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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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In-situ sampling and analysis is one of the major tasks in future NASA exploration missions. It is essential that the samples on other planets including Mars are free of contaminations from the earth. Recently, a novel drilling technology that is actuated by a piezoelectric drive mechanism was developed at it is called Ultrasonic/Sonic Driller/Corer.
Volcanic eruptions in the Aleutian Archipelago during the summer of 2008 deposited large quantities of iron‐laden ash to the subarctic North Pacific Ocean. The surface ocean ecosystem response has been previously characterized using limited in situ and autonomous field measurements and numerical modeling, but basin‐scale satellite remote sensing has been limited to a simple description of chlorophyll variability. Here, we show that the ecosystem response is a complex combination of phytoplankton biomass and physiology that can be described with satellite ocean color diagnostics such as the chlorophyll‐to‐carbon biomass ratio (Chl:Cphyto) and chlorophyll fluorescence yield. Together, these quantities outline a more complete picture of ecological responses spanning unique signals of iron stress (and relief from), photo acclimation, changes in phytoplankton growth rate, increases in biomass, and timescales of decay for these processes.
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and nonlinearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature matchups between GMI and the other instruments over both open ocean and rainforest scenes. To assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multidecadal climate data record from passive microwave satellite observations. Significance Statement Creating a long-term climate data record of satellite observations of ocean winds, water vapor, and other variables requires careful and accurate calibration of the various sensors that are used. In particular, it is important to achieve the best possible consistency between the measurements from all the different instruments. This is a challenging task as the configuration and accuracy of these instruments can differ widely. The purpose of our paper is to demonstrate and validate the basic methodology for performing this intercalibration. The backbone of our method is data observed by a well-calibrated sensor that measures the passive microwave emission from Earth’s surface and atmosphere. We show how to transfer its calibration standard to other sensors.
Novel robotic excavation technologies are needed to perform in-situ resource utilization (ISRU) tasks at levels required to sustain a long-term presence on the lunar surface. Developing and testing multiple iterations of functional hardware is time and cost prohibitive, thus slowing down the pace of progress and delaying humanity’s settlement of the Moon. High-fidelity, physics-based simulation can reduce the time and effort required to develop and deploy robotic systems [1]. We have adopted this approach to create high-fidelity models of robotic test hardware to enable rapid virtual design and optimization of excavation technologies [2]. Such models can leverage modern computational tools like Discrete Element Method (DEM) simulations that can be coupled with automated design approaches like topology optimization to reduce the amount of prototyping and physical testing needed to realize useful tools.
Qualification and certification of safety critical parts is a hurdle to the adoption of metallic additively manufactured components for aerospace vehicle applications. Challenges include variability in part properties due to inconsistent defect distribution and microstructure. Understanding of the process through finite element modeling (FEM), and process control through in-situ monitoring, may result in significant improvements; however, solutions useful to manufacturers will require large volumes of data and automated data utilization. Toward this end, a generalizable automated FEM calibration paradigm is developed. This paradigm leverages existing and novel tools from machine learning and uncertainty quantification to enable the automatic calibration of FEMs without requiring prior knowledge of the model performance across input parameter space, including meshing and solver settings, which can require time consuming manual model probing or cause noisy and inconsistent predictions. The result is a probabilistic distribution of calibrated and validated FEM input parameters targeting measured data.
The solar wind can, to a good approximation be described as a two component flow with fast, tenuous, quiescent flow emanating from coronal holes, and slow, dense and variable flow associated with the boundary between open and closed magnetic fields. In spite of its simplicity, this picture naturally produces a range of complex heliospheric phenomena, including the presence, location, and orientation of corotating interaction regions and their associated shocks. In this study, we apply a two-step mapping technique, incorporating a magnetohydrodynamic model of the solar corona, to bring in situ observations h m Ulysses, WIND, and ACE back to the solar surface in an effort to determine some intrinsic properties of the quasi-steady solar wind. In particular, we find that a "layer" of approx. 35,000 h n exists between the Coronal Hole Boundary (CHB) and the fast solar wind, where the wind is slow and variable. We also- derive a velocity gradient within large polar coronal boles (that were present during Ulysses rapid latitude scan) as a function of distance from the CHB. We find that nu = 713 km/s + 3.2 d, where d is the angular distance from the CHB boundary in degrees.
Understanding the relationship between remotely sensed snow disappearance and seasonal water supply may become vital in coming years to supplement limited ground based, in situ measurements of snow in a changing climate. For the period 2001–2019, we investigated the relationship between satellite derived Day of Snow Disappearance (DSD)—the date at which snow has completely disappeared—and the seasonal water supply, i.e., the April—July total streamflow volume, for 15 snow dominated basins across the western U.S. A Monte Carlo framework was applied, using linear regression models to evaluate the predictive skill—defined here as a model’s ability to accurately predict seasonal flow volumes—of varied predictors, including DSD and in situ snow water equivalent (SWE), across a range of spring forecast dates. In all basins there is a statistically significant relationship between mean DSD and seasonal water supply (p ≤ 0.05), with mean DSD explaining roughly half of the variance. Satellite-based model skill improves later in the forecast season, surpassing the skill of in-situ-based (SWE) models in skill in 10 of the 15 basins by the latest forecast date. We found little to no correlation between model error and basin characteristics such as elevation and the ratio of snow water equivalent to total precipitation. Despite a relatively short data record, this exploratory analysis shows promise for improving seasonal water supply prediction, in particular for snow dominated basins lacking in situ observations.
Modeling of surface energy fluxes was combined with in-situ measurement of surface parameters, specifically the surface sensible heat flux and the substrate soil moisture. A vegetation component was incorporated in the atmospheric/substrate model and subsequently showed that fluxes over vegetation can be very much different than those over bare soil for a given surface-air temperature difference. The temperature signatures measured by a satellite or airborne radiometer should be interpreted in conjunction with surface measurements of modeled parameters. Paradoxically, analyses of the large-scale distribution of soil moisture availability shows that there is a very high correlation between antecedent precipitation and inferred surface moisture availability, even when no specific vegetation parameterization is used in the boundary layer model. Preparatory work was begun in streamlining the present boundary layer model, developing better algorithms for relating surface temperatures to substrate moisture, preparing for participation in the French HAPEX experiment, and analyzing aircraft microwave and radiometric surface temperature data for the 1983 French Beauce experiments.
Models of the Venus neutral upper atmosphere, based on both in situ and remote sensing measurements, are provided for the height interval from 100 to 3500 km. The general approach in model formulation was to divide the atmosphere into three regions: 100-150 km, 150-250 km, and 250-3500 km. Boundary conditions at 150 km are consistent with both drag and mass spectrometer measurements. A paramount consideration was to keep the models simple enough to be used conveniently. Available observations are reviewed.
Fracture mechanics analysis and test data for a toughened composite material that has a resin-rich interlaminar layer (RIL) were used to investigate in situ strength. Exposure to a range of environmental conditions was considered. A parametric analysis study was performed to judge the effects of laminate and material variables. A finite thickness effect, indicating an interaction between ply group thickness and effective flaw size, was found dominant. The magnitude of the effect was directly related to RIL stiffness. In situ strength was found to decrease with decreasing RIL stiffness. This work indicates the need to use a fracture mechanics model of actual lamina microstructure and heterogeneous properties to predict in situ strength in materials with RIL.
Plume-surface interaction is a critical topic of interest as human spaceflight programs establish a renewed presence on the Moon and push onwards to Mars. The erosion of regolith and high-speed ejection of soil and rocks which occurs during a powered spacecraft landing on lunar or terrestrial bodies can pose serious risks to the vehicle during landing and to existing ground assets. Ground testing campaigns have provided data sets in relevant environments (e.g., low pressure) in tandem with predictive computational model validation. To-date, limited in-situ flight data exists to inform the study of the key phenomena of plume-surface interaction: plume-flow physics, soil erosion, and ejecta dynamics. In two upcoming lunar missions, erosion of the lunar regolith will be measured in-situ using the stereo photogrammetry technique as part of NASA’s Commercial Lunar Payload Services (CLPS) Program. The Stereo Cameras for Lunar Plume Surface Studies (SCALPSS) will use a multiple-camera system to measure the erosion of the lunar regolith due to the vehicles’ rocket engine plumes during descent and after landing. The application of stereo photogrammetry to accurately measure a realistic eroded shape has been validated in laboratory work ahead of the scheduled flights. In preparation for future CLPS, Artemis, or other lunar science missions, recent advancements have been made to measure the ejecta structure and particle dynamics in addition to the erosion of regolith using a SCALPSS-like photogrammetry system. The thickness and ejection angle of entrained soil and dust produced during plume-induced erosion can be used to determine how far debris will travel, and thus how it may impact nearby infrastructure or the landing vehicle itself. The present work highlights the development of this new flight instrumentation capability and its value to advancements in the plume-surface interaction measurement topic for lunar or planetary landings.
Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.
Global magnetohydrodynamic (MHD) models of the solar corona are computationally intensive, numerically complex simulations that have produced important new results over the past few years. After a brief overview of how these models usually work, I will address three topics: (1) How these models are now routinely used to predict the morphology of the corona and analyze Earth and space-based remote observations of the Sun; (2) The direct application of these models to the analysis of physical processes in the corona and chromosphere and to the interpretation of in situ solar wind observations; and (3) The use of results from global models to validate the approximations used to make detailed studies of physical processes in the corona that are not otherwise possible using the global models themselves.
An existing, three-dimensional, Eulerian-Lagrangian finite-difference model was modified and used to examine the transport processes of dumped sewage sludge in the New York Bight. Both in situ and laboratory data were utilized in an attempt to approximate model inputs such as mean current speed, horizontal diffusion coefficients, particle size distributions, and specific gravities. The results presented are a quantitative description of the fate of a negatively buoyant sewage sludge plume resulting from continuous and instantaneous barge releases. Concentrations of the sludge near the surface were compared qualitatively with those remotely sensed. Laboratory study was performed to investigate the behavior of sewage sludge dumping in various ambient density conditions.
A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.
The Vector Electric Field Investigation (VEFI) suite onboard the Communications/Navigation Outage Forecasting System (C/NOFS) spacecraft includes a sensitive fluxgate magnetometer to measure DC and ULF magnetic fields in the low latitude ionosphere. The instrument includes a DC vector measurement at 1 sample/sec with a range of +/- 45,000 nT whose primary objective is to provide direct measurements of both V x B and E x B that are more accurate than those obtained using a simple magnetic field model. These data can also be used for scientific research to provide information of large-scale ionospheric and magnetospheric current systems, which, when analyzed in conjunction with the C/NOFS DC electric field measurements, promise to advance our understanding of the electrodynamics of the low latitude ionosphere. In this study, we use the magnetic field data to study the temporal and local time variations of the ring currents during geomagnetic storms. We first compare the in situ measurements with the POMME (the POtsdam Magnetic Model of the Earth) model in order to provide an in-flight "calibration" of the data as well as compute magnetic field residuals essential for revealing large scale external current systems. We then compare the magnetic field residuals observed both during quiet times and during geomagnetic storms at the same geographic locations to deduce the magnetic field signatures of the ring current. As will be shown, the low inclination of the C/NOFS satellite provides a unique opportunity to study the evolution of the ring current as a function of local time, which is particularly insightful during periods of magnetic storms. This paper will present the initial results of this study.
In the southeast Atlantic, well-defined smoke plumes from Africa advect over marine boundary layer cloud decks; both are most extensive around September, when most of the smoke resides in the free troposphere. A framework is put forth for evaluating the performance of a range of global and regional atmospheric composition models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. A strength of the comparison is a focus on the spatial distribution of a wider range of aerosol composition and optical properties than has been done previously. The sparse airborne observations are aggregated into approximately 2° grid boxes and into three vertical layers: 3–6 km, the layer from cloud top to 3 km, and the cloud-topped marine boundary layer. Simulated aerosol extensive properties suggest that the flight-day observations are reasonably representative of the regional monthly average, with systematic deviations of 30 % or less. Evaluation against observations indicates that all models have strengths and weaknesses, and there is no single model that is superior to all the others in all metrics evaluated. Whereas all six models typically place the top of the smoke layer within 0–500 m of the airborne lidar observations, the models tend to place the smoke layer bottom 300–1400 m lower than the observations. A spatial pattern emerges, in which most models underestimate the mean of most smoke quantities (black carbon, extinction, carbon monoxide) on the diagonal corridor between 16° S, 6° E, and 10° S, 0° E, in the 3–6 km layer, and overestimate them further south, closer to the coast, where less aerosol is present. Model representations of the above-cloud aerosol optical depth differ more widely. Most models overestimate the organic aerosol mass concentrations relative to those of black carbon, and with less skill, indicating model uncertainties in secondary organic aerosol processes. Regional-mean free-tropospheric model ambient single scattering albedos vary widely, between 0.83 and 0.93 compared with in situ dry measurements centered at 0.86, despite minimal impact of humidification on particulate scattering. The modeled ratios of the particulate extinction to the sum of the black carbon and organic aerosol mass concentrations (a mass extinction efficiency proxy) are typically too low and vary too little spatially, with significant inter-model differences. Most models overestimate the carbonaceous mass within the offshore boundary layer. Overall, the diversity in the model biases suggests that different model processes are responsible. The wide range of model optical properties requires further scrutiny because of their importance for radiative effect estimates.
The spatiotemporal variability of terrestrial biospheric carbon dioxide (CO2) flux over South Asia is poorly understood. The inverse model “top-down” CO2 flux estimates which rely on atmospheric CO2 observational data are impeded by sparse in situ measurements over this region. The Orbiting Carbon Observatory 2 (OCO-2) provides much needed global retrievals of column-average CO2 dry-air mole fraction (XCO2) with the finest spatial and temporal resolution and highest sensitivity to surface fluxes available to-date. This study conducted global inverse model simulations as part of the second OCO-2 Multi-model Intercomparison Project assimilating version 9 of the OCO-2 XCO2 retrievals in land nadir (LN) and land glint (LG) observing modes and global in situ (IS) measurements. The four-dimensional variational assimilation system with the GEOS-Chem global chemical transport model was used to estimate CO2 fluxes from 2015 to 2018. We then assessed the spatiotemporal variability of optimized CO2 Net ecosystem Exchange (NEE) fluxes aggregated over the South Asia region. The most robust result found by assimilating OCO-2 observations was the constraints imposed on the seasonal cycle of terrestrial biospheric fluxes over South Asia. The seasonality of South Asian NEE estimated by assimilating OCO-2 or global IS data showed a larger seasonal cycle compared to the current understanding of NEE in this region (represented by the prior NEE used in the model). The satellite-in situ joint inversion (IS + LN + LG) led to land flux seasonal amplitude (absolute magnitude of the difference between peak and trough of monthly mean values over a year) of 4.0 PgC yr-1, compared to the prior model (1.5 PgC yr-1). Moreover, OCO-2 data imposed a phase shift in the seasonal cycle, resulting in a large CO2 source in April and a large uptake in September. Most of the features in the seasonal cycle imposed by OCO-2 data are in agreement with previous “top-down” studies assimilating regional aircraft observations. Nevertheless, we recommend a closer examination of the novel features highlighted by the OCO-2 column data over South Asia in future studies by conducting regional inverse modelling with denser in situ and vertically-resolved regional observations along with satellite data.