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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Reflectivity of earth's surface and clouds in ultraviolet from satellite observations

The Total Ozone Mapping Spectrometer on board Nimbus 7 is used to infer the UV surface and cloud reflectance at 370 nm. Cloudless surface reflectivity was analyzed on a global basis for all surface types for several months. The UV surface reflectivity varies from 2 percent for some forest and grassland regions to 14 percent for some sandy desert areas. A notable exception is the large salt flats of Bolivia, which have a reflectivity of about 60 percent. Cloud reflectivity was also analyzed for clouds located at three levels in the atmosphere, as determined by the 11.5 micron channel of the Temperature Humidity Infrared Radiometer. Average cloud reflectivity at 370 nm ranges from 52 percent for low clouds (tops less than 2 km) to 76 percent for high clouds (tops greater than 7 km at the equator, decreasing to greater than 4 km at poles).

Eck, T. F.↗
NASA NTRS2014

How Do A-train Sensors Intercompare in the Retrieval of Above-cloud Aerosol Optical Depth? A Case Study-based Assessment

We intercompare the above-cloud aerosol optical depth (ACAOD) of biomass burning plumes retrieved from A-train sensors, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Polarization and Directionality of Earth Reflectances (POLDER), and Ozone Monitoring Instrument (OMI). These sensors have shown independent capabilities to retrieve aerosol loading above marine boundary layer clouds-a kind of situation often found over the southeast Atlantic Ocean during dry burning season. A systematic comparison reveals that all passive sensors and CALIOP-based research methods derive comparable ACAOD with differences mostly within 0.2 over homogeneous cloud fields. The 532 nm ACAOD retrieved by CALIOP operational algorithm is underestimated. The retrieved 1064 nm AOD however shows closer agreement with passive sensors. Given the different types of measurements processed with different algorithms, the reported close agreement between them is encouraging. Due to unavailability of direct measurements above cloud, the validation of satellite-based ACAOD remains an open challenge. The intersatellite comparison however can be useful for the relative evaluation and consistency check

Aerosol↗
NASA NTRS1986

Sandqvist 187 - A dense molecular cloud in Norma

Observations of Sandqvist 187, an elongated dust cloud in the southern constellation Norma are presented and discussed. The cloud contains two Herbig-Haro objects, HH 56 and HH 57. HH 57 currently displays on its NE edge a 17th mag variable star of the FU Ori type. Using the Columbia University 1.2 m millimeter-wave telescope at Cerro Tololo, the region is mapped and an extended CO cloud which envelops and is elongated along the optical dust cloud is found. The position of maximum CO emission coincides with HH 56 and HH 57. Assuming a distance of 0.7 kpc, the total mass of the cloud is found to be close to 500 solar masses. The CO spectra show evidence of a molecular flow. Photographs and CCD images obtained mostly with the CTIO 4 m telescope show the detailed optical structure of the dark cloud's core region. The Herbig-Haro object HH 56 appears to be related to an emission-line star embedded in the small nebula Reipurth 13, not to the FU Ori star in HH 57.

Alvarez, H.↗
NASA NTRS1994

Star formation in Taurus

Data with the Two Micron All Sky Survey (2MASS) prototype camera were obtained in a 2.3 sq. deg region in Taurus containing Heiles Cloud 2, a region known from Infrared Astronomy Satellite (IRAS) observations to contain a number of very young solar type stars. Data at 1.25 (J), 1.65 (H), and 2.2 (K(sub s)) micrometers are presented. These data are representative of the type and quality of data expected from the planned near-IR surveys, 2MASS and Deep Near-Infrared Survey (DENIS). Near-IR surveys will be useful for determining the large scale variation of extinction with clouds, for determining the luminosity function in nearby clouds down to ranges of 0.1-1.0 solar luminosity, and for finding highly extincted T Tauri stars missed by IRAS because the bulk of their luminosity is emitted shortward of 12 micrometers.

Beichman, C. A.↗
NASA NTRS1998

Characterization of Clouds and the Anisotropy of Emitted and Reflected Radiances for the Purpose of Obtaining the Radiative Heating of the Atmosphere

The goal of the work supported through this grant was to assess the validity of the assumptions underlying the CERES Strategy for determining radiative fluxes. Specifically, the work focused on the determination of scene type and the use of anisotropic factors to derive radiative fluxes from observed broadband radiances. The work revealed a dependence of the anisotropy of reflected and emitted broadband radiances on the spatial resolution of the observations that had been overlooked in the formulation of the CERES strategy. This dependence on spatial resolution coupled with errors in scene identification led to view zenith angle dependent biases in the ERBE derived radiative fluxes. Scene identification will be greatly improved in CERES thereby alleviating somewhat the biases arising from the dependence of the anisotropy of the radiances on spatial resolution. Attention was then focused on the validity of plane-parallel radiative transfer theory which is relied on to characterize the scene types viewed by the CERES scanner. Again, viewing geometry dependent biases were found even for single-layered, overcast cloud systems. Such systems are taken to be the closest examples of plane-parallel clouds. At least some of the departures from plane-parallel behavior were evidently due to relatively small bumps on the tops of extensive stratus layers. The bumps cannot be resolved in the imagery that will be used to characterize the scenes viewed by the CERES scanner. As part of this investigation, the ice sheets of Greenland and Antarctica were shown to provide radiometrically stable targets for determining the visible and near infrared calibrations of radiometers. These targets were used to calibrate the reflected sunlight at visible wavelengths used in this study. Finally, the limitations of plane-parallel theory notwithstanding, the common practice of ignoring fractional cloud cover within the fields of view of imaging radiometers was shown to lead to biases in the retrieved cloud properties. The development of retrievals for pixel-scale cloud cover fraction is an attempt to reduce such bases. Work on these retrievals continues.

Coakley, James A., Jr.↗
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Gail Skofronick-Jackson↗
NASA NTRS2022

Aerosol Detection from the Cloud Aerosol Transport System on the International Space Station: Algorithm Overview and Implications for Diurnal Sampling

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia.

Edward P Nowottnick↗
NASA NTRSDate not supplied

The Influence of Sea ice on Arctic Cloud Properties: What Can We Learn by Applying a In Situ Observational Strategy to Satellite Data?

Sea ice is declining because of anthropogenic climate change. This change alters many aspects of the Arctic climate system, including the way that the surface and atmosphere interact. Atmosphere-surface coupling processes represent an important cloud feedback mechanism that can alter the Arctic surface energy budget. For the Arctic, it has been hypothesized that a reduction in sea ice cover could lead to an increase in clouds. If this process were to occur as originally hypothesized, sea ice loss in all seasons would lead to an increase in clouds. Recent observational studies find a cloud response to sea ice loss in non-summer months and no cloud response in summer months. However, previous studies rely on inter-annual variability and reanalysis to control for the influence of meteorology, reducing the confidence in the resulting conclusions. We adopt a phenomenological, event-based approach that does not need to use meteorological reanalysis. The approach analyzes cloud properties derived from CALIPSO-CloudSat over sea ice and adjacent ice-free footprints by compositing individual satellite ground tracks that cross the Arctic sea ice edge. The underlying assumption, which we verify, is that footprints that are close to each other in space and time experience similar large-scale meteorological conditions. Our results show larger cloud fraction and more cloud liquid water over ice-free than over sea ice footprints and provide additional evidence for a seasonal dependence of cloud-sea ice coupling that is in line with previous work. We find a different result where the maximum cloud property differences between sea ice and ice-free ocean occurs in spring, not fall as earlier studies suggest. We argue that these cloud differences between sea ice and ice-free ocean are primarily caused by the influence of the surface type on the thermodynamic stability of the lower troposphere and not principally from an increase in surface evaporation. In addition, we explore the sensitivity of these results to marginal ice zone width, season, Atlantic vs. Pacific sector, and the cloud property dependence on the distance from the sea ice edge. Overall, our results provide further evidence that cloud-sea ice coupling processes are not offsetting the observed surface energy budget perturbation due to sea ice loss in summer; thus, the cloud response to declining sea ice appears to contribute to amplified Arctic warming.

Patrick C Taylor↗
NASA NTRS2008

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Aerosols and especially their effect on clouds are one of the key components of the climate system and the hydrological cycle [Ramanathan et al., 2001]. Yet, the aerosol effect on clouds remains largely unknown and the processes involved not well understood. A recent report published by the National Academy of Science states "The greatest uncertainty about the aerosol climate forcing - indeed, the largest of all the uncertainties about global climate forcing - is probably the indirect effect of aerosols on clouds [NRC, 2001]." The aerosol effect on clouds is often categorized into the traditional "first indirect (i.e., Twomey)" effect on the cloud droplet sizes for a constant liquid water path [Twomey, 1977] and the "semi-direct" effect on cloud coverage [e.g., Ackerman et al ., 2001]." Enhanced aerosol concentrations can also suppress warm rain processes by producing a narrow droplet spectrum that inhibits collision and coalescence processes [e.g., Squires and Twomey, 1961; Warner and Twomey, 1967; Warner, 1968; Rosenfeld, 19991. The aerosol effect on precipitation processes, also known as the second type of aerosol indirect effect [Albrecht, 1989], is even more complex, especially for mixed-phase convective clouds. Table 1 summarizes the key observational studies identifying the microphysical properties, cloud characteristics, thermodynamics and dynamics associated with cloud systems from high-aerosol continental environments. For example, atmospheric aerosol concentrations can influence cloud droplet size distributions, warm-rain process, cold-rain process, cloud-top height, the depth of the mixed phase region, and occurrence of lightning. In addition, high aerosol concentrations in urban environments could affect precipitation variability by providing an enhanced source of cloud condensation nuclei (CCN). Hypotheses have been developed to explain the effect of urban regions on convection and precipitation [van den Heever and Cotton, 2007 and Shepherd, 2005]. Please see Tao et al. (2007) for more detailed description on aerosol impact on precipitation. Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low "clean" concentration and a high "dirty" concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

Tao, Wei-Kuo↗
NASA NTRS2018

MODIS Collection 6 MAIAC Algorithm

This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

MAIAC Algorithm↗
NASA NTRS2004

On the Retrieval of Ice Cloud Particle Shapes from POLDER Measurements

Shapes of ice crystals can significantly affect the radiative transfer in ice clouds. The angular distribution of the polarized reflectance over ice clouds strongly depends on ice crystal shapes. Although the angular-distribution features of the total or polarized reflectance over ice clouds implies a possibility of retrieving ice cloud particle shapes by use of remote sensing data, the accuracy of the retrieval must be evaluated. In this study, a technique, which applies single ice crystal habit and multidirectional polarized radiance to retrieve ice cloud particle shapes, is assessed. Our sensitivity studies show that the retrieved particle shapes &om this algorithm can be considered good approximations to those in actual clouds in calculation of the phase matrix elements. Although a fractal poly-crystal shape or an inhomogeneous hexagonal column may also produce this type of phase functions, more representative single-scattering properties from combinations of natural particle shapes and size distributions may still be necessary in accurate retrieval of other cloud properties such as optical thickness and particle size.

Sun, W.↗
NASA NTRS1991

Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection

The performance of several ice parameterizations has been evaluated through a numerical cloud model. Ice effects using different schemes are contrasted with each other and with an ice-free control by incorporating them into the cloud model and by applying them to simulations of tropical squall systems. The latter are simulated in 2D so that a large domain can be used to incorporate a complete anvil. Nonsquall-type convective lines are simulated in 3D owing to their smaller horizontal scale. It is concluded that inclusion of ice microphysics in the cloud model enhanced the agreement of the simulated convection with some features of observed convection, including the proportion of surface rainfall in the anvil region and the intensity and structure of the radar brightband near the melting level in the anvil. In the experiments with bulk microphysics, three ice categories produced much better results than two ice categories, which in turn was better than no ice. For the tropical squall-type and nonsquall-type systems the optimal mix was ice, snow, and graupel.

Mccumber, Michael↗
DOE OSTI2023

Ground and excited electronic structures of electride and alkalide units: The cases of Metal-Tren, -Azacryptand, and -TriPip222 complexes

A systematic electronic structure analysis was conducted for M(L) n molecular electrides and their corresponding alkalide units M(L) n @M' (M/M' = Na, K; L = Tren, Azacryptand, TriPip222; n = 1, 2). All complexes belong to the “superalkali” category due to their low ionization potentials. The saturated molecular electrides display M+(L) n - form with a greatly diffuse quasispherical electron cloud. They were identified as “superatoms” considering the contours of populating atomic-type molecular orbitals. The observed superatomic Aufbau order of M(Tren) 2 is 1S, 1P, 1D, 1F, 2S, 2P, and 1G and it is consistent with those of M(Azacryptand) and M(TriPip222) up to the analyzed 1F level. Their excitation energies decrease gradually moving from M(Tren) 2 to M(Azacryptand) and to M(TriPip222). The studied alkalide complexes carry [M(L) n ] + @M' - ionic structure and their dissociation energies vary in the sequence of K(L) n @Na > Na(L) n @Na > K(L) n @K > Na(L) n @K. Similar to molecular electrides, the anions of alkalide units occupy electrons in diffuse Rydberg-like orbitals. In this work, excited states of [M(L) n @M'] 0/+/- and their trends are also analyzed.

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

Modified Quadrupole Ion Trap Mass Spectrometer for Infrared Ion Spectroscopy: Application to Protonated Thiated Uridines

Modifications to a Paul-type quadrupole ion trap mass spectrometer providing optical access to the trapped ion cloud as well as hardware and software for coupling to a table-top IR optical parametric oscillator laser (OPO) are detailed. Critical experimental parameters for infrared multiple photon dissociation (IRMPD) on this instrument are characterized. IRMPD action spectra, collected in the hydrogen-stretching region with this instrument, complemented by spectra in the IR fingerprint region acquired at the FELIX facility, are employed to characterize the structures of the protonated forms of 2-thiouridine, [s{sup 2}Urd+H]{sup +}, and 4-thiouridine, [s{sup 4}Urd+H]{sup +}. The measured spectra are compared with predicted linear IR spectra calculated at the B3LYP/6-311+G(d,p) level of theory to determine the conformers populated in the experiments. This comparison indicates that thiation at the 2- or 4-positions shifts the protonation preference between the 2,4-H tautomer and 4-protonation in opposite directions versus canonical uridine, which displays a roughly equal preference for the 2,4-H tautomer and O4 protonation. As found for canonical uridine, protonation leads to a mixture of conformers exhibiting C2′-endo and C3′-endo sugar puckering with an anti nucleobase orientation being populated for both 2- and 4-thiated uridine. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Dependence of tropical cyclone seeds and climate sensitivity on tropical cloud response

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth’s surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase.

Science & Technology - Other Topics↗
DOE OSTI2024

Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification

Abstract Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between ∼50- and 100-km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of a type of superresolving convolutional neural network (SR-CNNs) to downscale surface wind speed data over land from different coarse resolutions (25-, 48-, and 100-km resolution) to 3 km. For each downscaling factor, we consider three convolutional neural network (CNN) configurations that generate superresolved predictions of fine-scale wind speed, which take between one and three input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated through which sample wind speeds can be generated, accounting for the intrinsic stochasticity of wind speed. For assessing generalization to new data, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of superresolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance when compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability when compared with other input configurations.

17 WIND ENERGY↗
DOE OSTI2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automated Sorghum Phenotyping and Trait Development Platform

There is an urgent need to accelerate energy crop development for the production of renewable transportation fuels from biomass. Our interdisciplinary team developed mobile, ground-based and aerial phenotyping platforms and advanced remote sensing data analysis tools to acquire and process imagery from different types of cameras (e.g. RGB, multispectral, hyperspectral, and thermal sensors) and LiDAR point cloud data in large-scale sorghum field trials during phase 1 of the project. Phase 2 focused on Technology to Market activities to deliver these systems to the market place coupled with targeted research programs to address specific limitations or challenges for these systems.

09 BIOMASS FUELS↗