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NASA NTRS2012

Synchrotron Spectral Curvature from 22 MHZ to 23 GHZ

We combine surveys of the radio sky at frequencies 22 MHz to 1.4 GHz with data from the ARCADE-2 instrument at frequencies 3 GHz to 10 GHz to characterize the frequency spectrum of diffuse synchrotron emission in the Galaxy. The radio spectrum steepens with frequency from 22 MHz to 10 GHz. The projected spectral index at 23 GHz derived from the low-frequency data agrees well with independent measurements using only data at frequencies 23 GHz and above. Comparing the spectral index at 23 GHz to the value from previously published analyses allows extension of the model to higher frequencies. The combined data are consistent with a power-law index beta = -2.64 +/-= 0.03 at 0.31 GHz, steepening by an amount of Delta-beta = 0.07 every octave in frequency. Comparison of the radio data to models including the cosmic-ray energy spectrum suggests that any break in the synchrotron spectrum must occur at frequencies above 23 GHz.

Kogut, Alan J.↗

NASA NTRS2014

Comparison of Computational Approaches for Rapid Aerodynamic Assessment of Small UAVs

Computational Fluid Dynamic (CFD) methods were used to determine the basic aerodynamic, performance, and stability and control characteristics of the unmanned air vehicle (UAV), Kahu. Accurate and timely prediction of the aerodynamic characteristics of small UAVs is an essential part of military system acquisition and air-worthiness evaluations. The forces and moments of the UAV were predicted using a variety of analytical methods for a range of configurations and conditions. The methods included Navier Stokes (N-S) flow solvers (USM3D, Kestrel and Cobalt) that take days to set up and hours to converge on a single solution; potential flow methods (PMARC, LSAERO, and XFLR5) that take hours to set up and minutes to compute; empirical methods (Datcom) that involve table lookups and produce a solution quickly; and handbook calculations. A preliminary aerodynamic database can be developed very efficiently by using a combination of computational tools. The database can be generated with low-order and empirical methods in linear regions, then replacing or adjusting the data as predictions from higher order methods are obtained. A comparison of results from all the data sources as well as experimental data obtained from a wind-tunnel test will be shown and the methods will be evaluated on their utility during each portion of the flight envelope.

Shafer, Theresa C.↗

NASA NTRS2016

Monitoring Drought Conditions in the Navajo Nation Using NASA Earth Observations

The Navajo Nation, a 65,700 sq km Native American territory located in the southwestern United States, has been increasingly impacted by severe drought events and changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring drought are dependent on state-based monthly Standardized Precipitation Index value maps calculated by the Western Regional Climate Center. However, these maps do not provide the spatial resolution needed to illustrate differences in drought severity across the vast Nation. To better understand and monitor drought events and drought regime changes in the Navajo Nation, this project created a geodatabase of historical climate information specific to the area, and a decision support tool to calculate average Standardized Precipitation Index values for user-specified areas. The tool and geodatabase use Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Monitor (GPM) observed precipitation data and Parameter-elevation Relationships on Independent Slopes Model modeled historical precipitation data, as well as NASA's modeled Land Data Assimilation Systems deep soil moisture, evaporation, and transpiration data products. The geodatabase and decision support tool will allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources.

Ly, Vickie↗

NASA NTRS2014

Interpreting the Helioseismic and Magnetic Imager (HMI) Multi-Height Velocity Measurements

The Solar Dynamics Observatory Helioseismic and Magnetic Imager (SDOHMI) filtergrams, taken at six wavelengths around the Fe I 6173.3 Å line, contain information about the line-of-sight velocity over a range of heights in the solar atmosphere. Multiheight velocity inferences from these observations can be exploited to study wave motions and energy transport in the atmosphere. Using realistic convection-simulation datasets provided by the STAGGER and MURaM codes, we generate synthetic filtergrams and explore several methods for estimating Dopplergrams.We investigate at which height each synthetic Dopplergram correlates most strongly with the vertical velocity in the model atmospheres. On the basis of the investigation, we propose two Dopplergrams other than the standard HMI-algorithm Dopplergram produced from HMI filtergrams: a line-center Dopplergram and an average-wing Dopplergram. These two Dopplergrams correlate most strongly with vertical velocities at the heights of 30 40 km above (line center) and 30 40 km below (average wing) the effective height of the HMI-algorithm Dopplergram. Therefore, we can obtain velocity information from two layers separated by about a half of a scale height in the atmosphere, at best. The phase shifts between these multi-height Dopplergrams from observational data as well as those from the simulated data are also consistent with the height-difference estimates in the frequency range above the photospheric acoustic-cutoff frequency.

velocity fields↗

NASA NTRS2019

Sherlock Data Warehouse

This slide deck provides an overview of the data and resources available in the Sherlock Data Warehouse. Sherlock was developed and is currently maintained by the Aviation Systems Division at NASA Ames Research Center. Sherlock contains a valuable collection of flight, air traffic management, and weather data. But Sherlock is not just a data archive. Sherlock also includes tools and resources to access, download, and visualize data, as well as resources to process the data. This overview summarizes Sherlock data sources, demonstrates data analytics and visualization with MicroStrategy, illustrates disparate data integration using the ATM Knowledge graph, and presents a machine learning use case using the Big Data system.

data warehouse↗

NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗

NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗

NASA NTRS2020

Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensor on the Metop-A satellite (GOME-2A) for the period 2007-2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007-2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterized the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compares well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations, and is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data to be used as an independent constraint on photosynthetic activity on regional to global scales.

Solar-induced fluorescence (SIF)↗

NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗

NASA NTRS1998

Developing Information Power Grid Based Algorithms and Software

This was an exploratory study to enhance our understanding of problems involved in developing large scale applications in a heterogeneous distributed environment. It is likely that the large scale applications of the future will be built by coupling specialized computational modules together. For example, efforts now exist to couple ocean and atmospheric prediction codes to simulate a more complete climate system. These two applications differ in many respects. They have different grids, the data is in different unit systems and the algorithms for inte,-rating in time are different. In addition the code for each application is likely to have been developed on different architectures and tend to have poor performance when run on an architecture for which the code was not designed, if it runs at all. Architectural differences may also induce differences in data representation which effect precision and convergence criteria as well as data transfer issues. In order to couple such dissimilar codes some form of translation must be present. This translation should be able to handle interpolation from one grid to another as well as construction of the correct data field in the correct units from available data. Even if a code is to be developed from scratch, a modular approach will likely be followed in that standard scientific packages will be used to do the more mundane tasks such as linear algebra or Fourier transform operations. This approach allows the developers to concentrate on their science rather than becoming experts in linear algebra or signal processing. Problems associated with this development approach include difficulties associated with data extraction and translation from one module to another, module performance on different nodal architectures, and others. In addition to these data and software issues there exists operational issues such as platform stability and resource management.

Dongarra, Jack↗

NASA NTRSDate not supplied

Testing and Application of Commercial Thermodynamics Software for Solid-Gas Equilibria in Planetary Science Applications

The ability to calculate chemical equilibria from thermochemical constants (e.g., H, S, Cp) or other experimental data is a tool widely employed by scientists. However, phase equilibria for planetary science problems is limited by easy access to libraries of thermochemical data, as well as the software to invert that data. Many academic software packages are focused on specific types of problems (e.g., aqueous solutions or melting and crystallization of magmas). This focus makes them excellent tools for specific uses, but poorer tools for other scenarios, especially those involving elements or chemical species that are less well-studied in that specific scientific field (e.g. NaCl vapor). Amongst the more general thermodynamic software tools, there are closed academic software packages, open-source options, as well as commercial software. We have explored the extent to which commercial chemistry/chemical engineering software may be able to satisfy the need for thermochemical modeling packages that can accommodate the diverse species and conditions of planetary science problems. Here we compare the output of software package HSC Chemistry™ (Metso Outotec) to published vapor-solid phase diagrams for a variety of major and minor elements. HSC Chemistry™ has been used in several recent geochemical studies of terrestrial volcanic systems [2- 4]. However, it has not to our knowledge been benchmarked against peer-reviewed model results for the conditions and compositions relevant to planetary science. We present one such comparison here, the low- pressure environment of the solar nebula, using literature data. In short, we find excellent agreement between HSC Chemistry™ and the well-regarded academic software package CONDOR. These preliminary results suggest that at least some commercial software packages are capable of robust thermochemical calculations for planetary science.

planetary science↗

NASA NTRS2004

Visual data mining for quantized spatial data

In previous papers we've shown how a well known data compression algorithm called Entropy-constrained Vector Quantization ( can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we descuss how to visualize and understand the content of such reduced data sets.

cluster analysis↗

NASA NTRSDate not supplied

Recent Advances in the U.S. in Ablative TPS for In-situ Exploration of Giant Planets

The Decadal Survey report released [1] prioritizes Uranus as the highest priority Flagship class mission to be explored with an orbiter and a probe and it also recommended Saturn Probe under New Frontiers mission class. Thermal protection system (TPS) is essential for Uranus and Saturn probe missions. The two cardinal requirements are that it must be fail-safe and yet be mass efficient. The Decadal Survey report also pointed out the readiness of heatshield for extreme entry environment technology (HEEET) at TRL 6 for probe missions at Gas Giants. HEEET relies on 3-D weaving and is shown to be a robust and mass efficient TPS through ground testing and analysis. HEEET was matured to TRL 6 in 2019. HEEET was reported at the Ice Giant Workshop in 2019 at Marseille. Since 2019, significant advances have been made primarily because of Mars Sample Return (MSR) mission. MSR baselined 3-D Woven TPS as its heatshield for the earth entry system. MSR earth entry system (EES) requirements and the resulting heatshield/TPS requirements are most stringent of all entry missions. This is a result of backward contamination protection which classifies MSR as a “restricted class 5” mission to safeguard the accidental release of potentially hazardous Mars Sample into Earth’s atmosphere. Complex requirements for the heatshield start with micro-meteor impact tolerance followed by the requirement for steep entry to minimize the size of the impact footprint which results in extreme heating. The heatshield is part of the impact attenuation system. The EES architecture does not use a parachute and is designed to tolerate impact loads. Hence heatshield TPS selection and design becomes one of the key challenges. After completion of the HEEET technology maturation in 2019, IRAD efforts focused on assessing the 3-D Woven family of TPS to MSR EES. After nearly two years of evaluation of alternate TPS such as carbon phenolic, C-C hot structure, PICA, and 3-D woven family of TPS, a single layer 3-D woven TPS derived from the dual-layer HEEET was down selected and is currently the baseline. A new loom capable of weaving the single-layer 3-D woven preform at 80” wide has been designed, assembled and is currently on the verge of weaving the MSR EES TPS. During the HEEET maturation and the follow-on development, the single-layer TPS has been tested at extreme heating conditions. As a result, single layer TPS was recommended and evaluated during the Planetary Mission Concept Studies funded by NASA in preparation for the Decadal committee. In addition, in anticipation of Saturn mission proposals, single layer was evaluated as well. Aerocapture can reduce the trip time, also allow bigger payload fraction and in addition, it allows for the possibility of probe delivery once the spacecraft is in orbit. This can lead to obtaining both in-situ data as well as data from orbit simultaneously. Since Aerocapture depletes energy/velocity, probe delivery from orbit reduces the demand on TPS. Aerocapture was mentioned in the Decadal Study report as ready for implementation, but due to perceived risk it is not adopted by the mission designers. Establishing TPS readiness for aerocapture missions will be addressed. Going one step beyond aerocapture is aerogravity assist if fast return is the goal. Recent studies [4] looked at aerogravity assist and the TPS readiness. This talk will highlight both aerocapture and aerogravity assist from a TPS perspective. The main objective of this proposed talk is to present a comprehensive picture of the SOA TPS technology including recent developments. The talk will highlight advances in manufacturing, results from the Decadal White Papers, PMCS and other studies, and aerocapture and aerogravity assist that could play a role in the near or far term in-situ exploration.

E. Venkatapathy↗

NASA NTRS2008

Mars Global Geologic Mapping: Amazonian Results

We are in the second year of a five-year effort to map the geology of Mars using mainly Mars Global Surveyor, Mars Express, and Mars Odyssey imaging and altimetry datasets. Previously, we have reported on details of project management, mapping datasets (local and regional), initial and anticipated mapping approaches, and tactics of map unit delineation and description [1-2]. For example, we have seen how the multiple types and huge quantity of image data as well as more accurate and detailed altimetry data now available allow for broader and deeper geologic perspectives, based largely on improved landform perception, characterization, and analysis. Here, we describe early mapping results, which include updating of previous northern plains mapping [3], including delineation of mainly Amazonian units and regional fault mapping, as well as other advances.

Tanaka, K. L.↗

NASA NTRS2012

Performance Requirements for Emergency Responder Interoperable and Compatible Electronic Safety Equipment

Firefighters and other emergency response personnel are presented with an increasing array of technologies to improve their health and safety. This includes real-time bidirectional communication, navigation and positional information, data on physiological and metabolic functions, as well as data on their surrounding environment. The emerging challenge is to integrate these elements into a practical system, addressing such features as power, data transfer, and inter-element coordination and communication. In many respects, NASA has addressed these aspects in the context of Extra Vehicular Activity (EVA). The EVA environment shares many common attributes with that of emergency response scenarios. A similar situation exists in terms of the need for interoperability among the various system sub-elements. A brief overview is presented on the similarities and differences in these two applications, as well as the technical approach adopted by NASA in terms of system design philosophy.

Greenberg, Paul S.↗

NASA NTRS1973

Broad band solar EUV absorption in the earth's upper atmosphere.

Observation data on solar radiation intensity, based on measurements performed as a function of time for three broad wavelength bands between 280 and 1030 A by a wheel spectrometer on Oso 5 during sunrise and sunset, are compared with predicted intensity variations based on Cira models. The differences between sunrise and sunset data, as well as those between observed and predicted data are discussed.

Allen, K. H.↗

NASA NTRS1974

Contributions of satellite-determined gravity results in geodesy

Different forms of the theoretical gravity formula are summarized and methods of standardization of gravity anomalies obtained from satellite gravity and terrestrial gravity data are discussed in the context of three most commonly used reference figures, e.g., International Reference Ellipsoid, Reference Ellipsoid 1967, and Equilibrium Reference Ellipsoid. These methods are important in the comparison and combination of satellite gravity and gravimetric data as well as the integration of surface gravity data, collected with different objectives, in a single reference system. For ready reference, tables for such reductions are computed. Nature of the satellite gravity anomalies is examined to aid the geophysical and geodetic interpretation of these anomalies in terms of the tectonic features of the earth and the structure of the earth's crust and mantle. Computation of the Potsdam correction from satellite-determined geopotential is reviewed. The contribution of the satellite gravity results in decomposing the total observed gravity anomaly into components of geophysical interest is discussed. Recent work on the possible temporal variations in the geogravity field is briefly reviewed.

Khan, M. A.↗

NASA NTRS1976

Assessment of NDE Reliability Data

Twenty sets of relevant Nondestructive Evaluation (NDE) reliability data have been identified, collected, compiled, and categorized. A criterion for the selection of data for statistical analysis considerations has been formulated. A model to grade the quality and validity of the data sets has been developed. Data input formats, which record the pertinent parameters of the defect/specimen and inspection procedures, have been formulated for each NDE method. A comprehensive computer program has been written to calculate the probability of flaw detection at several confidence levels by the binomial distribution. This program also selects the desired data sets for pooling and tests the statistical pooling criteria before calculating the composite detection reliability. Probability of detection curves at 95 and 50 percent confidence levels have been plotted for individual sets of relevant data as well as for several sets of merged data with common sets of NDE parameters.

Yee, B. G. W.↗