Radiation measurement is analyzed. To study the cloud radiative effects, the observed thermal infrared measurements under cloudy conditions are analyzed together with microphysical cloud properties like liquid and ice water path obtained from the CloudNet algorithms applied to the remote-sensing instrumentation suite at the ARM site in Utqiagvik. Cloud observations have been sorted depending on the coupling of the cloud with upwind water vapor transport events that can interact with sea ice upwind. Statistics of surface net thermal fluxes and cloud water/ice properties show a distinguished distribution for coupled and decoupled clouds. It was found that coupled clouds contain more liquid water and are more efficient reduce the cooling of surface LW radiation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

The cloud bright spot.

A cloud effect, called 'cloud bright spot,' is presented in terms of field measurements, theory, and significance to remote sensing. Field measurements of global solar radiation show that clouds can cause ground bright spots having a 30% higher incident solar radiation flux than surrounding areas. The possible cause and recommendations concerning ground truth measurements and instrumentation are also presented.

Hulstrom, R. L.↗
NASA NTRS1998

Observations of the Spectral Distribution of Solar Irradiance at the Ground During SUCCESS

During SUCCESS two independent and independently calibrated spectrometers were deployed at a surface site to determine the spectrally resolved downwelling solar irradiance. Preliminary evidence shows that for cloud-free conditions there is general agreement between measurements and model derived irradiance, at least to within instrumental and model uncertainties of 5%, with greatest disagreement Occurring in the 400 nm - 700 nm band. Cirrus effects are most prominent in the near-infrared bands beyond 1500 nm while the influence of very thin (subvisual) cirrus is evident in the scattered flux at the surface. Further remote sensing applications and the assessment of cloud absorption is contingent upon measurements of the surface spectral albedo.

Pilewskie, Peter↗
NASA NTRS2022

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D(e), that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D(e) has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D(e) as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds.

Ice Cloud↗
NASA NTRS2002

Seasonal and Global Variations of Water Vapor and High Clouds Observed with MODIS near-IR Channels

The NASA Moderate Resolution Imaging Spectrometer (MODIS) on the Terra Spacecraft has been collecting scientific data since February of 2000. MODIS is a major facility instrument for remote sensing of the atmosphere, land surfaces, and ocean color. On the MODIS instruments, there are five channels located within and around the .0.94 micron water vapor band absorption region for remote sensing of atmospheric water vapor. There is also a channel located at 1.375 micron for detecting thin cirrus clouds. We will describe the basic principles for using these near-IR channels for remote sensing of water vapor and high clouds. Based on our analysis of two years# measurements with these channels, we have found that reliable observations of water vapor and high clouds on regional and global scales can be made. We will present results on daily, seasonal and annual variations of water vapor and high clouds.

Gao, Bo-Cai↗
NASA NTRS1996

Cloud Properties Derived from Surface-Based Near-Infrared Spectral Transmission

Surface based near-infrared cloud spectral transmission measurements from a recent precipitation/cloud physics field study are used to determine cloud physical properties and relate them to other remote sensing and in situ measurements. Asymptotic formulae provide an effective means of closely approximating the qualitative and quantitative behavior of transmission computed by more laborious detailed methods. Relationships derived from asymptotic formulae are applied to measured transmission spectra to test objectively the internal consistency of data sets acquired during the field program and they confirmed the quality of the measurements. These relationships appear to be very useful in themselves, not merely as a quality control measure, but also a potentially valuable remote-sensing technique in its own right. Additional benefits from this analysis have been the separation of condensed water (cloud) transmission and water vapor transmission and the development of a method to derive cloud liquid water content.

Pilewskie, Peter↗
NASA NTRS2009

Cold Season QPF: Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

As operational numerical weather prediction is performed at increasingly finer spatial resolution, precipitation traditionally represented by sub-grid scale parameterization schemes is now being calculated explicitly through the use of single- or multi-moment, bulk water microphysics schemes. As computational resources grow, the real-time application of these schemes is becoming available to a broader audience, ranging from national meteorological centers to their component forecast offices. A need for improved quantitative precipitation forecasts has been highlighted by the United States Weather Research Program, which advised that gains in forecasting skill will draw upon improved simulations of clouds and cloud microphysical processes. Investments in space-borne remote sensing have produced the NASA A-Train of polar orbiting satellites, specially equipped to observe and catalog cloud properties. The NASA CloudSat instrument, a recent addition to the A-Train and the first 94 GHz radar system operated in space, provides a unique opportunity to compare observed cloud profiles to their modeled counterparts. Comparisons are available through the use of a radiative transfer model (QuickBeam), which simulates 94 GHz radar returns based on the microphysics of cloudy model profiles and the prescribed characteristics of their constituent hydrometeor classes. CloudSat observations of snowfall are presented for a case in the central United States, with comparisons made to precipitating clouds as simulated by the Weather Research and Forecasting Model and the Goddard single-moment microphysics scheme. An additional forecast cycle is performed with a temperature-based parameterization of the snow distribution slope parameter, with comparisons to CloudSat observations provided through the QuickBeam simulator.

Molthan, A. L.↗
NASA NTRS2008

Understanding Ice Supersaturation, Particle Growth, and Number Concentration in Cirrus Clouds

Many factors control the ice supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, ice nucleation mechanisms, and ice crystal growth rate on the evolution and distribution of water vapor and cloud properties in nighttime cirrus clouds using a one-dimensional cloud model with bin microphysics and remote sensing measurements obtained at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. We forced the model using both large-scale vertical ascent and, for the first time, mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where a classical theory heterogeneous scheme is compared with empirical representations. We evaluated model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities. Model sensitivity to the ice growth rate is also investigated. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast ice crystal growth, and initiate ice by either homogeneous or heterogeneous nucleation. Simulated ice crystal number concentrations (tens to hundreds particles per liter) are typically two orders of magnitude smaller than previously published results based on aircraft measurements in cirrus clouds, although higher concentrations are possible in isolated pockets within the nucleation zone.

Comstock, Jennifer M.↗
DOE OSTI2023

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mixed-phase clouds and precipitation in Southern Ocean cyclones and cloud systems observed poleward of 64°S by ship-based cloud radar and lidar

Mixed-phase clouds (MPCs), composed of both liquid and ice, are prevalent in Southern Ocean cyclones. A characterisation of these clouds on fine vertical scales is required in order to understand the microphysical processes within these clouds, and for model and satellite evaluations over this region. We investigated three examples of cloud systems collected by ship-mounted remote sensing instruments adjacent to East Antarctica at latitudes between 64°S and 69°S. These cases allow us to examine the properties of mid-level MPCs, with cloud tops between 2 and 6 km. Mid-level MPCs contain multiple layers of supercooled liquid water (SLW) embedded within ice during the passage of cyclones. SLW layers are capped by strong temperature inversions and are observed at temperatures as low as - 31°C. Convective generating cells (GCs) are present inside supercooled liquid-topped mid-level MPCs. The horizontal extent, vertical extent and maximum upward Doppler velocity of these GCs were 0.6 – 3.6 km, 0.7 – 1.0 km and 0.5 – 1.0 m s -1 , respectively, and are consistent with observations from previous lower-latitude studies. Ice precipitation is nearly ubiquitous, except in the thinnest clouds at the trailing end of the observed systems. Seeding of lower SLW layers from above leads to periods with either larger ice particles or greater ice precipitation rates. Additionally, periods of supercooled drizzle lasting up to two hours were observed toward the end of two of the three cyclone systems. This supercooled drizzle turns into predominantly ice precipitation as the result of seeding by ice clouds located above the precipitating SLW layer.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Smoke, Clouds, and Radiation-Brazil (SCAR-B) Experiment

The Smoke, Clouds, and Radiation-Brazil (SCAR-B) field project took place in the Brazilian Amazon and cerrado regions in August-September 1995 as a collaboration between Brazilian and American scientists. SCAR-B, a comprehensive experiment to study biomass burning, emphasized measurements of surface biomass, fires, smoke aerosol and trace gases, clouds, and radiation. their climatic effects, and remote sensing from aircraft and satellites. It included aircraft and ground-based in situ measurements of smoke emission factors and the compositions, sizes, and optical properties of the smoke particles; studies of the formation of ozone; the transport and evolution of smoke; and smoke interactions with water vapor and clouds. This overview paper introduces SCAR-B and summarizes some of the main results obtained so far. (1) Fires: measurements of the size distribution of fires, using the 50 m resolution MODIS Airborne Simulator, show that most of the fires are small (e.g. 0.005 square km), but the satellite sensors (e.g., AVHRR and MODIS with I km resolution) can detect fires in Brazil which are responsible for 60-85% of the burned biomass: (2) Aerosol: smoke particles emitted from fires increase their radius by as much as 60%, during their first three days in the atmosphere due to condensation and coagulation, reaching a mass median radius of 0.13-0.17 microns: (3) Radiative forcing: estimates of the globally averaged direct radiative forcing due to smoke worldwide, based on the properties of smoke measured in SCAR-B (-O.l to -0.3 W m(exp -2)), are smaller than previously modeled due to a lower single-scattering albedo (0.8 to 0.9), smaller scattering efficiency (3 square meters g(exp -2) at 550 nm), and low humidification factor; and (4) Effect on clouds: a good relationship was found between cloud condensation nuclei and smoke volume concentrations, thus an increase in the smoke emission is expected to affect cloud properties. In SCAR-B, new techniques were developed for deriving the absorption and refractive index of smoke from ground-based remote sensing. Future spaceborne radiometers (e.g., MODIS on the Earth Observing System), simulated on aircraft, proved to be very useful for monitoring smoke properties, surface properties, and the impacts of smoke on radiation and climate.

Kaufman, Y. J.↗
NASA NTRS2007

The Far Infrared Earth

The paper presents a review of the far infrared (FIR) properties of the Earth's atmosphere, and the role of these properties in climate. These properties have been relatively poorly understood, and it is one of the purposes of this review to demonstrate that, in recent years, we have made great strides in improving this understanding. Seen from space, the Earth is a cool object, with an effective emitting temperature of about 255 K. This contrasts with a global mean surface temperature of ~288 K, and is due primarily to strong absorption of outgoing longwave energy by water vapour, carbon dioxide and clouds (especially ice). A large fraction of this absorption occurs in the FIR, and so the Earth is effectively a FIR planet. The FIR is important in a number of key climate processes, for example the water vapour and cloud feedbacks (especially ice clouds). The FIR is also a spectral region which can be used to remotely sense and retrieve atmospheric composition in the presence of ice clouds. Recent developments in instrumentation have allowed progress in each of these areas, which are described, and proposals for a spaceborne FIR instrument are being formulated. It is timely to review the FIR properties of the clear and cloudy atmosphere, the role of FIR processes in climate, and its use in observing our planet from space.

Harries, John↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRS2010

Quantitative Measures of Immersion in Cloud and the Biogeography of Cloud Forests

Sites described as tropical montane cloud forests differ greatly, in part because observers tend to differ in their opinion as to what constitutes frequent and prolonged immersion in cloud. This definitional difficulty interferes with hydrologic analyses, assessments of environmental impacts on ecosystems, and biogeographical analyses of cloud forest communities and species. Quantitative measurements of cloud immersion can be obtained on site, but the observations are necessarily spatially limited, although well-placed observers can examine 10 50 km of a mountain range under rainless conditions. Regional analyses, however, require observations at a broader scale. This chapter discusses remote sensing and modeling approaches that can provide quantitative measures of the spatiotemporal patterns of cloud cover and cloud immersion in tropical mountain ranges. These approaches integrate remote sensing tools of various spatial resolutions and frequencies of observation, digital elevation models, regional atmospheric models, and ground-based observations to provide measures of cloud cover, cloud base height, and the intersection of cloud and terrain. This combined approach was applied to the Monteverde region of northern Costa Rica to illustrate how the proportion of time the forest is immersed in cloud may vary spatially and temporally. The observed spatial variation was largely due to patterns of airflow over the mountains. The temporal variation reflected the diurnal rise and fall of the orographic cloud base, which was influenced in turn by synoptic weather conditions, the seasonal movement of the Intertropical Convergence Zone and the north-easterly trade winds. Knowledge of the proportion of the time that sites are immersed in clouds should facilitate ecological comparisons and biogeographical analyses, as well as land use planning and hydrologic assessments in areas where intensive on-site work is not feasible.

Lawton, R. O.↗
NASA NTRS2010

Ten Years of MISR Observations from Terra: Looking Back, Ahead, and in Between

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.

remote sensing↗