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NASA NTRS2007

Mars Global Reference Atmospheric Model (Mars-GRAM 2005) Applications for Mars Science Laboratory Mission Site Selection Processes

The new Mars-GRAM auxiliary profile capability, using data from TES observations, mesoscale model output, or other sources, allows a potentially higher fidelity representation of the atmosphere, and a more accurate way of estimating inherent uncertainty in atmospheric density and winds. Figure 3 indicates that, with nominal value rpscale=1, Mars-GRAM perturbations would tend to overestimate observed or mesoscale-modeled variability. To better represent TES and mesoscale model density perturbations, rpscale values as low as about 0.4 could be used. Some trajectory model implementations of Mars-GRAM allow the user to dynamically change rpscale and rwscale values with altitude. Figure 4 shows that an mscale value of about 1.2 would better replicate wind standard deviations from MRAMS or MMM5 simulations at the Gale, Terby, or Melas sites. By adjusting the rpscale and rwscale values in Mars-GRAM based on figures such as Figure 3 and 4, we can provide more accurate end-to-end simulations for EDL at the candidate MSL landing sites.

Justh, H. L.↗

NASA NTRS2018

Systems and Methods for Advanced Rapid Imaging and Analysis for Earthquakes

Many embodiments provide a hybrid data processing system (HySDS) of an end-to-end geodetic imaging data system enabling near-real-time science, assessment, response, and rapid recovery. The HySDS may be an operation data processing system that integrates data from many different geodetic data sources and/or sensors, including interferometric synthetic aperture radar (InSAR), GPS, pixel tracking, seismology, and/or modeling, and processes the data to generate actionable high quality science data products. The HySDS may provide for an automated imaging and analysis capabilities that is able to handle the imminent increases in raw data from new and existing geodetic monitoring sensor systems.

Owen, Susan Ethel↗

NASA NTRS2011

What Preparatory Science is Needed in Coronal Structure and Activity

Solar Orbiter and Solar Probe Plus will launch in six short years! Before then, we need to accomplish a great deal of science in order to be able to maximize the return of these missions. Preparatory science is especially important for exploratory missions such as SO and SPP, because they truly will be going "where no mission has gone before". Such preparatory science may include all types of research: theory, modeling, data exploitation, and supporting observations. This meeting provides an opportunity for the community to define and begin this critical preparatory work. In this talk I will provide an overview of our state of knowledge in coronal structure and activity, describe what I believe are the most promising opportunities for advances by SO and SPP, and lead a discussion on what programs need to be implemented now in order to achieve these science advances by the time SO and SPP launch.

Antiochos, S. K.↗

NASA NTRS2008

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

Hegde, Mahabaleshwara↗

NASA NTRSDate not supplied

Reanalysis of Rodent Data from Spacelab Life Sciences-1

The space bioscience field has long been plagued by the challenge of spaceflight with effects of radiation and microgravity. Having multiple and repeated spaceflight experiments for model organisms to solve these space stressors is costly and time consuming. Therefore, reusing and reanalyzing legacy experiments is one way that scientists can draw new conclusions in a timely manner and without using too many resources. Moreover, advances in general biological knowledge allows legacy experiments to be placed into more complete context.Here we aim to analyze all data and metadata taken from rats flown on the SLS-1 mission to create a comprehensive biological model that can be supplemented with current data to allow new discoveries in how space flown organisms adapt to the space environment. Our approach begins with the identification of all the data and metadata, including graphs and tables, for SLS-1 in NASA archives and other sources. Then, each piece of data and metadata will be digitized, reformatted and analyzed. Lastly, a previously developed astronaut model will be used to create the data framework and a comprehensive biological rodent model. The datasets we are using is from the 1991 SpaceLab Life Science 1 (SLS-1) NASA Mission. This was the first designated spacelab mission flown. All 29 rodents were tested for nine days in two different habitats: Research Animal Holding Facility (RAHF) and Animal Enclosure Module (AEM). The rodents were prepared for a live return and compared to a ground control. A total of 30 rodent experiments were accepted as flight studies on the mission. By digitization and reorganizing SLS-1 rat data we will both directly generate new insights and indirectly enable other scientists to by providing the data and metadata in a digitized form.

Space Biology↗

NASA NTRSDate not supplied

Reanalysis of Rodent Data from Spacelab Life Science-1

Space Biology↗

NASA NTRSDate not supplied

Advancing Open Science in Atmospheric Research: Integrating Data Usability and Machine Learning

In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences." "In the dynamic realm of atmospheric sciences, the convergence of data science methodologies and open data marks a transformative era, driving research advancements and nurturing aspiring scientists. This abstract highlights two pivotal projects that epitomize open science principles, aligning seamlessly with the session's objective of interdisciplinary synergy and the cultivation of emerging talent. As a NASA-certified data center, our foremost endeavor focuses on enhancing the visibility and traceability of NASA datasets within atmospheric science research. This initiative not only elevates these datasets' prominence but also establishes a robust framework ensuring their credibility in scholarly discourse. By bridging the gap between data sources and research publications, this project serves as an educational catalyst, nurturing a new generation of scholars in open collaboration and dataset authenticity. Concurrently, our second project pioneers an early warning system for flooding events, utilizing machine learning algorithms to predict flooded fractions. Through multi-source data fusion and predictive modeling, this initiative goes beyond forecasting; it embodies the core of open science by enabling proactive risk mitigation strategies. This project not only advances atmospheric sciences but also fosters an environment where young scholars engage in practical, data-driven solutions. These intertwined projects exemplify the fusion of data science with open data solutions, ensuring both the usability of quality datasets and the cultivation of scientific knowledge among emerging scholars. By spotlighting these impactful use cases, our aim is to foster discussions emphasizing the importance of open collaboration, data integrity, and the nurturing of scientific talent in atmospheric sciences.

Jennifer Wei↗

NASA NTRS1991

The AE-8 trapped electron model environment

The machine sensible version of the AE-8 electron model environment was completed in December 1983. It has been sent to users on the model environment distribution list and is made available to new users by the National Space Science Data Center (NSSDC). AE-8 is the last in a series of terrestrial trapped radiation models that includes eight proton and eight electron versions. With the exception of AE-8, all these models were documented in formal reports as well as being available in a machine sensible form. The purpose of this report is to complete the documentation, finally, for AE-8 so that users can understand its construction and see the comparison of the model with the new data used, as well as with the AE-4 model.

Vette, James I.↗

NASA NTRSDate not supplied

The Satellite Flythrough and Reconstruction Softwares at CCMC

The next-generation of model data visualization to be offered at the Community Coordinated Modeling Center (CCMC) will be based on Kamodo, an open source python package. To increase the usefulness of our services, we are also developing new capabilities based on this software, called the satellite flythrough and the reconstruction tools, to be offered both as packages for offline analysis and through an online interface (coming soon). The satellite flythrough software ‘flies’ a satellite trajectory, whether real or imaginary, through either model data hosted at CCMC or on a personal machine. This service greatly simplifies the complexity of users’ access to model data, abstracting away the time-consuming details of model data formats and interpolation. We demonstrate execution times of a few seconds to a few minutes for several example flythroughs of a trajectory stretching over a few days, depending on the parameters chosen. We also demonstrate a reconstruction tool built on top of the satellite flythrough software, for use with mission planning and model-data comparisons. This tool, based on reconstructions provided for the GDC Science and Technology Definition Team, converts multiple, simultaneous satellite flythroughs into two-dimensional reconstructions. The reconstruction tool provides a software capability for satellite constellations to determine how many satellites are needed and in what configuration to resolve the desired features in the model data. Both tools are currently available through GitHub for a selection of CCMC-hosted ITM models. Finally, we present initial results from work in progress and plans for future work, including an expansion of the reconstruction tool to provide 3D reconstruction capabilities and a line-of-sight calculation tool.

software, python↗

NASA NTRS2008

The Heliophysics Data Environment Today

Driven by the nature of the research questions now most critical to further progress in heliophysics science, data-driven research has evolved from a model once centered on individual instrument Principal investigator groups and a circle of immediate collaborators into a more inclusive and open environment where data gathered ay great public cost must then be findable and useable throughout the broad national and international research community. In this paper and as an introduction to this special session, we will draw a picture of existing and evolving resources throughout the heliophyscs community, the capabilities and data now available to end users, and the relationships and complementarity of different elements in the environment today. We will cite the relative roles of mission and instrument data centers and resident archives, multi-mission data centers, and the growing importance of virtual discipline observatories and cross-cutting services including the evolution of a common data dictionary. We will briefly summarize our view of the most important challenges still faced by users and providers, and our vision in ow the efforts today can evolve into a more and more enabling data framework for the global research community to tap the widest range of existing missions and their data to address a full range of critical science questions from the scale of microphysics to the heliospheric system as a whole.

Fung, Shing F.↗

NASA NTRS2007

SERVIR: From Space to Village. A Regional Monitoring and Visualization System For Environmental Management Using Satellite Applications For Sustainable Development

NASA is committed to providing technological support and expertise to regional and national organizations for earth science monitoring and analysis. This commitment is exemplified by NASA's long-term relationship with Central America. The focus of these efforts has primarily been to measure the impact of human development on the environment and to provide data for the management of human settlement and expansion in the region. Now, NASA is planning to extend and expand this capability to other regions of the world including Africa and the Caribbean. NASA began using satellite imagery over twenty-five years ago to locate important Maya archeological sites in Mesoamerica and to quantify the affect of deforestation on those sites. Continuing that mission, NASA has partnered with the U.S. Agency for International Development (USAID), the World Bank, the Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC) and the Central American Commission for Environment and Development (CCAD) to develop SERVIR (Sistema Regional de Visualizacion y Monitoreo), for the Mesoamerican Biological Corridor. SERVIR has become one of the most important aspects of NASA's geospatial efforts in Central America by establishing a common access portal for information that affects the lives, livelihood and future of everyone in the region. SERVIR, most commonly referred to as a regional visualization and monitoring system, is a scientific and technological platform that integrates satellite and other geospatial data sets to generate tools for improved decision-making capabilities. It has a collection of data and models that are easily accessible to earth science managers, first responders, NGO's (Non-Government Organizations) and a host of others. SERVIR is currently used to monitor and forecast ecological changes as well as provide information for decision support during severe events such as forest fires, red tides,and tropical storms. Additionally, SERVIR addresses the nine societal benefit areas of the Global Earth Observation System (GEOSS): disasters, ecosystems, biodiversity, weather, water, climate, health, agriculture and energy.

Sever, Tom↗

NASA NTRS2015

The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.

farm crops↗

NASA NTRS2010

GLGM-3: A Degree-ISO Lunar Gravity Model from the Historical Tracking Data of NASA Moon Orbiters

In preparation for the radio science experiment of the Lunar Reconnaissance Orbiter (LRO) mission, we analyzed the available radio tracking data of previous NASA lunar orbiters. Our goal was to use these historical observations in combination with the new low-altitude data to be obtained by LRO. We performed Precision Orbit Determination on trajectory arcs from Lunar Orbiter 1 in 1966 to Lunar Prospector in 1998, using the GEODYN II program developed at NASA Goddard Space Flight Center. We then created a set of normal equations and solved for the coefficients of a spherical harmonics expansion of the lunar gravity potential up to degree and order 150. The GLGM-3 solution obtained with a global Kaula constraint (2.5 x 10(exp -4)/sq l) shows good agreement with model LP150Q from the Jet Propulsion Laboratory, especially over the nearside. The levels of data fit with both gravity models are very similar (Doppler RMS of approx.0.2 and approx. 1-2 mm/s in the nominal and extended phases, respectiVely). Orbit overlaps and uncertainties estimated from the covariance matrix also agree well. GLGM-3 shows better correlation with lunar topography and admittance over the nearside at high degrees of expansion (l > 100), particularly near the poles. We also present three companion solutions, obtained with the same data set but using alternate inversion strategies that modify the power law constraint and expectation of the individual spherical harmonics coefficients. We give a detailed discussion of the performance of this family of gravity field solutions in terms of observation fit, orbit quality, and geophysical consistency.

Mazarico, E.↗

NASA NTRS1985

Radiometric considerations in remote sensing

The need for accurate radiometric data for the verification and use of science radiation models is emphasized. The radiometric problems associated with reflectance and atmospheric correction field measurements and sensor calibration are reviewed. Estimates are made of the attainable accuracy in each case under favorable conditions. The loss in radiometric accuracy by resampling procedures in digital imagery processing is discussed.

Slater, P. N.↗

NASA NTRS1991

The High Performance Computing Initiative

The paper discusses NASA High Performance Computing Initiative (HPCI), an essential component of the Federal High Performance Computing Program. The HPCI program is designed to provide a thousandfold increase in computing performance, and apply the technologies to NASA 'Grand Challenges'. The Grand Challenges chosen include integrated multidisciplinary simulations and design optimizations of aerospace vehicles throughout the mission profiles; the multidisciplinary modeling and data analysis of the earth and space science physical phenomena; and the spaceborne control of automated systems, handling, and analysis of sensor data and real-time response to sensor stimuli.

Holcomb, Lee B.↗

NASA NTRS2024

Dual-Resonator Kinetic-Inductance Detector for Distinction between Signal and 1/ƒ Frequency Noise

Astronomical kinetic inductance detectors (KIDs), similar to quantum information devices, experience performance-limiting noise from materials. In particular, 1/ƒ (frequency) noise arises from two-level system defects (TLSs) in the circuit dielectrics and material interfaces and can be a dominant noise mechanism. Here, we present a dual-resonator KID (DuRKID), which is designed for improved noise-equivalent power relative to standard 1/ƒ -noise-limited KIDs. In this study we present the DuRKID schematic, a fabricated example, our first measurement results, a theoretical model including 1/ƒ noise, and a system-noise model containing additional noise sources. The circuit consists of two superconducting resonators sharing an electrical capacitance bridge of four capacitors, each of which hosts TLSs. The device is intended to operate using hybridization of the modes, which causes TLSs to either couple to one mode or the other, depending upon which capacitor they reside in. In contrast, the signal will affect a resonator inductance and, due to mode hybridization, this causes correlated frequency changes in both modes. Therefore, one can better distinguish the photon signal from the TLS frequency noise. To achieve hybridization, a TiN inductor is current biased to allow tuning of one bare-resonator mode into degeneracy with the other. Measurements show that the resonator modes hybridize as expected. The inter-resonator coupling and unintentional coupling of the two resonators to transmission lines are also characterized in measurements. A quantum information-science model allows device-parameter extraction from experimental data and a 1/ƒ noise analysis with uncorrelated noise. A system-noise analysis of the DuRKID, with comparisons to standard KIDs, is performed with generation-recombination noise and amplifier noise. The study reveals that the DuRKID can exhibit a large performance advantage over TLS-limited KID detectors.

N Forouzani↗

NASA NTRSDate not supplied

Recent Progress at the International Laser Ranging Service (ILRS)

The International Laser Ranging Service (ILRS) is experiencing significant growth. New stations are being built and brought into operation, expanding the spatial and temporal coverage of the Satellite Laser Ranging (SLR) observations. Some of these stations are multi-technique Core Sites that allow us to exploit the combined strengths of different geodetic space techniques. New satellites are strengthening the ILRS contribution to the International Terrestrial Reference Frame (ITRF), and expanding the spectrum of satellite applications supported by the ILRS. New analysis, modeling, and data processing techniques continue to improve the science products. New campaigns are continuing to expand SLR applications into relativity and the study of non-gravitational forces. The ILRS is currently participating in the Galileo for Science campaign (G4S_2.0 project), funded by the Italian Space Agency (ASI), which aims to perform a set of measurements in the field of gravitation with the Galileo satellites, taking advantage of the accuracy of the on-board atomic clocks and, in particular, of GSAT0201and GSAT0202 by exploiting their relatively high eccentricity (~0.16). New activities underway in Lunar Laser Ranging (LLR), with the imminent launch and deployment of the upcoming Next Generation Lunar Retroreflector - 1 (NGLR-1) to the lunar surface. Some new satellites supported by the ILRS are now exploring means of capturing Space Debris, while some ILRS stations run local laser ranging projects to track space debris, contributing to the maintenance of various data catalogs. Time transfer experiments are also expanding the application of laser ranging, linking two fully characterized optical clocks at different locations with SLR (Einstein Synchronization) providing measurements of height differences, demonstrating the feasibility of a physical height system. Two-way and one-way transponders activity has been underway since the LASSO experiment in the 1990’s. More experiments are underway or planned. Applications include time and frequency transfer and clock synchronization between ground and space, interplanetary laser ranging, and fundamental physics. This presentation will give an update on ILRS activities and the impact that these activities have had and will have on ILRS data products.

Lunar Laser Ranging↗

NASA NTRSDate not supplied

An Analytic Collaborative Framework for the Earth System Observatory

NASA's Earth System Observatory groundbreaking observations will provide critical measurements to address societal relevant problems in climate change, natural hazard mitigation, fighting forest fires, and improving real-time agricultural processes. Central to the ESO vision is the notion of Open-Source Science (OSS), a collaborative culture enabled by technology that promotes the open sharing of data, information, and knowledge aiming to facilitate and accelerate scientific understanding, and the agile development of applications for the benefit of society. The larger vision of an Earth System Digital Twin (ESDT) calls for integrated Earth science frameworks that mirror the Earth by a proxy digital construct that includes km-scale resolution Earth system models and data assimilation systems along with an integrated set of analytic tools to enable the next generation of science discoveries and evidence-based decision making. The goal of this project is to develop an Analytic Collaborative Framework for ESO missions, based on realistic, science-based observing system simulations and the Program of Record (PoR). Tying it all together is a cloud-based cyberinfrastructure that will enable each uniquely designed satellite in the Earth System Observatory to work in tandem to create a 3D, holistic view of Earth. In this presentation, we lay the technological groundwork for enabling such a vision. Our approach consists of the 3 main interconnected building blocks: 1. Cloud-optimized representative datasets for ESO missions and the PoR to serve as basis for developing and prototyping an Analytic Collaborative Framework. 2. An Algorithm Workbench for enabling experimentation and exploration of synergistic algorithms not only for instruments within a mission, but also including the PoR and other ESO missions. 3. A series of concrete Open-Source Science demonstrations including use cases that span science discovery and end-user applications with direct societal impact. While our ultimate goal is to include all of the main missions comprising the Earth System Observatory, in our initial 2 years we will focus on AOS and SBG, two missions for which specific synergisms have been identified in a recent workshop. In this presentation we will describe our approach and discuss some illustrative examples of our framework.

Arlindo da SIlva↗