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NASA NTRS2023

Orbital Debris Shape Effect Investigations for Mitigating Risk

NASA’s Orbital Debris Program Office (ODPO) and Hypervelocity Impact Technology (HVIT) team have coordinated to better understand the risks to upper stages and spacecraft from non-spherical orbital debris. It is well understood that fragmentation (collision or explosion) events in orbit produce fragments of various materials, sizes, and shapes. To further characterize these parameters, the ODPO is developing the next-generation Orbital Debris Engineering Model (ORDEM) version 4.0 to include orbital debris shape distributions. Ground-based assets, such as radar and optical sensors, can provide size estimates and some insight into material based on radar return or optical filter photometry/spectroscopy, respectively. Characterizing an object’s shape requires more laboratory analyses to infer how shape affects these measurements. More importantly, in addition to size and material/density, the shape of fragments in orbit will alter the ballistic limit equations used in orbital debris risk assessments with NASA’s Bumper Code. The ODPO plans to release ORDEM 4.0 in the coming years. Performing ground-based laboratory impact tests on high-fidelity spacecraft mockups provides the means to directly measure size, mass, material/density, and shape of fragments, all key parameters needed to characterize real-world break up events. The DebriSat test, the results of which are provided, showcases the details of this type of experiment. The goal of this collaborative research between the ODPO and the HVIT team is to include a shape parameter in the environmental and breakup models used to assess risk for various space structures. This paper examines ground-based laboratory impact tests and the associated fragment shape categories. Provided these defined shapes, the approach is simplified by assuming a right circular cylinder (RCC) approximation with varying length-to-diameter ratios. Highlights of impact tests conducted by the HVIT team using non-spherical projectiles based on the RCC approximation are presented. Hydrocode simulations have also been performed to expand on the complexity of variations with non-spherical projectiles. Lastly, ray-tracing simulations of various RCCs of known material are provided to support the ongoing research on optical reflectance distributions with known shapes and to highlight how this may modify the current optical size estimation model. The status and plan forward are outlined for NASA's orbital debris shape effect investigation using a multidisciplinary approach by the ODPO and the HVIT team.

Heather Cowardin↗

DOE OSTI2023

Exposing new taxonomic variation with inflammation — a murine model-specific genome database for gut microbiome researchers

The murine CBA/J mouse model widely supports immunology and enteric pathogen research. This model has illuminated Salmonella interactions with the gut microbiome since pathogen proliferation does not require disruptive pretreatment of the native microbiota, nor does it become systemic, thereby representing an analog to gastroenteritis disease progression in humans. Despite the value to broad research communities, microbiota in CBA/J mice are not represented in current murine microbiome genome catalogs. Here we present the first microbial and viral genomic catalog of the CBA/J murine gut microbiome. Using fecal microbial communities from untreated and Salmonella-infected, highly inflamed mice, we performed genomic reconstruction to determine the impacts on gut microbiome membership and functional potential. From high depth whole community sequencing (~ 42.4 Gbps/sample), we reconstructed 2281 bacterial and 4516 viral draft genomes. Salmonella challenge significantly altered gut membership in CBA/J mice, revealing 30 genera and 98 species that were conditionally rare and unsampled in non-inflamed mice. Additionally, inflamed communities were depleted in microbial genes that modulate host anti-inflammatory pathways and enriched in genes for respiratory energy generation. Our findings suggest decreases in butyrate concentrations during Salmonella infection corresponded to reductions in the relative abundance in members of the Alistipes. Strain-level comparison of CBA/J microbial genomes to prominent murine gut microbiome databases identified newly sampled lineages in this resource, while comparisons to human gut microbiomes extended the host relevance of dominant CBA/J inflammation-resistant strains. This CBA/J microbiome database provides the first genomic sampling of relevant, uncultivated microorganisms within the gut from this widely used laboratory model. Using this resource, we curated a functional, strain-resolved view on how Salmonella remodels intact murine gut communities, advancing pathobiome understanding beyond inferences from prior amplicon-based approaches. Salmonella-induced inflammation suppressed Alistipes and other dominant members, while rarer commensals like Lactobacillus and Enterococcus endure. The rare and novel species sampled across this inflammation gradient advance the utility of this microbiome resource to benefit the broad research needs of the CBA/J scientific community, and those using murine models for understanding the impact of inflammation on the gut microbiome more generally.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1997

Parameterizing Grid-Averaged Longwave Fluxes for Inhomogeneous Marine Boundary Layer Clouds

This paper examines the relative impacts on grid-averaged longwave flux transmittance (emittance) for Marine Boundary Layer (MBL) cloud fields arising from horizontal variability of optical depth tau and cloud sides, First, using fields of Landsat-inferred tau and a Monte Carlo photon transport algorithm, it is demonstrated that mean all-sky transmittances for 3D variable MBL clouds can be computed accurately by the conventional method of linearly weighting clear and cloudy transmittances by their respective sky fractions. Then, the approximations of decoupling cloud and radiative properties and assuming independent columns are shown to be adequate for computation of mean flux transmittance. Since real clouds have nonzero geometric thicknesses, cloud fractions A'(sub c) presented to isotropic beams usually exceed the more familiar vertically projected cloud fractions A(sub c). It is shown, however, that when A(sub c)less than or equal to 0.9, biases for all-sky transmittance stemming from use of A(sub c) as opposed to A'(sub c) are roughly 2-5 times smaller than, and opposite in sign to, biases due to neglect of horizontal variability of tau. By neglecting variable tau, all-sky transmittances are underestimated often by more than 0.1 for A(sub c) near 0.75 and this translates into relative errors that can exceed 40% (corresponding errors for all-sky emittance are about 20% for most values of A(sub c). Thus, priority should be given to development of General Circulation Model (GCM) parameterizations that account for the effects of horizontal variations in unresolved tau, effects of cloud sides are of secondary importance. On this note, an efficient stochastic model for computing grid-averaged cloudy-sky flux transmittances is furnished that assumes that distributions of tau, for regions comparable in size to GCM grid cells, can be described adequately by gamma distribution functions. While the plane-parallel, homogeneous model underestimates cloud transmittance by about an order of magnitude when 3D variable cloud transmittances are less than or equal to 0.2 and by approx. 20% to 100% otherwise, the stochastic model reduces these biases often by more than 80%.

Barker, Howard W.↗

NASA NTRSDate not supplied

Using Secondary Craters to Assess Strength Differences Between Neighboring Surface Units

Introduction: An issue with Martian remote sensing studies is the difficulty directly inferring surface and crustal material properties. For direct measurements we currently rely on the instruments of landed missions, like InSight. However, these are limited to specific locations (i.e., Elysium Planitia for InSight). To understand how results from in situ missions apply to other parts of Mars’ crust, we would like to apply a methodology that uses remote sensing. We are exploring using secondary craters, which are often thought of as nuisances when finding accurate relative ages, to get at these properties. An upside of secondary craters is that they form by ejecta blocks moving at relatively slow speeds (compared to primary impacts), which lets them reside in a different crater scaling regime [1], one that is more sensitive to the crust’s effective strength (Yeff) and its porosity. Secondary Crater Scaling: There are multiple parameters that influence the size of secondary craters, but we can measure craters in ways that isolate the dependence of crater size to just Yeff. We focus on secondary clusters that have impacted into distinct but bordering geological units. These secondaries can be used to determine Yeff thanks to one important fact: all the secondaries in this cluster are from the same distant primary, and as a result, the ejecta velocity, median block sizes, and densities are expected to be the same (at least to first order). Thus, variations between crater size on the two units are due to material property differences (i.e. Yeff). Thanks to the steep size frequency distributions (SFD) of secondaries [2], observable secondary crater sizes are tightly clustered, and we can simply use differences in median secondary sizes between two geologic units to constrain the ratio of Yeff. We are also able to use the SFD of our counting areas to check if our counted craters are secondaries (i.e. have a steep distribution), and if the two crater counting areas are from the same source (and thus have the same power law slope), but are offset from each other at around the same factor as the median difference.

Mars; Craters; Crustal Strength↗

DOE OSTI2023

Quantifying the Variation in the Number of Donors in Quantum Dots Created Using Atomic Precision Advanced Manufacturing

Atomic-precision advanced manufacturing enables unique silicon quantum electronics built on quantum dots fabricated from small numbers of phosphorus dopants. The number of dopant atoms comprising a dot plays a central role in determining the behavior of charge and spin confined to the dots and thus overall device performance. Here in this work, we use both theoretical and experimental techniques to explore the combined impact of lithographic variation and stochastic kinetics on the number of P incorporations in quantum dots made using these techniques and how this variation changes as a function of the size of the dot. Using a kinetic model of PH3 dissociation augmented with novel reaction barriers, we demonstrate that for a 2 × 3 silicon dimer window the probability that no donor incorporates goes to zero, allowing for certainty in the placement of at least one donor. However, this still comes with some uncertainty in the precise number of incorporated donors (either one or two), and this variability may still impact certain applications. We also examine the impact of the size of the initial lithographic window, finding that the incorporation fraction saturates to δ-layer-like coverage as the circumference-to-area ratio decreases. We predict that this incorporation fraction depends strongly on the dosage of the precursor and that the standard deviation of the number of incorporations scales as ~√n, as would be expected for a sequence of largely independent incorporation events. Finally, we characterize an array of 36 experimentally prepared multidonor 3 × 3 nm lithographic windows with scanning tunneling microscopy, measuring the fidelity of the lithography to the desired array and the final location of PH x fragments within these lithographic windows. We use our kinetic model to examine the expected variability due to the observed lithographic error, predicting a negligible impact on incorporation statistics. We find good agreement between our model and the inferred incorporation locations in these windows from scanning tunneling microscope measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy

Abstract Archaeal anaerobic methanotrophs (“ANME”) and sulfate-reducing Deltaproteobacteria (“SRB”) form symbiotic multicellular consortia capable of anaerobic methane oxidation (AOM), and in so doing modulate methane flux from marine sediments. The specificity with which ANME associate with particular SRB partners in situ, however, is poorly understood. To characterize partnership specificity in ANME-SRB consortia, we applied the correlation inference technique SparCC to 310 16S rRNA amplicon libraries prepared from Costa Rica seep sediment samples, uncovering a strong positive correlation between ANME-2b and members of a clade of Deltaproteobacteria we termed SEEP-SRB1g. We confirmed this association by examining 16S rRNA diversity in individual ANME-SRB consortia sorted using flow cytometry and by imaging ANME-SRB consortia with fluorescence in situ hybridization (FISH) microscopy using newly-designed probes targeting the SEEP-SRB1g clade. Analysis of genome bins belonging to SEEP-SRB1g revealed the presence of a complete nifHDK operon required for diazotrophy, unusual in published genomes of ANME-associated SRB. Active expression of nifH in SEEP-SRB1g within ANME-2b—SEEP-SRB1g consortia was then demonstrated by microscopy using hybridization chain reaction (HCR-) FISH targeting nifH transcripts and diazotrophic activity was documented by FISH-nanoSIMS experiments. NanoSIMS analysis of ANME-2b—SEEP-SRB1g consortia incubated with a headspace containing CH4 and 15N2 revealed differences in cellular 15N-enrichment between the two partners that varied between individual consortia, with SEEP-SRB1g cells enriched in 15N relative to ANME-2b in one consortium and the opposite pattern observed in others, indicating both ANME-2b and SEEP-SRB1g are capable of nitrogen fixation, but with consortium-specific variation in whether the archaea or bacterial partner is the dominant diazotroph.

58 GEOSCIENCES↗

NASA NTRS2019

Evaluation of the Impacts of Biomass Burning Aerosols on the NASA GEOS Sub-Seasonal Climate Forecasts

Emission sources of trace gases and aerosol particles in the South American (SA)and African (Af) continents have a strong seasonal and space variability associated with the extensive vegetation fires activities. In both continents, during the austral winter, the fires affect mainly tropical forest and savannah-type biomes and are mostly associated with deforestation and agricultural/pasture land management. Smoke aerosol particles, on average, contribute to at least 90% of the total aerosol optical depth (AOD) in the visible spectrum in the case of the South America regional smoke. Smoke aerosols also act as cloud condensation nuclei affecting cloud microphysics properties and therefore, changing the radiation budget, hydrological cycle and global circulation patterns over disturbed areas (Kaufman, 1995; Rosenfeld, 1999; Andreae,et al., 2004; Koren et al., 2004, Zhang, 2008; Ott et al., 2010; Randles et al., 2013). This study aims to evaluate and quantify the impact of including a comprehensive emission field of biomass burning aerosol on the performance of a seasonal climate forecast system, not only regarding the AOD itself but mainly on the meteorological state variable (e.g., precipitation and temperature). To address the questions put above, we designed two numerical experiments: 1- named"AERO_CTL" which applies the Quick Fire Emissions Dataset (QFED) emissions estimated with intra-diurnal variation (hereafter, BBE), and 2- named "AERO_CLM" where the sourcee mission is based on a climatology of the QFED emissions, with only monthly variation(hereafter, BBCLIM). Hindcast simulations were produced using the Goddard Earth ObservingSystem global circulation model, version 5, sub-seasonal to seasonal (GEOS5-S2S) system with a nominal spatial resolution of 56km (Rienecker et al., 2008). In both experiments, the aerosol feedbacks from cloud developments and radiation interactions were accounted. The two experiments consisted of 4 members each and ran from June to November spanning over the years 2000 to 2015. Model performance was evaluated by calculating statistical metrics on the mean area of SA and Af. Our results demonstrated that the skill model in predicting AOD is significantly improve when BBE source emission is applied over SA, but not over the Afcontinent. Over SA, the correlation between the AERO_CTL model configuration and MERRA-2 is 0.93 (R2= 0.86, RMS=0.02, BIAS=0.01), while the AERO_CLM model presents a value of0.81 (R2= 0.65, RMS=0.04, BIAS=0.06). However, the AERO_CTL experiment better represents the inter-annual variability of the AOS in both regions. The gain of the skill in predicting the AOD by the AERO_CTL experiment is also seen in some meteorological variables. We observed an increase in the model skill in predicting the 2-meter temperature and precipitation of up to 0.3 for the AERO_CTL experiment in comparison to the AERO_CLM. AERO_CLM. According to the analyzed hindcast, we inferred that representing the BBE more realistically implies in a significant gain of skills in the seasonal climate forecasting over SA and Af continents.

Aerosol↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

Saik Anam Siam↗

NASA NTRS2002

Clementine Observations of the Zodiacal Light and the Dust Content of the Inner Solar System

Using the Moon to occult the Sun, the Clementine spacecraft used its navigation cameras to map the inner zodiacal light at optical wavelengths over elongations of 3 approx. less than epsilon approx. less than 30 deg from the Sun. This surface brightness map is then used to infer the spatial distribution of interplanetary dust over heliocentric distances of about 10 solar radii to the orbit of Venus. The averaged ecliptic surface brightness of the zodiacal light falls off as Z(epsilon) is a member of epsilon(sup -2.45 +/- 0.05), which suggests that the dust cross-sectional density nominally falls off as sigma(r) is a member of r(sup - 1.45 +/- 0.05). The interplanetary dust also has an albedo of alpha approx. = 0.1 that is uncertain by a factor of approx. 2. Asymmetries of approx. 10% are seen in directions east-west and north-south of the Sun, and these may be due the giant planets' secular gravitational perturbations. We apply a simple model that attributes the zodiacal light as due to three dust populations having distinct inclination distributions, namely, dust from asteroids and Jupiter-family comets (JFCs) having characteristic inclinations of i approx. 7 deg, dust from Halley-type comets having i approx. 33 deg, and an isotropic cloud of dust from Oort Cloud comets. The best-fitting scenario indicates that asteroids + JFCs are the source of about 45% of the optical dust cross section seen in the ecliptic at 1 AU but that at least 89% of the dust cross section enclosed by a 1-AU-radius sphere is of a cometary origin. Each population's radial density variations can also deviate somewhat from the nominal sigma(r) is a member of r(sup -1.45). When these results are extrapolated out to the asteroid belt, we find an upper limit on the mass of the light-reflecting asteroidal dust that is equivalent to a 12-km asteroid, and a similar extrapolation of the isotropic dust cloud out to Oort Cloud distances yields a mass equivalent to a 30-km comet, although the latter mass is uncertain by orders of magnitude.

Hahn, Joseph M.↗

NASA NTRS2013

The Complex Spin State of 103P-Hartley 2: Kinematics and Orientation in Space

We derive the spin state of the nucleus of Comet 103P/Hartley 2, its orientation in space, and its short-term temporal evolution from a mixture of observations taken from the DIXI (Deep Impact Extended Investigation) spacecraft and radar observations. The nucleus is found to spin in an excited long-axis mode (LAM) with its rotational angular momentum per unit mass, M, and rotational energy per unit mass, E, slowly decreasing while the degree of excitation in the spin increases through perihelion passage. M is directed toward (RA, Dec; J2000) = 8+/-+/- 4 deg., 54 +/- 1 deg. (obliquity = 48 +/- 1 deg.). This direction is likely changing, but the change is probably <6 deg. on the sky over the approx. 81.6 days of the DIXI encounter. The magnitudes of M and E at closest approach (JD 2455505.0831866 2011-11-04 13:59:47.310) are 30.0 +/- 0.2 sq. m/s and (1.56 +/- 0.02) X 10(exp -3) sq. m /sq. s respectively. The period of rotation about the instantaneous spin vector, which points in the direction (RA, Dec; J2000) = 300 +/- 3.2deg., 67 +/- 1.3 deg. at the time of closest approach, was 14.1 +/- 0.3 h. The instantaneous spin vector circulates around M, inclined at an average angle of 33.2 +/- 1.3 deg. with an average period of 18.40 +/- 0.13 h at the time of closest approach. The period of roll around the principal axis of minimum inertia (''long'' axis) at that time is 26.72 +/- 0.06 h. The long axis is inclined to M by approx. 81.2 +/- 0.6 deg. on average, slowly decreasing through encounter. We infer that there is a periodic nodding motion of the long axis with half the roll period, i.e., 13.36+/- 0.03 h, with amplitude of 1 again decreasing through encounter. The periodic variability in the circulation and roll rates during a cycle was at the 2% and 10-14% level respectively. During the encounter there was a secular lengthening of the circulation period of the long axis by 1.3 +/- 0.2 min/d, in agreement with ground-based estimates, while the period of roll around the long axis changed by approx. -4.4 min/d at perihelion. M decreased at a rate of 0.038 (sq m/s) per day in a roughly linear fashion. Assuming a bulk density between 230-300 kg/m3 and a total volume for the nucleus of 8.09 X 10(exp 8) cubic m, the net torque acting on the nucleus was in the range 0.8-1.1 X 10(exp 5) kg m(exp 2) /s(exp 2). In order to bring the spacecraft photometric and imaging data into alignment on the direction of M, the directions of the intermediate and short principal axes of inertia had to be adjusted by 33 deg (on the sky) from the values indicated by the shape model with an assumed homogeneous interior. The adjusted direction of the intermediate axis is RA, Dec = 302 deg., -16.5 deg.. The morning and evening terminators in the images are identified, and the variation of the insolation at three regions on the nucleus associated with active areas calculated. The plume of water vapor observed in the inner coma is found to be directed close to the direction of local gravity over the sub-solar region for a range of reasonable bulk densities. The plume does not follow the projected normal to the surface at the sub-solar point.

Comets↗

NASA NTRS1994

Highly ionized gas in the Galactic disk: The l = 295 deg, b,= 0 deg interarm direction

I present new high-resolution measurements of ultraviolet interstellar absorption lines toward three distant stars in the Galactic disk in the general direction l = 295 deg, b = 0 deg. The high ionization lines of Si IV and C IV toward HD 100276, HD 103779, and HD 104705 show substantial variations on angular scales of less than 1 deg, or about 50-100 pc at the distance of the stars. The Si IV and C IV midplane densities derived for HD 100276 and HD 103779 are N(sub o) (Si IV) approximately = 1.3 x 10(exp -9) cm(exp -3) and n(sub o)(C IV) approximately = 4.5 x 10(exp -9) cvm(exp -3), approximately a factor of 2-3 lower than the Galactic averages. Toward HD 104705, the Si IV midplane density is near the average value, but the C IV midplane density is a factor of 3 higher than average. The integrated C IV and Si IV ratios along the HD 100276 and HD 100379 sight lines are similar to the N(C IV)/N(Si IV) = 3.6 +/- 1.3 ratio for a sample of halo stars. For the HD 104705 sight line, N(C IV)/N(Si IV) = 11 and N(C IV)/N(N V) greater than 10. Turbulent mixing layers provide a possible source for the observed absorption along the three sight lines as does gas associated with cooling flows. If mixing layers are the primary source of the absorption along the disk sight lines, then the entrainment velocity of the hot gas is approximately 100 km/s and the post mixed gas temperature is approximately 2 x 10(exp 5) K; same differences may exist for these mixing layers compared to those inferred for gas toward the Galactic poles. Current models of cooling, hot gas in supernova remnants, and magnetized conduction fronts do not accurately describe the high ion properties toward the disk stars in this study.

Sembach, Kenneth R.↗

NASA NTRS2004

Ground Ice at the Phoenix Landing Site: A Preflight Assessment

One of the objectives of the Mars Scout mission, Phoenix, is to characterize the present state of water in the martian environment, in a location where water may play a significant role in the present and past habitability of Mars. Given the generally dry and cold climate of Mars today any substantial amount of water is expected to occur in the form of ground ice (subsurface ice) within the regolith. The Mars Odyssey Gamma Ray Spectrometer has indicated abundant subsurface hydrogen and inferred ground ice at high latitudes. Therefore, the Phoenix mission will be targeted to land in the northern high latitudes (approximately 65 degrees N - 75 degrees N) where ground ice is expected to be abundantly available for analysis. The lander will be capable of excavating, sampling, and analyzing, dry and water-rich/icy soils. The location and depth of excavation necessary to achieve the goals of sampling and analysis of icy material become important parameters to assess. In the present work we ask two key questions: 1) At what depth within the regolith do we expect to find ice? 2) How might this depth vary over the region of potential landing sites? Numerous lines of evidence can be employed to provide an indication of the presence or absence of shallow ground ice at the potential landing sites. For example geomorphology, neutrons, gamma rays, and theory each contribute clues to an overall understanding of the distribution of ice. Orbital observations provide information on a variety of spatial scales, typically 10 s of meters (patterned ground) to 100 s of kilometers (gamma rays). While information on all of these scales are important, of particular interest is how the presence and depth of ground ice might vary on spatial scales comparable to the lander and its work area. While ground ice may be stable (and present) on a regional scale, local-scale slopes and changes in the physical characteristics of soils can result in significant variations in the distribution of ice.

Mellon, M. T.↗

DOE OSTI2024

Recommendations on setup in simulating atmospheric gravity waves under conventionally neutral boundary layer conditions

Wind farm-induced atmospheric gravity waves have been the subject of recent research as they can impact wind farm performance. Pressure variations associated with gravity waves can contribute to the global blockage effect and wind farm wake recovery. Therefore, accurate numerical simulation of flow fields, where wind-farm-induced gravity waves may be produced, is important. Three main considerations in such simulations are the overall domain size, the use of Rayleigh damping near domain boundaries to dampen gravity waves, and advection damping at the inlet to prevent spurious oscillations. Often these considerations are treated ad hoc rather than systematically. This work aims to test and extend the systematic modelling of internal gravity waves proposed in a preliminary investigation to modelling of both internal and trapped gravity waves. The preliminary study identifies the length scales to set the domain and damping layer sizes and the time scale to configure the Rayleigh damping coefficient but under linearly stratified conditions. Large eddy simulations of flow through a wind farm canopy are performed under conventionally neutral boundary layer (CNBL) conditions to test the validity of proposed setups for CNBL conditions. Background atmospheric parameters, such as Froude number (Fr), inversion height (H i ), and inversion layer Froude number (Fr i ) control most of the atmospheric gravity wave characteristics. We validated for CBNL conditions that the effective wavelengths of the internal gravity waves are the correct length scale to configure the domain size and damping layer thickness. Likewise, the optimum damping coefficient to dampen the internal gravity waves relates to the free atmosphere's buoyancy frequency or buoyant perturbations' time scale. We infer that the damping coefficient in the inversion layer may relate to the inversion buoyancy frequency to effectively dampen the trapped gravity waves. Moreover, the advection damping length is linked to the horizontal wavelength of the trapped gravity waves in the inversion layer to prevent spurious waves at the inlet by retaining wave energy accumulation.

17 WIND ENERGY↗

DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗

NASA NTRS2001

Sensitivity of the Tropical Atmosphere Energy Balance to ENSO-Related SST Changes: How Well Can We Quantify Hydrologic and Radiative Responses?

The continuing debate over feedback mechanisms governing tropical sea surface temperatures (SSTs) and tropical climate in general has highlighted the diversity of potential checks and balances within the climate system. Competing feedbacks due to changes in surface evaporation, water vapor, and cloud long- and shortwave radiative properties each may serve critical roles in stabilizing or destabilizing the climate system. It is also intriguing that even those climate variations having origins internal to the climate system-- changes in ocean heat transport for example, apparently require complementary equilibrating effects by changes in atmospheric energy fluxes. Perhaps the best observational evidence of this is the relatively invariant nature of tropically averaged net radiation exiting the top-of-atmosphere (TOA) as measured by broadband satellite sensors over the past two decades. Thus, analyzing how these feedback mechanisms are operating within the context of current interannual variability may offer considerable insight for anticipating future climate change. In this paper we focus on how fresh water and radiative fluxes over the tropical oceans change during ENSO warm and cold events and how these changes affect the tropical energy balance. At present, ENSO remains the most prominent known mode of natural variability at interannual time scales. Although great advances have been made in understanding this phenomenon and realizing prediction skill over the past decade, our ability to document the coupled water and energy changes observationally and to represent them in climate models seems far from settled (Soden, 2000 J Climate). Our analysis makes use a number of data bases, principally those derived from space-based measurements, to explore systematic changes in rainfall, evaporation, and surface and top-of-atmosphere (TOA) radiative fluxes, A reexamination of the Langley 8-Year Surface Radiation Budget data set reveals errors in the surface longwave emission due to use of biased SSTs. Subsequent correction allows subsequent use of this data set along with ERBE TOA fluxes to infer net atmospheric radiative beating. Further analysis of recent rainfall algorithms provides new estimates for precipitation variability in line with interannual evaporation changes inferred from the da Silva, Young, Levitus COADS analysis. The overall results from our analysis suggest an increase (decrease) of the hydrologic cycle during ENSO warm (cold) events at the rate of about 5 Wm-2 per K of SST change. This rate is slightly less than that which would be expected for constant relative humidity over the tropical oceans. Corresponding radiative fluxes seem systematically smaller resulting in a enhanced (suppressed) export of energy from the tropical ocean regions during warm (cold) SST events. Discussion of likely errors due to sampling and measurement strategies are discussed along with their impacts on our conclusions.

Robertson, Franklin R.↗

NASA NTRS2021

A theoretical assessment of the feasibility of potential Lunar Reconnaissance Orbiter radio occultation observations of the lunar ionosphere

The existence of a “dense” lunar ionosphere has been controversial for decades. Positive ions produced from the lunar surface and exosphere are inferred to have densities that are.106– 107m−3near the surface and smaller at higher altitudes, yet electron densities derived from radio occultation measurements occasionally exceed these values by orders of magnitude. For example, about 4% of the single-spacecraft radio occultation measurements from Kaguya/SELENE were consistent with peak electron densities of∼3×108m−3. Space plasmas should be neutral on macroscopic scales, so this represents a substantial discrepancy. Additional observations of electron densities in the lunar ionosphere are critical to resolving this long standing paradox. Here we theoretically assess whether radio occultation observations using two-way coherent S-band radio signals from the Lunar Reconnaissance Orbiter (LRO) spacecraft could provide useful measurements of electron densities in the lunar ionosphere. We predict the uncertainty in a single LRO radio occultation measurement of electron density to be∼3×108m−3, comparable to occasional observations by Kaguya/SELENE of a dense lunar ionosphere. Thus an individual profile from LRO is unlikely to reliably detect the lunar ionosphere; however, averages of multiple (∼10) LRO profiles acquired under similar geophysical and viewing conditions should be able to make reliable detections. An observing rate of six ingress occultations per day (∼2000 per year) could be achieved with mini-mal impact on current LRO operations. This rate compares favorably with the 378observations reported from the single-spacecraft experiment on Kaguya/SELENE between November 2007 and June 2009. The large number of observations possible for LRO would be sufficient to permit wide-ranging investigations of spatial and temporal variations in the poorly understood lunar ionosphere. These findings strengthen efforts to conduct such observations with LRO.

Paul Withers↗