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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Does Aspartic Acid Racemization Constrain the Depth Limit of the Subsurface Biosphere?

Previous studies of the subsurface biosphere have deduced average cellular doubling times of hundreds to thousands of years based upon geochemical models. We have directly constrained the in situ average cellular protein turnover or doubling times for metabolically active micro-organisms based on cellular amino acid abundances, D/L values of cellular aspartic acid, and the in vivo aspartic acid racemization rate. Application of this method to planktonic microbial communities collected from deep fractures in South Africa yielded maximum cellular amino acid turnover times of approximately 89 years for 1 km depth and 27 C and 1-2 years for 3 km depth and 54 C. The latter turnover times are much shorter than previously estimated cellular turnover times based upon geochemical arguments. The aspartic acid racemization rate at higher temperatures yields cellular protein doubling times that are consistent with the survival times of hyperthermophilic strains and predicts that at temperatures of 85 C, cells must replace proteins every couple of days to maintain enzymatic activity. Such a high maintenance requirement may be the principal limit on the abundance of living micro-organisms in the deep, hot subsurface biosphere, as well as a potential limit on their activity. The measurement of the D/L of aspartic acid in biological samples is a potentially powerful tool for deep, fractured continental and oceanic crustal settings where geochemical models of carbon turnover times are poorly constrained. Experimental observations on the racemization rates of aspartic acid in living thermophiles and hyperthermophiles could test this hypothesis. The development of corrections for cell wall peptides and spores will be required, however, to improve the accuracy of these estimates for environmental samples.

cellular↗
NASA NTRS2016

Validating MODIS Above-Cloud Aerosol Optical Depth Retrieved from Color Ratio Algorithm Using Direct Measurements Made by NASA's Airborne AATS and 4STAR Sensors

We present the validation analysis of above-cloud aerosol optical depth (ACAOD) retrieved from the color ratio method applied to MODIS cloudy-sky reflectance measurements using the limited direct measurements made by NASAs airborne Ames Airborne Tracking Sunphotometer (AATS) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) sensors. A thorough search of the airborne database collection revealed a total of five significant events in which an airborne sun photometer, coincident with the MODIS overpass, observed partially absorbing aerosols emitted from agricultural biomass burning, dust, and wildfires over a low-level cloud deck during SAFARI-2000, ACE-ASIA 2001, and SEAC4RS 2013 campaigns, respectively. The co-located satellite-airborne match ups revealed a good agreement (root-mean-square difference less than 0.1), with most match ups falling within the estimated uncertainties associated with the MODIS retrievals (about -10 to +50 ). The co-retrieved cloud optical depth was comparable to that of the MODIS operational cloud product for ACE-ASIA and SEAC4RS, however, higher by 30-50% for the SAFARI-2000 case study. The reason for this discrepancy could be attributed to the distinct aerosol optical properties encountered during respective campaigns. A brief discussion on the sources of uncertainty in the satellite-based ACAOD retrieval and co-location procedure is presented. Field experiments dedicated to making direct measurements of aerosols above cloud are needed for the extensive validation of satellite based retrievals.

airborne sun photometer↗
NASA NTRS2017

Intercomparison of Snow Depth Retrievals over Arctic Sea Ice from Radar Data Acquired by Operation IceBridge

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim (ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis) reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Operation IceBridge↗
NASA NTRS2021

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed from CALIPSO: A Neural Network Approach

An innovative neural network retrieval algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS(AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurement of 2015 also compare with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission –Water "Shizuku"(GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind –surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from Hu et al. (2008), as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

wind aerosols CALIPSO↗
NASA NTRS2019

Point-to-CAD 3D registration algorithm for relative navigation using depth-based maps

This paper presents an end-to-end 3D registration algorithm for relative navigation between known objects based on a point-to-CAD iterative closest point (ICP) principle. The objective of this method is to take in a measured point cloud extracted from a depth or disparity map – such as the ones obtained from stereo cameras, time-of-flight cameras, LiDARs, or depth from defocus sensors – and calculate the rigid body transformation that best aligns the measured data with a corresponding 3D CAD model.

Setterfield, Timothy P.↗
NASA NTRS2021

The Thermal Infrared Optical Depth of Mineral Dust Retrieved from Integrated CALIOP and IIR Observations

Recent studies reveal that a higher fraction of coarse mineral dust particles than that estimated by climate model simulations has been observed in the atmosphere, leading to a more significant positive (i.e., warming) longwave (LW) thermal infrared (TIR) direct radiative effect (DRE). However, the magnitude of this DRE remains highly uncertain because our understanding of the radiative properties, quantitatively represented by the optical depth of dust, especially information on the TIR, remains limited. This study presents a simple approach to retrieve the thermal infrared dust aerosol optical depth (DAODTIR) over oceans during nighttime using the observations from the Infrared Imaging Radiometer (IIR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO. For each cloud-free dust-laden profile identified by the IIR-CALIOP observation, a Lookup-Table (LUT) of the 10.6 μm IIR band brightness temperatures difference (dBT) under different DAODTIR with respect to their dust-free BTs is constructed based on the CALIOP retrieved dust vertical profile and pre-assumed dust scattering properties using a fast radiative transfer model. Then the DAODTIR is retrieved by projecting the IIR-observed dBT on the LUT. Sensitivity studies show that the DAODTIR retrieval at 10.6 μm is more susceptible to the dust particle size distribution (PSD) assumption than dust refractive indices. To estimate the uncertainty caused by PSD assumption, two DAODTIR retrieval products, one based on the dust PSD from the AERONET at Cape Verde and the other on an in situ measured PSD from the recent Fennec campaign, are provided. The retrieval uncertainty is mainly contributed by the BT difference between the observation and simulation using auxiliary atmospheric data. The climatology of the retrieval from 2013 to 2019 shows confident spatiotemporal variations of DAODTIR with the global-averaged value of 0.006 and 0.008 based on different pre-assumed dust PSDs. Climatological results agree reasonably well with two independent DAODTIR retrieval products based on the Infrared Atmospheric Sounding Interferometer (IASI) over the active dust transport regions, such as North and Tropical Atlantic (r = 0.904 and 0.819) and Indian Ocean (r = 0.832). The seasonal and interannual variation is also well-compared (r = 0.758) with AERONET coarse-mode AOD at 97 selected sites. The synergic CALIOP observation allows the retrieved DAODTIR to directly compare with the extrapolated DAODTIR from DAOD in visible (i.e., 532 nm), which helps evaluate the observational constraints on DAODTIR. This study offers a unique prospect of collocating active lidar and passive IR observations for retrieving dust DAODTIR.

aerosol↗
NASA NTRSDate not supplied

Column Optical Depth (COD) Derived from CALIOP Ocean Surface Returns

The Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite has been making near-global measurements of clouds and aerosols since mid-June 2006. Among the properties reported in CALIPSO data products are estimates of total column optical depth (COD) obtained by integrating the retrieved extinction coefficients of detected particulates (i.e., aerosols and clouds) from 36 km to the surface. However, due to algorithm detection limits and instrument sensitivities, particulates can go undetected when the particulate loading is especially diffuse or in regions where the signal is attenuated by over-lying layers. Because extinction is not calculated where features are not detected, these undetected particulates can introduce low biases into the reported COD. Lidar ratio assumptions used in the extinction retrievals can also introduce errors. To minimize these biases, future releases of the CALIPSO lidar data products will implement an ocean-derived column optical depth (ODCOD) retrieval. The following paper briefly describes the algorithm, the current status of the algorithm development and verification efforts, and preliminary comparisons to col-located COD estimates derived using other retrieval schemes and obtained from other sensors.

Robert A Ryan↗
NASA NTRSDate not supplied

An Initial Assessment of Variable Depth Liner Optimization for Ducted Proprotor Applications

The rise of the Urban Air Mobility market has spurred the design of a new generation of novel aircraft. To aid industry and researchers interested in these types of aircraft, the Revolutionary Vertical Lift Technology project at NASA has developed a fleet of reference vehicles for system studies. A new six-passenger reference vehicle has recently been added to the research portfolio that has ducted proprotors for propulsors. The ducts present an opportunity to apply acoustic treatment to the interior of the duct that could target both tonal and broadband noise, representative of the sound produced by this type of propulsor. In this paper, design methodologies are presented to design a variable depth liner for this application. An optimizer is used to design multiple liners with variable chamber depths. Experimental results from normal impedance testing are compared to numerical predictions using the optimizer model and a finite element model. Results show that an optimizer can be used to design an acoustic liner with favorable performance for a broad range of frequencies, which could be appropriate for a ducted proprotor application.

Matthew B Galles↗
NASA NTRSDate not supplied

Using CALIPSO's New Ocean Derived Column Optical Depths

CALIPSO’s Version 4.51 Level 2 data release introduces an all-new group of science data sets containing estimates of total column two-way transmittances and effective optical depths derived from CALIOP ocean surface backscatter measurements and MERRA-2 reanalysis wind speed data. These estimates use data from the standard CALIOP lidar signal but in a passive sensor-like way, thus creating a unique link to passive instrument measurements. These new retrievals are provided for the entire mission, day and night, at 532nm, and are reported at single shot, 1km, and 5km resolutions for all profiles in which a valid lidar ocean surface return is detected. The addition of a total column optical depth constraint on subsequent retrievals of cloud and aerosol optical properties opens the door for many exciting new ways to leverage the already rich and versatile CALIPSO dataset. Following a brief review of the retrieval technique, this talk will focus on quality assurance assessments, estimated uncertainties, and data usage scenarios. We will conclude with examples highlighting some of the exciting work already being done using this new addition to CALIPSO’s already rich data record.

R Ryan↗
NASA NTRS2022

Developing Data-Driven Artificial Neural Network for A High Throughput Retrieval of Aerosol Optical Depth and Surface Temperature of Mars

In this work, we aim to developartificial neural network(ANN)techniquesto reproduce the retrieval results ofphysical quantities from spacecraft observations of solar system bodiesusing radiative transfer methods. The particular application here is the retrieval of dust optical depth, water-ice optical depth, and surface temperatureonMarsusingdaytime observationsobtained by the Thermal Emission Spectrometer (TES) onboard the Mars Global Surveyor.Compared against the results obtained from traditional radiative transfer retrieval techniques, our ANN successfully recoveredthe three quantitiesusingdaytime observations. The principal advantage of thesemachine learning(ML) algorithms istheir complete automation and highthroughput. Therefore, the algorithms presented here would be useful for very large datasets andwould make practical the sampling of many different approximations or boundary conditions related to a given observation dataset and retrieval problem.

Rafael Moreno↗
NASA NTRS2021

On the Settling Depth of Meltwater Escaping From Beneath Antarctic Ice Shelves

Antarctic glacial meltwater is thought to play an important role in determining large-scale Southern Ocean climate trends, yet recent modeling efforts have proceeded without a good understanding of how its vertical distribution in the water column is set. To rectify this, here we conduct new large-eddy simulations of the ascent of a buoyant meltwater plume after its escape from beneath an Antarctic ice shelf. We find that the meltwater’s settling depth is primarily a function of the buoyancy forcing per unit width of the source and the ambient stratification, consistent with the classical theory of turbulent buoyant plumes and in contrast to previous work that suggested an important role for centrifugal instability. Our results further highlight the significant role played by localized variability in stratification; this helps explain observed interannual variability in the vertical meltwater distribution near Pine Island Glacier. Because of the vast heterogeneity in mass loss rates and ambient conditions at different Antarctic ice shelves, a dynamic parameterization of meltwater settling depth may be crucial for accurately simulating high-latitude climate in a warming world; we discuss how this may be developed following this work, and where the remaining challenges lie.

Antarctic glacial meltwater↗
NASA NTRSDate not supplied

Standardizing A Method to Recreate the Density Profile of the Lunar Surface at Depth

As humanity prepares to return to the moon, it becomes ever more important to understand the geotechnical properties of the body we are visiting. However, with only a small number of samples and few missions that took geotechnical measurements, lunar simulants are essential for developing working technologies to use on the lunar surface. Understanding density related properties can allow us to calculate the shear strength and bearing capacity, which is vital for surface trafficability and building permanent structures. However, testbeds do not exist at a depth that would allow us to be able to accurately test certain equipment and loads in these geotechnical conditions. The goal of this project is to be able to recreate the density profile of the lunar surface at depth using piston compaction, and to evaluate success with geotechnical measurements.

C G Locke↗
NASA NTRS1997

Misperceptions of angular velocities influence the perception of rigidity in the kinetic depth effect

Accuracy in discriminating rigid from nonrigid motion was investigated for orthographic projections of three-dimension rotating objects. In 3 experiments the hypothesis that magnitudes of angular velocity are misperceived in the kinetic depth effect was tested, and in 4 other experiments the hypothesis that misperceiving angular velocities leads to misperceiving rigidity was tested. The principal findings were (a) the magnitude of perceived angular velocity is derived heuristically as a function of a property of the first-order optic flow called deformation and (b) perceptual performance in discriminating rigid from nonrigid motion is accurate in cases when the variability of the deformations of the individual triplets of points of the stimulus displays favors this interpretation and not accurate in other cases.

NASA Center ARC↗
NASA NTRS2005

Effects of High-frequency Wind Sampling on Simulated Mixed Layer Depth and Upper Ocean Temperature

Effects of high-frequency wind sampling on a near-global ocean model are studied by forcing the model with a 12 hourly averaged wind product and its 24 hourly subsamples in separate experiments. The differences in mixed layer depth and sea surface temperature resulting from these experiments are examined, and the underlying physical processes are investigated. The 24 hourly subsampling not only reduces the high-frequency variability of the wind but also affects the annual mean wind because of aliasing. While the former effect largely impacts mid- to high-latitude oceans, the latter primarily affects tropical and coastal oceans. At mid- to high-latitude regions the subsampled wind results in a shallower mixed layer and higher sea surface temperature because of reduced vertical mixing associated with weaker high-frequency wind. In tropical and coastal regions, however, the change in upper ocean structure due to the wind subsampling is primarily caused by the difference in advection resulting from aliased annual mean wind, which varies with the subsampling time. The results of the study indicate a need for more frequent sampling of satellite wind measurement and have implications for data assimilation in terms of identifying the nature of model errors.

mixed layered depth↗