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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Photochemical ozone production in tropical squall line convection during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A

The role of convection was examined in trace gas transport and ozone production in a tropical dry season squall line sampled on August 3, 1985, during NASA Global Tropospheric Experiment/Amazon Boundary Layer Experiment 2A (NASA GTE/ABLE 2A) in Amazonia, Brazil. Two types of analyses were performed. Transient effects within the cloud are examined with a combination of two-dimensional cloud and one-dimensional photochemical modeling. Tracer analyses using the cloud model wind fields yield a series of cross sections of NO(x), CO, and O3 distribution during the lifetime of the cloud; these fields are used in the photochemical model to compute the net rate of O3 production. At noon, when the cloud was mature, the instantaneous ozone production potential in the cloud is between 50 and 60 percent less than in no-cloud conditions due to reduced photolysis and cloud scavenging of radicals. Analysis of cloud inflows and outflows is used to differentiate between air that is undisturbed and air that has been modified by the storm. These profiles are used in the photochemical model to examine the aftereffects of convective redistribution in the 24-hour period following the storm. Total tropospheric column O3 production changed little due to convection because so little NO(x) was available in the lower troposphere. However, the integrated O3 production potential in the 5- to 13-km layer changed from net destruction to net production as a result of the convection. The conditions of the August 3, 1985, event may be typical of the early part of the dry season in Amazonia, when only minimal amounts of pollution from biomass burning have been transported into the region.

Pickering, Kenneth E.↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗
NASA NTRS1977

Absorption of X-rays in the interstellar medium

The X-ray photoabsorption cross sections (per hydrogen atom) of interstellar clouds and the intercloud medium are calculated separately, taking into account abundance evolution, self-blanketing by grains, molecule formation, and ionization structure. A reference cross section describing a neutral monatomic gas of elements in solar abundances is computed for purposes of comparison, the cross section of the intercloud medium is calculated by assuming that 15% of the hydrogen is ionized, and the cross section of the clouds is computed for the cases where all hydrogen is in either molecular or atomic form. A comparison of the reference cross section with the others shows that a cross section is increased relative to the reference if abundances are enhanced or if molecular hydrogen exists and is decreased if atoms exist in grains or if hydrogen is ionized. It is noted that the total cross section will be a linear combination of the cross sections of the clouds and the intercloud medium and can reflect the characteristics of a particular line of sight.

Ride, S. K.↗
NASA NTRS2011

Photogrammetric Characterization of a Brownout Cloud

Brownout is a dangerous problem for rotorcraft operating in arid and dusty environments such as the current operating theaters in Iraq and Afghanistan. Although the interest in brownout has increased in the past decade, the fundamental physics that govern the shape and size of the cloud are not yet well understood. Many computational and scaled experimental studies have been performed in an attempt to further this understanding and to simulate and predict the brownout cloud formation. However, the phenomenon significantly lacks experimental data, particularly at full-scale, which is needed to help validate the brownout simulations being performed. In an effort to increase the data set needed for this validation, tests were performed at the US Army Yuma Proving Ground using photogrammetry to obtain brownout cloud data of an EH-60L Black Hawk. Particle testing was performed on a sample of sand from the landing zone to gain more understanding on the nature of the soil. The photogrammetry technique applied to obtaining data on the formation and evolution of a brownout cloud was verified in an earlier study. The data for a landing approach was examined in greater detail and enabled velocity components of points on the cloud to be determined, as well as the dimensions of structures within the cloud.

Tanner, Philip E.↗
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 NTRS1993

Radiative transfer for a three-dimensional raining cloud

Satellite-sensor-based microwave brightness temperatures for a three-dimensional raining cloud over a reflecting surface are computed by using a radiative transfer model based on the discrete-ordinates solution procedure. The three-dimensional model applied to a plane layer is validated by comparison with results from a one-dimensional model that is available in the literature. Results examining the effects of cloud height, rainfall rate, surface reflectance, rainfall footprint area, and satellite viewing position on one- and three-dimensional brightness temperature calculations are reported. The numerical experiments indicate that, under certain conditions, three-dimensional effects are significant in the analysis of satellite-sensor-based rainfall retrieval algorithms. The results point to the need to consider carefully three-dimensional effects as well as surface reflectance effects when interpreting satellite-measured radiation data.

Haferman, J. L.↗
NASA NTRS1996

Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network

This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.

Alexander, June↗
NASA NTRSDate not supplied

Additional Characterization of Sonoran Pics in Support of A Stable Multi-Sensor Geostationary Imager Record

The Sonoran Desert is the most utilized Pseudo Invariant Calibration Site (PICS) located in the Americas for post-launch radiometer drift monitoring and sensor pair radiometric scaling. The Sonoran Desert is located near the US Arizona and Mexican border with an elevation of 37 m. The site has small spatial and angular variations; however, soil moisture can cause short- term periodic reflectance fluctuations. The NASA Clouds and the Earth’s Radiant Energy System (CERES) project utilizes the site to validate the GOES East and West imager calibration coefficients derived from inter-calibrating GOES with MODIS. Because the daily local noon angular solar and viewing angles repeat every year over the site, a daily exoatmospheric radiance model (DERM) can be constructed over the lifetime of a well-calibrated GOES sensor. DERM based on a well-calibrated GOES imager can then be used to determine the calibration coefficients of successive GOES imagers to provide a stable multi-sensor GOES imager record. This will ensure that the GOES retrieved clouds and derived broadband fluxes can be used to infer the regional diurnal cycle in between Terra and Aqua CERES measurements to accurately compute the daily mean regional fluxes and clouds over the 20-year CERES SYN1deg product. Although the Sonoran Desert surface reflectance and atmospheric column vary seasonally, the inter-annual variability of the seasonal cycle is small, but it adds noise to the resulting DERM calibration coefficients. We seek to reduce the uncertainty of the DERM approach by improving the clear-sky filtering and correlating the observed interannual reflectance variability with atmospheric parameters, such as precipitable water, ozone concentration, and aerosol optical depth. The additional characterization of the Sonoran Desert site DERM for both the GOES East and West positions should improve the accuracy of the CERES SYN1deg product clouds and fluxes.

Prathana Khakurel↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33601-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1? gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation.

Moguo Sun↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33753-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1 gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation. The paper will also discuss CERES FlxbyCldTyp simulator and its application.

Moguo Sun↗
NASA NTRS2014

Development of Super Ensemble-Based Aviation Turbulence Guidance (SEATG) for Air Traffic Management

A new method for forecasting turbulence is developed and evaluated using the high resolution weather model and in situ turbulence observations from commercial aircraft. The new method is an ensemble of various turbulence metrics from multiple time-lagged ensemble forecasts created using a sequence of four procedures. These include weather modeling, calculation of turbulence metrics, mapping the metrics into a common turbulence-scale, and production of final forecast. The new method uses similar methodology as current operational turbulence forecast with three improvements. First, it uses a higher resolution ((delta)x = 3 km) weather model to capture cloud resolving scale phenomena. Second, it computes the metrics for multiple forecasts that are combined at the same valid time resulting in a time-lagged ensemble of multiple turbulence metrics. Finally, it provides both deterministic and probabilistic turbulence forecasts. Results show the new forecasts match well with observed radar reflectivity along a surface front as well as convectively induced turbulence outside the clouds on research period. Overall performance skill of the new turbulence forecast compared with the observed EDR data during the research period is superior to any single turbulence metric. The probabilistic turbulence forecast is used in an example air traffic management application for creating a wind-optimal route considering turbulence information. The wind-optimal route passing through areas of 50% potential for moderate-or-greater turbulence and the lateral turbulence avoidance routes starting from three different waypoints along the wind-optimal route from Los Angeles international airport to John F. Kennedy international airport are calculated using different turbulence forecasts. This example shows additional flight time is required to avoid potential turbulence encounters.

modeling↗
NASA NTRS2012

Effects of Cloud Horizontal Inhomogeneity and Drizzle on Remote Sensing of Cloud Droplet Effective Radius: Case Studies Based on Large-eddy Simulations

This study investigates effects of drizzle and cloud horizontal inhomogeneity on cloud effective radius (re) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to identify the relative importance of various factors, we developed a MODIS cloud property retrieval simulator based on the combination of large-eddy simulations (LES) and radiative transfer computations. The case studies based on synthetic LES cloud fields indicate that at high spatial resolution (100 m) 3-D radiative transfer effects, such as illumination and shadowing, can induce significant differences between retrievals ofre based on reflectance at 2.1 m (re,2.1) and 3.7 m (re,3.7). It is also found that 3-D effects tend to have stronger impact onre,2.1 than re,3.7, leading to positive difference between the two (re,3.72.1) from illumination and negative re,3.72.1from shadowing. The cancellation of opposing 3-D effects leads to overall reasonable agreement betweenre,2.1 and re,3.7 at high spatial resolution as far as domain averages are concerned. At resolutions similar to MODIS, however, re,2.1 is systematically larger than re,3.7when averaged over the LES domain, with the difference exhibiting a threshold-like dependence on bothre,2.1and an index of the sub-pixel variability in reflectance (H), consistent with MODIS observations. In the LES cases studied, drizzle does not strongly impact reretrievals at either wavelength. It is also found that opposing 3-D radiative transfer effects partly cancel each other when cloud reflectance is aggregated from high spatial resolution to MODIS resolution, resulting in a weaker net impact of 3-D radiative effects onre retrievals. The large difference at MODIS resolution between re,3.7 and re,2.1 for highly inhomogeneous pixels with H 0.4 can be largely attributed to what we refer to as the plane-parallelrebias, which is attributable to the impact of sub-pixel level horizontal variability of cloud optical thickness onre retrievals and is greater for re,2.1 than re,3.7. These results suggest that there are substantial uncertainties attributable to 3-D radiative effects and plane-parallelre bias in the MODIS re,2.1retrievals for pixels with strong sub-pixel scale variability, and theH index can be used to identify these uncertainties.

Zhang, Zhibo↗
NASA NTRS2019

Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors

New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.

Duffy, Kate↗
NASA NTRS1994

Independent pixel and Monte Carlo estimates of stratocumulus albedo

Monte Carlo radiative transfer methods are employed here to estimate the plane-parallel albedo bias for marine stratocumulus clouds. This is the bias in estimates of the mesoscale-average albedo, which arises from the assumption that cloud liquid water is uniformly distributed. The authors compare such estimates with those based on a more realistic distribution generated from a fractal model of marine stratocumulus clouds belonging to the class of 'bounded cascade' models. In this model the cloud top and base are fixed, so that all variations in cloud shape are ignored. The model generates random variations in liquid water along a single horizontal direction, forming fractal cloud streets while conserving the total liquid water in the cloud field. The model reproduces the mean, variance, and skewness of the vertically integrated cloud liquid water, as well as its observed wavenumber spectrum, which is approximately a power law. The Monte Carlo method keeps track of the three-dimensional paths solar photons take through the cloud field, using a vectorized implementation of a direct technique. The simplifications in the cloud field studied here allow the computations to be accelerated. The Monte Carlo results are compared to those of the independent pixel approximation, which neglects net horizontal photon transport. Differences between the Monte Carlo and independent pixel estimates of the mesoscale-average albedo are on the order of 1% for conservative scattering, while the plane-parallel bias itself is an order of magnitude larger. As cloud absorption increases, the independent pixel approximation agrees even more closely with the Monte Carlo estimates. This result holds for a wide range of sun angles and aspect ratios. Thus, horizontal photon transport can be safely neglected in estimates of the area-average flux for such cloud models. This result relies on the rapid falloff of the wavenumber spectrum of stratocumulus, which ensures that the smaller-scale variability, where the radiative transfer is more three-dimensional, contributes less to the plane-parallel albedo bias than the larger scales, which are more variable. The lack of significant three-dimensional effects also relies on the assumption of a relatively simple geometry. Even with these assumptions, the independent pixel approximation is accurate only for fluxes averaged over large horizontal areas, many photon mean free paths in diameter, and not for local radiance values, which depend strongly on the interaction between neighboring cloud elements.

Cahalan, Robert F.↗
NASA NTRS2011

Graphics Processing Units (GPU) and the Goddard Earth Observing System atmospheric model (GEOS-5): Implementation and Potential Applications

Earth system models like the Goddard Earth Observing System model (GEOS-5) have been pushing the limits of large clusters of multi-core microprocessors, producing breath-taking fidelity in resolving cloud systems at a global scale. GPU computing presents an opportunity for improving the efficiency of these leading edge models. A GPU implementation of GEOS-5 will facilitate the use of cloud-system resolving resolutions in data assimilation and weather prediction, at resolutions near 3.5 km, improving our ability to extract detailed information from high-resolution satellite observations and ultimately produce better weather and climate predictions

Putnam, William M.↗
NASA NTRS2015

Nearly a Decade of CALIPSO Observations of Asian and Saharan Dust Properties Near Source and Transport Regions

The lidar on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, makes robust measurements of dust and has generated a length of record that is significant both seasonally and inter-annually. We exploit this record to determine a multi-year climatology of the properties of Asian and Saharan dust, in particular seasonal optical depths, layer frequencies, and layer heights of dust gridded in accordance with the Level 3 data products protocol, between 2006-2015. The data are screened using standard CALIPSO quality assurance flags, cloud aerosol discrimination (CAD) scores, overlying features and layer properties. To evaluate the effects of transport on the morphology, vertical extent and size of the dust layers, we compare probability distribution functions of the layer integrated volume depolarization ratios, geometric depths and integrated attenuated color ratios near the source to the same distributions in the far field or transport region. CALIPSO is collaboration between NASA and Centre National D'études Spatiales (CNES), was launched in April 2006 to provide vertically resolved measurements of cloud and aerosol distributions. The primary instrument on the CALIPSO satellite is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a near-nadir viewing two-wavelength polarization-sensitive instrument. The unique nature of CALIOP measurements make it quite challenging to validate backscatter profiles, aerosol type, and cloud phase, all of which are used to retrieve extinction and optical depth. To evaluate the uncertainty in the lidar ratios, we compare the values computed from dust layers overlying opaque water clouds, considered nominal, with the constant lidar ratio value used in the CALIOP algorithms for dust. We also explore the effects of noise on the CALIOP retrievals at daytime by comparing the distributions of the properties at daytime to the nighttime distributions.

Omar, Ali H.↗
NASA NTRS2007

Validation of GOES-10 Satellite-derived Cloud and Radiative Properties for the MASRAD ARM Mobile Facility Deployment

This poster presentation reviews the process used to validate the GOES-10 satellite derived cloud and radiative properties. The ARM Mobile Facility (AMF) deployment at Pt Reyes, CA as part of the Marine Stratus Radiation Aerosol and Drizzle experiment (MASRAD), 14 March - 14 September 2005 provided an excellent chance to validate satellite cloud-property retrievals with the AMF's flexible suite of ground-based remote sensing instruments. For this comparison, NASA LaRC GOES10 satellite retrievals covering this region and period were re-processed using an updated version of the Visible Infrared Solar-Infrared Split-Window Technique (VISST), which uses data taken at 4 wavelengths (0.65, 3.9,11 and 12 m resolution), and computes broadband fluxes using improved CERES (Clouds and Earth's Radiant Energy System)-GOES-10 narrowband-to-broadband flux conversion coefficients. To validate MASRAD GOES-10 satellite-derived cloud property data, VISST-derived cloud amounts, heights, liquid water paths are compared with similar quantities derived from available ARM ground-based instrumentation and with CERES fluxes from Terra.

Khaiyer, M. M.↗
NASA NTRS2007

Evaluation of a Cloud Resolving Model Using TRMM Observations for Multiscale Modeling Applications

The climate change simulation community is moving toward use of global cloud resolving models (CRMs), however, current computational resources are not sufficient to run global CRMs over the hundreds of years necessary to produce climate change estimates. As an intermediate step between conventional general circulation models (GCMs) and global CRMs, many climate analysis centers are embedding a CRM in each grid cell of a conventional GCM. These Multiscale Modeling Frameworks (MMFs) represent a theoretical advance over the use of conventional GCM cloud and convection parameterizations, but have been shown to exhibit an overproduction of precipitation in the tropics during the northern hemisphere summer. In this study, simulations of clouds, precipitation, and radiation over the South China Sea using the CRM component of the NASA Goddard MMF are evaluated using retrievals derived from the instruments aboard the Tropical Rainfall Measuring Mission (TRMM) satellite platform for a 46-day time period that spans 5 May - 20 June 1998. The NASA Goddard Cumulus Ensemble (GCE) model is forced with observed largescale forcing derived from soundings taken during the intensive observing period of the South China Sea Monsoon Experiment. It is found that the GCE configuration used in the NASA Goddard MMF responds too vigorously to the imposed large-scale forcing, accumulating too much moisture and producing too much cloud cover during convective phases, and overdrying the atmosphere and suppressing clouds during monsoon break periods. Sensitivity experiments reveal that changes to ice cloud microphysical parameters have a relatively large effect on simulated clouds, precipitation, and radiation, while changes to grid spacing and domain length have little effect on simulation results. The results motivate a more detailed and quantitative exploration of the sources and magnitude of the uncertainty associated with specified cloud microphysical parameters in the CRM components of MMFs.

Posselt, Derek J.↗