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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Heterogeneous Distributions of Amino Acids Provide Evidence of Multiple Sources Within the Almahata Sitta Parent Body, Asteroid 2008 TC(sub 3)

Two new fragments of the Almahata Sitta meteorite and a sample of sand from the related strewn field in the Nubian Desert, Sudan, were analyzed for two to six carbon aliphatic primary amino acids by ultrahigh performance liquid chromatography with UV-fluorescence detection and time-of-flight mass spectrometry (LC-FT/ToF-MS). The distribution of amino acids in fragment #25, an H5 ordinary chondrite, and fragment #27, a polymict ureilite, were compared with results from the previously analyzed fragment #4, also a polymict ureilite. All three meteorite fragments contain 180-270 parts-per-billion (ppb) of amino acids, roughly 1000-fold lower than the total amino acid abundance of the Murchison carbonaceous chondrite. All of the Almahata Sitta fragments analyzed have amino acid distributions that differ from the Nubian Desert sand, which primarily contains L-alpha-amino acids. In addition, the meteorites contain several amino acids that were not detected in the sand, indicating that many of the amino acids are extraterrestrial in origin. Despite their petrological differences, meteorite fragments #25 and #27 contain similar amino acid compositions; however, the distribution of amino acids in fragment #27 was distinct from those in fragment #4, even though both arc polymict ureilites from the same parent body. Unlike in CM2 and CR2/3 meteorites, there are low relative abundances of alpha-amino acids in the Almahata Sitta meteorite fragments, which suggest that Strecker-type chemistry was not a significant amino acid formation mechanism. Given the high temperatures that asteroid 2008 TC3 appears to have experienced and lack of evidence for aqueous alteration on the asteroid, it is possible that the extraterrestrial amino acids detected in Almahata Sitta were formed by Fischer-Tropsch/Haber-Bosch type gas-grain reactions at elevated temperatures.

Burton, Aaron S.↗
NASA NTRS2016

The Foreign Clast Populations of Anomalous Polymict Urelite Almahata Sitta (Asteroid 2008 TC(sub3) and Typical Polymict Ureilites: Implications for Asteroid-Meteorite Connections

Almahata Sitta (AhS) is the first meteorite to originate from an asteroid (2008 TC3) that had been studied in space before it hit Earth [1,2]. It is also unique because the fallen fragments comprise a variety of types: approximately 69% ureilites (achondrites) and 31% chondrites [3]. Two models have been proposed for the origin 2008 TC3: 1) an accretionary model [3,4]; or 2) a regolith model [5,6]. Typical polymict ureilites are interpreted to represent regolith, and contain a few % foreign clasts [7,8]. The most common are dark (CC matrix-like) clasts similar to those in many meteoritic breccias [9]. A variety of other chondrites, as well as achondrites (angrites), have also been reported [7,9,10]. We have been working to determine the full diversity of these clasts [10-13] for comparison with AhS. We discuss implications for mixing of materials in the early solar system and the origin of 2008 TC3.

Goodrich, C. A.↗
NASA NTRSDate not supplied

Close out the Critical Commitment for System Wide Safety’s Technical Challenge 3 (TC 3)

- Deliver validated methods and recommended practices to reduce certification costs for ground and onboard vehicle guidance and control systems through expanded acceptability of analysis as evidence for certification FY 2022. - Communicate results to maximize public awareness of the research and transfer capabilities via a set of on-demand training seminars and supporting materials including reports, publications and open source software codes where applicable.

Joseph C Coughlan↗
NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗
NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗
NASA NTRSDate not supplied

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

This work is focused on the role of soil moisture in the prediction of tropical cyclones (TCs) approaching land and after landfall. Soil moisture conditions can impact the circulation and structure of an existing tropical cyclone (TC) when part or all of the circulation is over land. For example, dry land surface conditions may lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions may help sustain or in rare cases re-intensify a TC. Moreover, the presence of strong soil moisture gradients may affect the symmetry and development of the TC circulation leading to changes in its over-land track. While the link between soil moisture conditions and TC evolution in proximity to land is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tb observations significantly improves modeled land surface states. Thus, it is expected that SMAP can be used to constrain land surface initial conditions and potentially benefit TC forecasts. Here we present two sets of retrospective forecasts of TC Idai that are compared in an Observing System Experiment framework at ¼ degree resolution: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land using a weakly-coupled land analysis. We find that the assimilation of SMAP meaningfully improves the representation of TC Idai’s structure as well as the prediction of its intensity and track. The analyzed TC size, as measured by the wind speed radius, is improved by up to 18% in the analysis with SMAP assimilation relative to the control run. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. At the 1/4-degree resolution used here, GEOS unavoidably under-estimates TC intensity and over-estimates TC size. The SMAP assimilation therefore corrects the model in the right direction, leading to a storm that is more energetic and more compact. Furthermore, we find that the along-track forecast error is reduced by up to 34%, indicating a more accurate propagation speed, which is consistent with the fact that TC speed over land is strongly affected by surface processes. The impact of SMAP assimilation on the forecast cross-track error is neutral. Across the TC forecast skill metrics used here, the improvements from SMAP DA are largest at lead times of 36 to 72 hours, suggesting that the predictability of forecasts at shorter lead times may be dominated by short-term convective processes, while the land and its longer memory gains in importance as a source of predictability on a 2-3 day timescale. We further investigated the underlying mechanisms leading to the skill improvements from SMAP data assimilation by isolating the land areas that directly influence TC Idai using a back trajectory analysis. We find that the assimilation of SMAP leads to wetter soil moisture conditions that cause an increased latent heat flux, which ultimately results in TC analyzed representation that has higher column-integrated total moisture content and total energy compared to the analysis in the control run without SMAP assimilation. Overall, the results highlight that the assimilation of SMAP observations into a global numerical weather prediction model can lead to pronounced improvements of TC predictions. This is a crucial step towards a better mitigation of the socio-economic impact of landfalling TCs and thus safeguarding human lives. Finally, our study presents an event-based approach that assesses the impact of land data assimilation for a particular weather event rather than by globally averaging differences in skill. We argue that global skill assessments – while necessary – can mute the impact of land data assimilation, because the land’s influence on the atmosphere is constrained to certain locations and certain times. Instead, the event-based approach better highlights the true potential of land data assimilation in the context of NWP, especially for extreme events when accurate predictions are critical.

Jana Kolassa↗
NASA NTRSDate not supplied

A New ML-Based Adaptive Thinning Methodology to Improve the Impact of AIRS and CrIS Assimilation on Global Tropical Cyclone Forecasts

This work builds on previous research performed by this team to improve the forecast of Tropical Cyclones (TCs) by assimilating AIRS and CrIS radiances into the NASA Global Earth Observing System (GEOS). Past published work demonstrated that the assimilation of radiances with variable density was beneficial to TC forecasting in the GEOS. In the previous setup, a fixed-size moving square named 'TC domain' was activated by the so-called TC-vitals, an international real-time message accessible to all NWP forecasting centers, that documents the existence of a TC, its estimated position, and its size. The information from TC-vitals activated a switch in the GEOS, which allowed to reduce the distance used for thinning AIRS and CrIS data inside a 15 degrees by 15 degrees moving TC domain centered on the storm, so that more data were assimilated in the vicinity of the TC during its lifetime. The methodology produced improved TC analyses and led to better forecasts, particularly related to intensity, without damaging the global forecast skill. In the new version, the adaptive thinning methodology is based on a machine-learning technique. The technique searches for TCs and creates TC masks by using cloud-top temperatures from all geostationary satellites without the need for additional information. It is being trained against the International Best Track Archive for Climate Stewardship (IBTrACS) data base. Once a TC mask is created, a switch identical to the one used in the previous adaptive thinning method is activated, allowing the GEOS to ingest more data in the TC-shaped size-changing domain that follows the storm. As of today, the team has been able to successfully assimilate data inside the ML-detected TC domains. Future work includes an improved capability of reducing false alarm rates (i.e., cloud systems that are erroneously labeled as TCs).

Oreste Reale↗
NASA NTRS1997

Attachment of Free Filament Thermocouples for Temperature Measurements on CMC

Ceramic Matrix Composites (CMC) are being developed for use as enabling materials for advanced aeropropulsion engine and high speed civil transport applications. The characterization and testing of these advanced materials in hostile, high-temperature environments require accurate measurement of the material temperatures. Commonly used wire Thermo-Couples (TC) can not be attached to this ceramic based material via conventional spot-welding techniques. Attachment of wire TC's with commercially available ceramic cements fail to provide sufficient adhesion at high temperatures. While advanced thin film TC technology provides minimally intrusive surface temperature measurement and has good adhesion on the CMC, its fabrication requires sophisticated and expensive facilities and is very time consuming. In addition, the durability of lead wire attachments to both thin film TC's and the substrate materials requires further improvement. This paper presents a newly developed attachment technique for installation of free filament wire TC's with a unique convoluted design on ceramic based materials such as CMC's. Three CMC's (SiC/SiC CMC and alumina/alumina CMC) instrumented with type IC, R or S wire TC's were tested in a Mach 0.3 burner rig. The CMC temperatures measured from these wire TC's were compared to that from the facility pyrometer and thin film TC's. There was no sign of TC delamination even after several hours exposure to 1200 C. The test results proved that this new technique can successfully attach wire TC's on CMC's and provide temperature data in hostile environments. The sensor fabrication process is less expensive and requires very little time compared to that of the thin film TC's. The same installation technique/process can also be applied to attach lead wires for thin film sensor systems.

Lei, Jih-Fen↗
NASA NTRS2015

The Prospective Search for Highly Ionized Technetium in Hot (pre-) White Dwarfs

The discovery of technetium (Tc) in the atmospheres of red giants by Merrill (1952, ApJ, 116, 21) constituted convincing proof that s-process nucleosynthesis is indeed occurring in evolved stars. In principle, Tc should still be present in the atmospheres of hot post-AGB stars and (pre-) white dwarfs although, due to radioactive decay, it should be present in decreasing quantities along post-AGB evolution. The recent discovery of a large number of trans-iron group elements in hot white dwarfs with atomic numbers in the range A = 30-56 (Zn to Ba) raises the prospect that Tc (A = 43) may also be detected. However, this is currently not feasible because no atomic data exist for ionization stages beyond Tc ii. As an initial step, we calculated atomic energy levels and oscillator strengths of Tc iv-vi and used these data to compute non-local thermodynamic equilibrium (NLTE) model atmospheres to estimate at which minimum abundance level Tc could be detected. We show that Tc lines can be found in ultraviolet spectra of hot white dwarfs provided Tc is as abundant as other detected trans-Fe elements. We find that radiative levitation can keep Tc in large, easily detectable quantities in the atmosphere. A direct identification of Tc lines is still not feasible because wavelength positions cannot be computed with necessary precision. Laboratory measurements are necessary to overcome this problem. Our results suggest that such efforts are beneficial to the astrophysical community.

prospective↗
NASA NTRSDate not supplied

Assessing the Impact of SMAP Soil Moisture Data Assimilation on the Simulation and Prediction of Tropical Cyclone Idai

The role of soil moisture in the evolution of tropical cyclones (TCs) approaching land has long been recognized. Dry land surface conditions can lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions can help sustain or even re-intensify a TC. The ability to forecast post-landfall TC evolution and mitigate the associated socio-economic impact thus hinges on accurate knowledge of land surface conditions prior to landfall. The NASA Soil Moisture Active Passive (SMAP) mission provides accurate observations of soil moisture globally and at high revisit times of 2-3 days. It has been shown that the assimilation of SMAPbrightness temperatures (Tb) significantly improves modeled land surface states and thus has the potential to constrain land surface initial conditions in TC forecasts. In this presentation, we investigate this potential through an extensive set of Observing System Experiments that systematically assess the impact of assimilating SMAP Tbs on TC forecast skill in the Goddard Earth Observing System. Focusing on the case of TC Idai, we show that the assimilation of SMAP generally leads to a drier soil moisture analysis for the land surface underneath much of the storm’s circulation. An exception is the direct TC track, where SMAP assimilation increases soil moisture. These changes are reflected in the surface fluxes, which propagate the land surface state changes to the atmosphere. This results a TC that is overall weaker with lower winds, but with a better-defined eye and a slightly smaller track error than a simulation without SMAP DA. The changes in soil moisture resulting from the assimilation of SMAP also impact the total precipitation amounts as well as the precipitation structure associated with TC Idai.

SMAP↗
NASA NTRS2006

Thermal Conductivity of Ultem(TradeMark)/Carbon Nanofiller Blends

In an effort to improve polymer thermal conductivity (TC), Ultem(TradeMark) 1000 was compounded with nano-fillers of carbon allotropes. Ultem(TradeMark) 1000 was selected since it is both solution and melt processable. As-received and modified multiwalled carbon nanotubes (MWCNTs), vapor grown carbon nanofibers (CNF) and expanded graphite (EG) were investigated. MWCNTs were modified by functionalizing the surface through oxidization with concentrated acids, mixing with an alkyl bromide, and addition of alkyl and phosphorus compounds after initial treatment with n-butyl lithium. Functionalization was performed to improve the TC compatibility between the resin and MWCNTs. It was postulated that this may provide an improved interface between the MWCNT and the polymer which would result in enhanced TC. The nano-fillers were mixed with Ultem(TradeMark) 1000 in the melt and in solution at concentrations ranging from 5 to 40 wt%. Ribbons were extruded from the blends to form samples where the nano-fillers were aligned to some degree in the extrusion direction. Samples were also fabricated by compression molding resulting in random orientation of the nano-fillers. Thermal properties of the samples were evaluated by Differential Scanning Calorimetry (DSC) and Thermal Gravimetric Analyzer (TGA). Tensile properties of aligned samples were determined at room temperature. The specimens were cut from the ribbons in the extrusion direction; hence the nano-fillers are somewhat aligned in the direction of stress. Typically it was observed that melt mixed samples exhibited superior mechanical properties compared to solution mixed samples. As expected, increased filler loading led to increased modulus and decreased elongation with respect to the neat polymer. The degree of dispersion and alignment of the nano-fillers was determined by high-resolution scanning electron microscopy (HRSEM). HRSEM of the ribbons revealed that the MWCNTs and CNFs were predominantly aligned in the flow direction. The TC of the samples was measured using a Nanoflash(TradeMark) instrument. Since the MWCNTs and CNF are anisotropic, the TC was expected to be different in the longitudinal (parallel to the nanotube and fiber axis) and transverse (perpendicular to the nanotube and fiber axis) directions. The extruded ribbons provided samples for transverse TC measurements. However, to determine the TC in the longitudinal direction, the ribbons needed to be stacked and molded under 1.7 MPa and 270 C. Samples were then obtained by cutting the molded block with a diamond saw. The largest TC improvement was achieved for aligned samples when the measurement was performed in the direction of MWCNT and CNF alignment (i.e. longitudinal axis). Unaligned samples also showed a significant improvement in TC and may be potentially useful in applications when it is not possible to align the nano-filler. The results of this study will be presented.

Ghose, S.↗
NASA NTRS2007

Tropical Cyclones Feed More Heavy Rain in a Warmer Climate

The possible linkage of tropical cyclones (TC) to global warming is a hotly debated scientific topic, with immense societal impacts. Most of the debate has been focused on the issue of uncertainty in the use of non-research quality data for long-term trend analyses, especially with regard to TC intensity provided by TC forecasting centers. On the other hand, it is well known that TCs are associated with heavy rain during the processes of genesis and intensification, and that there are growing evidences that rainfall characteristics (not total rainfall) are most likely to be affected by global warming. Yet, satellite rainfall data have not been exploited in any recent studies of linkage between tropical cyclones (TC) and global warming. This is mostly due to the large uncertainties associated with detection of long-term trend in satellite rainfall estimates over the ocean. This problem, as we demonstrate in this paper, can be alleviated by examining rainfall distribution, rather than rainfall total. This paper is the first to use research-quality, satellite-derived rainfall from TRMM and GPCP over the tropical oceans to estimate shift in rainfall distribution during the TC season, and its relationships with TCs, and sea surface temperature (SST) in the two major ocean basins, the northern Atlantic and the northern Pacific for 1979-2005. From the rainfall distribution, we derive the TC contributions to rainfall in various extreme rainfall categories as a function to time. Our results show a definitive trend indicating that TCs are contributing increasingly to heavier rain events, i.e., intense TC's are more frequent in the last 27 years. The TC contribution to top 5% heavy rain has nearly doubled in the last two decades in the North Atlantic, and has increased by about 10% in the North Pacific. The different rate of increase in TC contribution to heavy rain may be related to the different rates of different rate of expansion of the warm pool (SST >2S0 C) area in the two oceans.

Lau, K.-M.↗