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Higher order statistics, especially 2nd order statistics, have been used to study ocean processes for many years in the past, and occupy an appreciable part of the research literature on physical oceanography. They in turn form part of a much larger field of study in statistical fluid mechanics.
Johnson Space Center has piloted an innovation team known as the Forge to perform rapid turnaround, concurrent engineering studies. The Forge is intended to be able to pull together a team of strategically selected domain experts to innovatively solve a specified problem within a short period of time. The Forge’s inaugural project was the development of a Lunar Surface Laboratory Module concept. The lab module is intended as an option for the NASA Lunar Architecture Team and may allow for increased science productivity and return on investment than with only the Surface Habitat. The Forge team developed two concepts, a stationary laboratory and a mobile laboratory. Human factors evaluations have been conducted to determine the acceptability of the resulting design concepts. The human factors evaluations conducted include a card sort evaluation of the laboratories by science discipline and the System Usability Scale (SUS). The card sort focused subject matter expert expectations for the relative positioning of the laboratory functional areas, determining whether a given lab area should be adjacent to, or separated from, another. The SUS focused on layout efficiency. Both used human factors personnel and both life and physical scientists as subject matter experts. Each of these evaluations will focus primarily on objective data with limited subjective comments, reducing the time required for processing responses. The results of the card sort and SUS evaluations and their implications will be discussed. Based on the evaluation data, an overall acceptability of the Lunar Surface Laboratory Module produced by the Forge study can be determined. Lessons learned and recommendations for future use of human factors rapid assessment processes in early design trades will also be provided.
Johnson Space Center has piloted an innovation team known as the Forge to perform rapid turnaround, concurrent engineering studies. The Forge is intended to be able to pull together a team of strategically selected domain experts to innovatively solve a specified problem within a short period of time. The Forge’s inaugural project was the development of a Lunar Surface Laboratory Module concept. The lab module is intended as an option for the NASA Lunar Architecture Team and may allow for increased science productivity and return on investment than with only the Surface Habitat. The Forge team developed two concepts, a stationary laboratory and a mobile laboratory. Human factors evaluations have been conducted to determine the acceptability of the resulting design concepts. The human factors evaluations conducted include a card sort evaluation of the laboratories by science discipline and the System Usability Scale (SUS). The card sort is focused subject matter expert expectations for the relative positioning of the laboratory functional areas, determining whether a given lab area should be adjacent to, or separated from, another. The SUS will focus on layout efficiency. Both will use human factors personnel and both life and physical scientists as subject matter experts. Each of these evaluations will focus primarily on objective data with limited subjective comments, reducing the time required for processing responses. The results of the card sort and SUS evaluations and their implications will be discussed. Based on the evaluation data, an overall acceptability of the Lunar Surface Laboratory Module produced by the Forge study can be determined. Lessons learned and recommendations for future use of human factors rapid assessment processes in early design trades will also be provided.
The SLS rocket was instrumented to collect liftoff environment data during Artemis I launch. The instruments were microphones and pressure transducers. Most data were acceptable for analysis, but required corrections for port resonance, mean signal drift, data system delays, and data spikes. The data were all transmitted via telemetry and in this process some of the measurements experienced unexpectedly long gaps in the data stream. A time validated reconstruction algorithm failed when encountering the long gaps and required use of data from a simpler reconstruction process, with manually inserted data delay corrections. This paper discusses the issues and the corrections that were necessary for the launch data to determine the true physics of the acoustics occurring during launch.
The SLS rocket was instrumented to collect liftoff environment data during Artemis I launch. The instruments were microphones and pressure transducers. Most data were acceptable for analysis, but required corrections for port resonance, mean signal drift, data system delays, and data spikes. The data were all transmitted via telemetry and in this process some of the measurements experienced unexpectedly long gaps in the data stream. A time validated reconstruction algorithm failed when encountering the long gaps and required use of data from a simpler reconstruction process, with manually inserted data delay corrections. This paper discusses the issues and the corrections that were necessary for the launch data to determine the true physics of the acoustics occurring during launch.
Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.
Analyses of quasi-periodic oscillations(QPOs)are important to understanding the dynamic behavior in manyastrophysical objects during transient events like gamma-ray bursts, solarflares, magnetarflares, and fast radiobursts. Astrophysicists often search for QPOs with frequency-domain methods such as(Lomb–Scargle)periodograms, which generally assume power-law models plus some excess around the QPO frequency. Time-series data can alternatively be investigated directly in the time domain using Gaussian process(GP)regression.While GP regression is computationally expensive in the general case, the properties of astrophysical data andmodels allow fast likelihood strategies. Heteroscedasticity and nonstationarity in data have been shown to causebias in periodogram-based analyses. GPs can take account of these properties. Using GPs, we model QPOs as astochastic process on top of a deterministicflare shape. Using Bayesian inference, we demonstrate how to infer GPhyperparameters and assign them physical meaning, such as the QPO frequency. We also perform model selectionbetween QPOs and alternative models such as red noise and show that this can be used to reliablyfind QPOs. Thismethod is easily applicable to a variety of different astrophysical data sets. We demonstrate the use of this methodon a range of short transients: a gamma-ray burst, a magnetarflare, a magnetar giantflare, and simulated solarflare data.
The objective of this project is to produce a system to fully model earthquake-related data. This task develops simulation and analysis tools to study the physics of earthquakes using state-of-the-art modeling.
The DSO tool, a structural design software system that provides the designer with a graphics-based menu-driven design environment to perform easy design optimization for general applications, is presented. Three design stages, preprocessing, design sensitivity analysis, and postprocessing, are implemented in the DSO to allow the designer to carry out the design process systematically. A framework, including data base, user interface, foundation class, and remote module, has been designed and implemented to facilitate software development for the DSO. A number of dedicated commercial software/packages have been integrated in the DSO to support the design procedures. Instead of parameterizing an FEM, design parameters are defined on a geometric model associated with physical quantities, and the continuum design sensitivity analysis theory is implemented to compute design sensitivity coefficients using postprocessing data from the analysis codes. A tracked vehicle road wheel is given as a sizing design application to demonstrate the DSO's easy and convenient design optimization process.
Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.
By observing the transit of various cloud features across the Jovian disk, limb-darkening curves were constructed for three regions in the 4.6 to 5.1 mu cm band. Several models currently employed in describing the radiative or dynamical properties of planetary atmospheres are here examined to understand their implications for limb-darkening. The statistical problem of fitting these models to the observed data is reviewed and methods for applying multiple regression analysis are discussed. Analysis of variance techniques are introduced to test the viability of a given physical process as a cause of the observed limb-darkening.
The effect of solar cell metallization pattern design on solar cell performance and the costs and performance effects of different metallization processes are discussed. Definitive design rules for the front metallization pattern for large area solar cells are presented. Chemical and physical deposition processes for metallization are described and compared. An economic evaluation of the 6 principal metallization options is presented. Instructions for preparing Format A cost data for solar cell manufacturing processes from UPPC forms for input into the SAMIC computer program are presented.
Dataflow concepts are used to generate a unified hardware/software model of redundant physical systems which are prone to faults. Basic results in input congruence and synchronization are shown to reduce to a simple model of data exchanges between processing sites. Procedures are given for the construction of congruence schemata, the distinguishing features of any correctly designed redundant system.
The Coma cluster of galaxies was observed with the position sensitive proportional counter (PSPC) during the ROSAT all sky survey. We find evidence for substructure in this cluster. Diffuse X-ray emission is detected from the regions of the NGC 4839 and 4911 subgroups at 6 percent and 1 percent of the total cluster emission respectively. There may be emission associated with the NGC 4874 and 4889 subgroups as well. The NGC 4839 group appears to be in the process of merging with the cluster. These X-ray data show that at least some of the groups previously found in projection are in fact physical objects possessing potential wells deep enough to trap their own X-ray gas. Because of the unlimited field of view of the all sky survey and the low background of the PSPC, we were able to measure the azimuthally averaged surface brightness of Coma out to approximately 100 arcmin, twice as far as was previously possible. Given the validity of our mass models, these new X-ray data imply that within 5/h(50) Mpc the binding mass of the Coma cluster is 1.8 +/- 0.6 x 10 exp 15/h(50) solar mass, and the fraction of cluster mass contained in hot gas is 0.30 +/- 0.14h(50) exp -3/2. Furthermore, the binding mass is more centrally concentrated than is the X-ray gas.
The effects of wind-stress on the optical properties of the ocean surface have been studied for several decades. In particular, the classic study by Cox and Munk (1954) linking sea-surface wind field to wave slope statistics provides a phenomenology by which the sea-surface wind velocity can be estimated from direct measurement of the wave-modulated surface reflectance. A limited number of studies along these lines have been conducted using airborne or spaceborne lidar systems. In these instances, truthing was provided by in situ ship reports or satellite microwave remote sensing instruments (e.g., ERS scatterometer, SSM/I). During the second deployment of the MACAWS Doppler wind lidar in the summer of 1996 measurements of sea-surface reflectance as a function of azimuth- and nadir-viewing angles were acquired off the California coast. MACAWS data products include directly measured winds, as well as calibrated backscatter/reflectance profiles, thus enabling comparison of the winds inferred from sea-surface reflectance measurements with those deriving from the Doppler-processed direct line-of-sight (LOS) estimates. Additional validation data was extracted from the ERS and SSM/I satellite microwave sensor archives maintained by the JPL Physical Oceanography Distributed Active Archive Center (PO- DAAC).
This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).
Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or adsorption and ion exchange. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission with minimal resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.
Water recovery technologies on the International Space Station (ISS) are designed and optimized for microgravity environments, thus creating a need for innovative systems optimized for mission operations in the presence of gravity. Alternative water recovery technologies under research by the Life Support Systems division were compiled into a series of Partial Gravity Water Recovery System (PGWRS) architectures. Individual unit processes were modeled via fundamental physical-chemical process equations to simulate their method of treatment, including chemical or biological oxidation, flash evaporation, filtration, or adsorption and ion exchange. Dimension and sizing data were then utilized to scale each architecture. Modeling was completed on an assortment of architectures assuming three scenarios in which treatment methods could take advantage of partial gravity: a short-duration mission with a temporary surface habitat, a longer duration mission with a more permanent habitat, and a long duration mission with minimal resupply. Total system mass was estimated to quantitatively compare architectures within scenarios, but qualitative observations were also made regarding system robustness, flexibility, and capacity to meet more stringent demands. Technologies that traded most favorably between the scenarios were those that included a pre-oxidation stage to minimize mass loading to downstream absorption and ion exchange beds, exploited gravity in liquid-vapor separation processes, and reconstituted wastewater components into recyclable material streams.