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NASA NTRS2001

Flying the TRMM Satellite in a GCM

By incorporating the Tropical Rainfall Measurement Mission (TRMM) satellite orbital information into the Colorado State University General Circulation Model (CSU GCM), we are able to 'fly' a satellite in the GCM, and sample the simulated atmosphere in the same way as the TRMM sensors sample the real atmosphere. The TRMM-sampled statistics for precipitation and radiative fluxes at annual, intraseasonal, monthly-mean and seasonal-mean diurnal time scales are evaluated by comparing the satellite-sampled against fully-sampled simulated atmospheres. The sampling rates of the TRMM sensors are significantly affected by the sensors' swath widths. The TRMM Microwave Imager (TMI) and the Visible Infrared Scanner (VIRS) sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics about once per day, but at a different local time every day, while the Precipitation Radar (PR) and the Clouds and the Earth's Radiant Energy System (CERES) sensor visit each grid box about once every three days and twice per day, respectively. Besides inadequate samplings resulting from sensors' swath widths, there is a large, systematic diurnal undersampling associated with TRMM's orbital geometry for grid boxes away from the Equator. When only one month of TRMM data are used, this diurnal undersampling can lead to more daytime samples relative to nighttime samples in one hemisphere, and more nighttime samples relative to daytime samples in the other hemisphere. The resulting sampling biases (3-6 W m(exp-2)) are very pronounced in outgoing longwave radiation (OLR) over the subtropical land masses. The sampling errors in OLR monthly- and seasonal-means are less than 8 W m(exp-2) (5%) for each 2.25 x 2.25 degree grid box. The OLR monthly- and seasonal-means are not sensitive to diurnal undersamplings associated with the TRMM orbits and sensors' swath widths. However, this is not the case for total precipitation. Diurnal undersampling could produce errors as large as 20% in the Tropics and 40% in the subtropics, for the zonally averaged monthly-mean rain rates. The TRMM orbits sample each 2.25 x 2.25 degree grid box in the Tropics and subtropics 1-6 times for each hour of the day within a single season. The seasonal-mean diurnal cycles of precipitation and OLR are not well sampled for any one grid box. By either accumulating the satellite data for a long enough period, or averaging the data over a large area with a relatively uniform diurnal signal, the diurnal cycles of precipitation and OLR can be satisfactorily sampled. The effects TRMM sampling errors on the inferred tropical-mean hydrologic cycle and radiative fluxes are also evaluated. There are strong spurious oscillations associated with TRMM's orbital geometry, with periods of 23 days and 3-4 months, in tropical-mean daily and monthly precipitation. While the relative fluctuations of the sampled-OLR are negligible, the relative fluctuations of the sampled precipitation have magnitudes similar to those of the observed climate variability. Caution must therefore be used when applying TRMM observations of tropical-mean precipitation to interpret climate variations at intraseasonal and interannual scales.

Lin, Xin↗

NASA NTRS1995

Sources of the 13 Microns Feature Associated with Oxygen-Rich Circumstellar Dust

A search of the database of spectra from the IRAS Low-Resolution Spectrometer has revealed 13 micron emission features in 187 sources, most of which are associated with stars embedded within oxygen-rich circumstellar dust shells. Our sample is very incomplete, but we estimate that 40-50% of all sources with oxygen-rich dust on the asymptotic giant branch (AGB) exhibit emission features at 13 microns. The similarities of the galactic distribution, mean radial velocities, infrared colors, and other properties of the sample of 13 micron sources compared to the parent population of variables on the AGB suggest that the carrier of the 13 micron feature is commonly produced in oxygen-rich dust environments. A strikingly large fraction of SRb variables show 13 micron features, approximately 75-90%, suggesting that the formation and/or survival of the carrier of the 13 micron feature may be enhanced by the unusual pulsational properties of these variables.

Sloan, G. C.↗

NASA NTRS2002

Maximizing Macromolecule Crystal Size for Neutron Diffraction Experiments

A challenge in neutron diffraction experiments is growing large (greater than 1 cu mm) macromolecule crystals. In taking up this challenge we have used statistical experiment design techniques to quickly identify crystallization conditions under which the largest crystals grow. These techniques provide the maximum information for minimal experimental effort, allowing optimal screening of crystallization variables in a simple experimental matrix, using the minimum amount of sample. Analysis of the results quickly tells the investigator what conditions are the most important for the crystallization. These can then be used to maximize the crystallization results in terms of reducing crystal numbers and providing large crystals of suitable habit. We have used these techniques to grow large crystals of Glucose isomerase. Glucose isomerase is an industrial enzyme used extensively in the food industry for the conversion of glucose to fructose. The aim of this study is the elucidation of the enzymatic mechanism at the molecular level. The accurate determination of hydrogen positions, which is critical for this, is a requirement that neutron diffraction is uniquely suited for. Preliminary neutron diffraction experiments with these crystals conducted at the Institute Laue-Langevin (Grenoble, France) reveal diffraction to beyond 2.5 angstrom. Macromolecular crystal growth is a process involving many parameters, and statistical experimental design is naturally suited to this field. These techniques are sample independent and provide an experimental strategy to maximize crystal volume and habit for neutron diffraction studies.

Judge, R. A.↗

NASA NTRS2008

Desert Dust Air Mass Mapping in the Western Sahara, using Particle Properties Derived from Space-based Multi-angle Imaging

Coincident observations made over the Moroccan desert during the SAhara Mineral dUst experiMent (SAMUM) 2006 field campaign are used both to validate aerosol amount and type retrieved from Multi-angle Imaging SpectroRadiometer (MISR) observations, and to place the sub-orbital aerosol measurements into the satellite's larger regional context. On three moderately dusty days for which coincident observations were made, MISR mid-visible aerosol optical thickness (AOT) agrees with field measurements point-by-point to within 0.05 to 0.1. This is about as well as can be expected given spatial sampling differences; the space-based observations capture AOT trends and variability over an extended region. The field data also validate MISR's ability to distinguish and to map aerosol air masses, from the combination of retrieved constraints on particle size, shape, and single-scattering albedo. For the three study days, the satellite observations (a) highlight regional gradients in the mix of dust and background spherical particles, (b) identify a dust plume most likely part of a density flow, and (c) show an air mass containing a higher proportion of small, spherical particles than the surroundings, that appears to be aerosol pollution transported from several thousand kilometers away.

Kahn, Ralph↗

NASA NTRS2008

Desert Dust Aerosol Air Mass Mapping in the Western Sahara, Using Particle Properties Derived from Space-Based Multi-Angle Imaging

Coincident observations made over the Moroccan desert during the Sahara mineral dust experiment (SAMUM) 2006 field campaign are used both to validate aerosol amount and type retrieved from multi-angle imaging spectroradiometer (MISR) observations, and to place the suborbital aerosol measurements into the satellite s larger regional context. On three moderately dusty days during which coincident observations were made, MISR mid-visible aerosol optical thickness (AOT) agrees with field measurements point-by-point to within 0.05 0.1. This is about as well as can be expected given spatial sampling differences; the space-based observations capture AOT trends and variability over an extended region. The field data also validate MISR s ability to distinguish and to map aerosol air masses, from the combination of retrieved constraints on particle size, shape and single-scattering albedo. For the three study days, the satellite observations (1) highlight regional gradients in the mix of dust and background spherical particles, (2) identify a dust plume most likely part of a density flow and (3) show an aerosol air mass containing a higher proportion of small, spherical particles than the surroundings, that appears to be aerosol pollution transported from several thousand kilometres away.

Kahn, Ralph↗

NASA NTRS2016

Defining Long-Duration Traverses of Lunar Volcanic Complexes with LROC NAC Images

A long-duration lunar rover [e.g., 1] would be ideal for investigating large volcanic complexes like the Marius Hills (MH) (approximately 300 x 330 km), where widely spaced sampling points are needed to explore the full geologic and compositional variability of the region. Over these distances, a rover would encounter varied surface morphologies (ranging from impact craters to rugged lava shields), each of which need to be considered during the rover design phase. Previous rovers including Apollo, Lunokhod, and most recently Yutu, successfully employed pre-mission orbital data for planning (at scales significantly coarser than that of the surface assets). LROC was specifically designed to provide mission-planning observations at scales useful for accurate rover traverse planning (crewed and robotic) [2]. After-the-fact analyses of the planning data can help improve predictions of future rover performance [e.g., 3-5].

Stopar, J. D.↗

NASA NTRS2020

The Role of Composition and Surface Area Variability in Ice Nucleation, and How to Get Rid of It

Ice nucleation active site densities and nucleation rates are commonly determined by measuring the fraction of droplets that freeze at different temperatures. In analysing these experiments it is commonly assumed that variability in the immersed particle surface area and composition within a sample can be neglected, allowing a simple parameterization of ice nucleation efficiencies. Here it is shown that these approximations may result in large error, and translate into a bias in estimated active site densities of about six orders of magnitude. When applied in cloud models it may represent up to 5 K error in cloud glaciation temperature. A solution to this enduring problem is proposed for the first time. It is shown that by performing droplet freezing experiments varying mean particle surface area along with the temperature such a bias can be quantitatively removed. This amounts to differentiating the "freezing rate'' from the ice nucleation coefficient, or, the apparent and the actual active site density of a material. The proposed method will contribute to reduce existing uncertainties in the relative strength of the ice nucleation ability of different aerosol species, and the modeling of mixed-phase clouds.

Barahona, Donifan↗

NASA NTRSDate not supplied

Characterizing the Seasonal and Diurnal Aerosol Cycles using CATS Space-Based Lidar

The broader effects aerosols have on the earth and climate system is determined by a host of factors including their composition and vertical distribution. Characterizing the vertical distribution of clouds and aerosols is especially important considering it remains one of the greatest uncertainties in climate predictions. Space-based lidar, such as Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud-Aerosol Transport System (CATS), is especially well suited as a remote sensing tool in determining this vertical distribution. CALIOP has been used for over a decade to observe the global and vertical distribution of clouds and aerosols but is limited by Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations’ (CALIPSO’s) polar orbit to set equatorial crossing times of ~1:30AM/PM. This limited temporal sampling limits the use of CALIOP in determining the full diurnal variability of clouds and aerosols. On the International Space Station (ISS), with its inclined orbit and variable equatorial crossing times, the CATS lidar provided an opportunity to study the vertical profile of clouds and aerosols at a variety of local times. With this 33-month CATS record, we show that CATS captured the seasonal changes in global aerosol distributions and compares favorably with other instruments and models. Specifically targeting biomass burning regions, we also find CATS could observe the documented diurnal cycle in aerosol altitudes with some caveats. With the next generation of proposed space-based lidars including designs in similar orbits as the ISS and CATS, future studies of the aerosol diurnal and seasonal cycles would also be possible.

Kenneth Christian↗

DOE OSTI2022

Arbitrary electron dose waveforms for electron microscopy

A device may include an electron source, a detector, and a deflector. The electron source may be directed toward a sample area. The detector may receive an electron signal or an electron-induced signal. A deflector may be positioned between the electron source and the sample. The deflector may modulate an intensity of the electron source directed to the sample area according to an electron dose waveform having a continuously variable temporal profile.

Bloom, Ruth Shewmon↗

DOE OSTI2023

Arbitrary electron dose waveforms for electron microscopy

Bloom, Ruth Shewmon↗

DOE OSTI2019

Identification of RR Lyrae stars in multiband, sparsely-sampled data from the Dark Energy Survey using template fitting and Random Forest classification

Many studies have shown that RR Lyrae variable stars (RRL) are powerful stellar tracers of Galactic halo structure and satellite galaxies. The Dark Energy Survey (DES), with its deep and wide coverage (g ~ 23.5 mag in a single exposure; over 5000 deg2) provides a rich opportunity to search for substructures out to the edge of the Milky Way halo. However, the sparse and unevenly sampled multiband light curves from the DES wide-field survey (a median of four observations in each of grizY over the first three years) pose a challenge for traditional techniques used to detect RRL. We present an empirically motivated and computationally efficient template-fitting method to identify these variable stars using three years of DES data. When tested on DES light curves of previously classified objects in SDSS stripe 82, our algorithm recovers 89% of RRL periods to within 1% of their true value with 85% purity and 76% completeness. Using this method, we identify 5783 RRL candidates, ~28% of which are previously undiscovered. This method will be useful for identifying RRL in other sparse multiband data sets.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1972

A dilatometer for volume-temperature determinations of liquids.

An instrument is described for the continuous volume measurement of small (3.7 ml) samples of liquids as a function of temperature. The sample is sealed in a stainless steel bellows chamber. Volume changes are measured with a linear variable differential transformer while temperature is measured by means of a thermistor and associated circuitry. Volume changes can be determined to better than .1 microliter ml over a temperature range of 10-60 C. The maximum sample volume change is 7%.

Rothman, J. E.↗

NASA NTRSDate not supplied

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

cloud↗

DOE OSTI2022

Examining AGN UV/Optical Variability beyond the Simple Damped Random Walk

We present damped harmonic oscillator (DHO) light-curve modeling for a sample of 12,714 spectroscopically confirmed quasars in the Sloan Digital Sky Survey Stripe 82 region. DHO is a second-order continuous-time autoregressive moving-average process, which can be fully described using four independent parameters: a natural oscillation frequency (ω 0 ), a damping ratio (ξ), a characteristic perturbation timescale (τ perturb ), and an amplitude for the perturbing white noise (σ ϵ ). The asymptotic variability amplitude of a DHO process is quantified by σ DHO —a function of ω 0 , ξ, τ perturb , and σ ϵ . We find that both τ perturb and σ ϵ follow different dependencies with rest-frame wavelength (λ RF ) on either side of 2500 Å, whereas σ DHO follows a single power-law relation with λ RF . After correcting for wavelength dependence, σ DHO exhibits anticorrelations with both the Eddington ratio and the black hole mass, while τ perturb —with a typical value of days in the rest frame—shows an anticorrelation with the bolometric luminosity. Modeling active galactic nuclei (AGN) variability as a DHO offers more insight into the workings of accretion disks close to the supermassive black holes at the center of AGN. The newly discovered short-term variability (characterized by τ perturb and σ ϵ ) and its correlation with bolometric luminosity pave the way for new algorithms that will derive fundamental properties (e.g., Eddington ratio) of AGN using photometric data alone.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Radio-bright versus radio-dark gamma-ray bursts – more evidence for distinct progenitors

ABSTRACT We analyse two distinct samples of gamma-ray bursts (GRBs), with and without radio afterglow emission. We use an updated sample of 211 GRBs and find, in agreement with previous results (although with a sample that is almost twice as large), that the intrinsic gamma-ray duration (Tint) and isotropic equivalent energy (Eiso) distributions between these two populations appear to be significantly different. This implies that the radio-bright GRBs are more energetic and last longer than radio-dark GRBs. The two samples’ redshift distributions (z) are not statistically different. We analyse several correlations between variables (Eiso, Tint, jet opening angle, and z), accounting for selection effects and redshift evolution using the Efron–Petrosian method. We find a statistically significant anticorrelation between the jet opening angle and redshift, as well as between Tint and redshift, for both radio-bright and radio-dark GRBs. Finally, in agreement with previous work, we find that very high energy (0.1–100 GeV) extended emission is present only in the radio-bright GRB sample. Our work supports the possibility that the radio-bright and radio-dark GRBs originate from different progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

Reconstructing Subgrid Cloud Variability Guided by CloudSat/CALIPSO Observations

Predicting realistic cloud subgrid variability remains a challenge for Global Climate Models (GCMs) even though it can play an important role for proper representation of processes pertaining to cloud microphysics, precipitation, and radiation, but also for comparisons with satellite observations which are of much higher resolution than model grids. The diagnostic approach of subgrid cloud variability is commonly handled by subcolumn cloud generators. For a specific GCM, one ideally wants the subgrid variability used for comparisons with satellite observations to be created by the same generator and with the same rules as the one used for model integration. With this in mind, we have embarked in an effort to test and improve cloud subcolumn generators appropriate for GCMs. For this purpose, we use cloud (hydrometeor) products from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering signi􀂦cant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability. The main question is: given a profile of cloud condensate mean and variance, can we create a subgrid cloud field that is statistically similar to the observed subgrid cloud field? By “statistically”, we suggest that we do not aspire to reproduce “well” each individual subgrid cloud field of a GCM-scale region, but that our generator performs well for a large ensemble of cases. We simulate radar, passive imager, and radiation flux fields from the observed 2D cloud fields and create one-point statistics; we use the profiles of cloud condensate mean and variance as input to the generator to create subgrid cloud fields; we compare the statistics; we adjust the rules of the generator to create the best possible agreement between the radar, imager and radiation field statistics. In this process, the active observations have actually a dual role: they provide the actual subgrid cloud field which can be reduced to a profile of mean and variance used by the subcolumn generator, but they also provide the rules needed by the generator, such as measures (e.g, decorrelation length) of the vertical overlap of cloud fraction and of the condensate horizontal variability. This presentation will show our progress using Cloudsat and CALIPSO products in this dual fashion.

clouds↗

NASA NTRS2004

Transition from Forward Smoldering to Flaming in Small Polyurethane Foam Samples

Experimental observations are presented of the effect of the flow velocity and oxygen concentration, and of a thermal radiant flux, on the transition from smoldering to flaming in forward smoldering of small samples of polyurethane foam with a gas/solid interface. The experiments are part of a project studying the transition from smolder to flaming under conditions encountered in spacecraft facilities, i.e., microgravity, low velocity variable oxygen concentration flows. Because the microgravity experiments are planned for the International Space Station, the foam samples had to be limited in size for safety and launch mass reasons. The feasible sample size is too small for smolder to self propagate because of heat losses to the surrounding environment. Thus, the smolder propagation and the transition to flaming had to be assisted by reducing the heat losses to the surroundings and increasing the oxygen concentration. The experiments are conducted with small parallelepiped samples vertically placed in a wind tunnel. Three of the sample lateral-sides are maintained at elevated temperature and the fourth side is exposed to an upward flow and to a radiant flux. It is found that decreasing the flow velocity and increasing its oxygen concentration, and/or increasing the radiant flux enhances the transition to flaming, and reduces the delay time to transition. Limiting external ambient conditions for the transition to flaming are reported for the present experimental set-up. The results show that smolder propagation and the transition to flaming can occur in relatively small fuel samples if the external conditions are appropriate. The results also indicate that transition to flaming occurs in the char left behind by the smolder reaction, and it has the characteristics of a gas-phase ignition induced by the smolder reaction, which acts as the source of both gaseous fuel and heat.

Bar-Ilan, A.↗

DOE OSTI2018

Uncertainty, Sensitivity Analysis, and Causal Identification in the Arctic using a Perturbed Parameter Ensemble of the HiLAT Climate Model

Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.

54 ENVIRONMENTAL SCIENCES↗