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NASA NTRS2024

Improvements to GCAS Visualization Functionality

Analysis of communication systems is crucial to NASA objectives, whether located here on Earth or on a cosmic scale. The Glenn Research Center Communication Analysis Suite (GCAS) is software designed for analysis of communication systems throughout the solar system. This suite includes web-based visualization software that is used to simulate the data generated from the GCAS MATLAB® (The MathWorks, Inc.) capability. This report focuses on recent additions to the visualization portion of GCAS, including the motivation and methodology behind them. Notable additions include improved camera controls, such as novel first- and third-person settings, methods for displaying pertinent analysis information, finer control over timing mechanisms, and automated features designed to improve efficiency and memory usage.

Visualization↗

NASA NTRSDate not supplied

From CMS to the U.S. Greenhouse Gas Center – Improved Tracking of Recent Changes in Co 2 and Ch 4 From NASA’s Quasi-Operational Modeling Systems

Reliable, low latency delivery of high quality global flux and concentration information is a growing but still unmet need to advance expanding measurement, monitoring, reporting, and verification efforts that underpin federal climate mitigation strategies. Here we present on progress toward developing space-based greenhouse gas (GHG) monitoring systems that can provide comprehensive information trailing real time by a matter of weeks to a few months. Through support from its Carbon Monitoring System (CMS) program, NASA has developed the capability to assimilate XCO 2 retrievals from the Orbiting Carbon Observatory, 2 (OCO 2 ) into the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS) to create gap-filled, three-dimensional (3D) estimates of CO 2 mixing ratio. When observations are unavailable, concentration fields are further informed by observationally-informed bottom-up flux packages that incorporate remotely sensed fire, nighttime lights, and vegetation observations combined with estimates of atmospheric growth rate based on surface in situ data. The 3D nature of these datasets supports evaluation with independent aircraft data to improve confidence in satellite data, development of new regional modeling approaches, and quantification of the climate impacts of GHGs. The system has recently been expanded to assimilate XCH 4 from ESA’s TROPOspheric Monitoring Instrument (TROPOMI) instrument and is supported by companion efforts to improve delivery of estimates of bottom-up land and ocean fluxes. The quasi-operational GEOS-GHG system is contributing to the recently announced U.S. Greenhouse Gas Center (GHG Center) by delivering information on recent changes in CO 2 and CH 4 emissions and concentrations to support stakeholder and research communities. In this presentation, we provide an overview of the current system configured to support the GHG Center. We highlight examples of how this data contributes to broader NASA initiatives including the Earth Information Center, an innovative virtual and physical exhibit designed to show how NASA data helps the nation combat climate change. We conclude by discussing how innovations in CMS research can address remaining data gaps and modeling challenges to advance operational GHG monitoring in the future.

Lesley Ott↗

NASA NTRS2024

Improved Chemistry and Attenuation Models for Communication Blackout Simulation During Mars 2020 Entry

As a blunt body enters a planetary atmosphere, a plasma forms in the hypersonic shock layer and attenuates radio communication causing signal blackout for some duration of the entry sequence. In our previous work,1 computational fluid dynamics (CFD) was applied to model the entry flow around the Mars 2020 spacecraft, including ionization and electron density throughout the flow field, and predict ultra-high frequency (UHF) radio wave attenuation due to electrons. In total, 17 chemical species and their spatial profiles are modelled around the Mars 2020 spacecraft at 11 different points in time during entry. Although the simulation predicted the onset of attenuation well, the timing of the end of the predicted blackout window significantly preceded the end time observed during the 2021 landing. The present work seeks to improve the attenuation model by accounting for the fact that electrons undergo collisions with heavier species in the flow, which is an effect that was neglected in previous analyses. It is determined that including electron collisions increases the overall magnitude of attenuation predicted especially towards the end of the measured attenuation period, improving qualitative agreement between predicted and measured attenuation to both spacecraft receiving the signal from Mars 2020. To explore the remaining uncertainty in signal attenuation predictions further, a sensitivity study is performed to investigate the impact of associative ionization and electron-impact ionization rate coefficients on the electron density predicted by CFD and on the resulting attenuation predictions. These coefficients are believed to contain up to order-of-magnitude uncertainty, and therefore may significantly affect the number density of electrons throughout the flow field. Variations in associative ionization coefficients demonstrate significant impact on the magnitude of attenuation due to variation in the electron density coming from associative ionization. However, the start and end times of the predicted signal attenuation period are only slightly impacted by said variation.

Eve Papajak↗

NASA NTRS2024

Improving the Fire Weather Index System for Peatlands Using Peat-Specific Hydrological Input Data

The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWIpeat, was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWIpeat should enable improved monitoring of fire risk in boreal peatlands.

Canadian Fire Weather Index↗

NASA NTRSDate not supplied

Improvements of the Load Schedule for the Machine Calibration of a Strain-Gage Balance

The load schedule for the calibration of a six-component force balance in a calibration machine was improved. Now, single-component loads are repeated in regular intervals during the calibration. This approach has several advantages. First, the number of single-component loads increases to about twenty-two percent of all loads and load combinations. Consequently, more accurate numerical estimates of the primary bridge sensitivities can be obtained if global regression is used for the analysis of the calibration data. In addition, single-component repeats make it possible to track the stability of the applied loads during the calibration process. Finally, interactions of single-component repeats can be compared with interactions that are observed during the application of manual loads to the balance. Machine calibration and manual data sets of two force balances are used to illustrate benefits of the new load schedule. It is shown in the examples how differences between the observed interactions of machine calibration and manual data can be quantified. The suggested improvements can also be implemented in the load schedule for the machine calibration of a moment or direct-read balance as long as single-component loads are included that are described in the design load format of the balance.

wind tunnel test↗

NASA NTRSDate not supplied

Lessons Learned from NASA Goddard Space Flight Center’s Product Development Lead Training Schedule and Cost Development Workshop: Continuous Improvement

This presentation provides a status of the Goddard Space Flight Center (GSFC) effort to increase foundational knowledge of Product Development Leads (PDLs) in schedule and cost management including earned value management (EVM). In 2012, GSFC’s Engineering and Technology Directorate (ETD) implemented an in-house training program to prepare PDLs for managing the technical, cost, schedule, and risk aspects of spaceflight systems to meet their subsystem commitments. Developed in-house, the PDL training program provides an integrated approach to requirements development, risk, schedule and cost management, EVM, performance tracking, and other areas. The program has been held twice yearly since its inception with 531 participating and 451 completing the curriculum. In 2017, the program won the Robert H. Goddard award for Quality and Process Improvement. Program development and evolution were presented in the 2018 NASA Schedule and Cost Symposium. The presentation was so well received that this year we focus on one workshop within the program: Schedule and Cost Development, including EVM. We examine the on-going logic modeling process and how participant and stakeholder data influence workshop content and design, and how the disciplines of schedule and cost contribute to mission success. In this presentation we refresh you on how the approach integrates lecture, small group discussion, estimating, case study exercises, and problem solving. We update you on the data collected from participants and stakeholders, and we discuss how we use these data to measure training effectiveness. Specific topics include: • How the logic model is used as the backbone for continuous program improvement, • How feedback influences implementation and curriculum updates, • How data collection and analysis inform workshop content and development, including participant discoveries of EVM data, • How including the resource analyst and planner in the product development team supports project success.

Lessons Learned↗

NASA NTRSDate not supplied

Improved GEO Derived LW Broadband Fluxes for the Edition5 CERES SYN1Deg Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides over 24 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The major level-3 data products include SSF1deg, SYN1deg, FluxByCldTyp, and EBAF data. The CERES SYN1deg product provides 1° gridded observed TOA fluxes and computed surface fluxes based on hourly GEO clouds and derived broadband TOA fluxes. The hourly GEO derived fluxes fill in the temporal gaps between the Terra and Aqua CERES observations. In order to maintain a climate quality dataset the GEO cloud and flux retrievals are consistent over the record. The CERES project is preparing for Edition 5 product providing an opportunity to improve the GEO flux retrieval algorithms. To achieve this, the GEO satellite channel radiances are calibrated against the Aqua-MODIS calibration reference. For SW the GEO channel radiances are then converted to broadband radiances using empirical and theoretical models. The SW broadband radiances are then converted to fluxes using the same CERES angular directional models. For LW the GEO channel radiances are directly converted to LW fluxes utilizing empirical models. The resulting GEO derived broadband fluxes are then normalized to the CERES fluxes regionally by regressing the instantaneous coincident flux pairs. The normalized GEO fluxes are then tied to the CERES instrument calibration, which is very stable over time. This study will attempt to improve the GEO derived LW fluxes based on machine learning technique for the future Edition5 SYN1deg data product.

Moguo Sun↗

NASA NTRSDate not supplied

Improvements to GlennICE Collection Efficiency Algorithm

GlennICE (Glenn Icing Computational Environment) is a computational tool designed to calculate ice growth on complex three-dimensional geometries using the input from a user-supplied computational fluid dynamics (CFD) solution for the geometry of interest. The NASA John H. Glenn Research Center at Lewis Field is developing this tool to aid those evaluating, designing and certifying aircraft, engines, and aircraft components for flight in icing conditions. This domestically available software is being developed to enable the introduction of new icing physics into a computational environment in a manner that is open for evaluation and eventual use by industry, academia, and other government organizations. This paper will document recent improvements that reduce the number of trajectories needed to converge on collection efficiency. These improvements are documented using example cases with complex geometries.

Aircraft Icing↗

NASA NTRSDate not supplied

Improvements to GlennICE Collection Efficiency Algorithm

Aircraft Icing↗

NASA NTRSDate not supplied

New Capabilities and Improvements to the High-Order Glenn Flux Reconstruction Code

The Glenn Flux Reconstruction (GFR) code is a computational fluid dynamics (CFD) code under development at NASA Glenn Research Center. GFR is based on the high-order flux reconstruction (FR) method and provides a large-eddy simulation (LES) capability that is both accurate and efficient for complex aeropropulsion flows. Three significant new capabilities have been added to the code that improve its performance and functionality. First, a variety of explicit Runge-Kutta methods, including some with adaptive time stepping, were added to GFR with two methods offering a 33% improvement in time-to-solution. Second, GFR can now utilize fully unstructured, mixed-element meshes to more easily facilitate the grid generation process for complex geometries. Finally, a rotating reference frame capability has been added to GFR for solving rotating turbomachinery problems. A selection of results demonstrating these new capabilities are presented in this work. The Taylor-Green vortex problem is used to verify the new unstructured capability by showing similar accuracy and resolution for all element types. LES of the Turbulent Heat Flux Phase III (THX3) experiment with comparison to another high-order LES code and a popular Reynolds-averaged Navier-Stokes (RANS) code demonstrates the accuracy of the code for complex aeropropulsion flows. Finally, LES of a spacecraft cabin ventilation fan shows the ability of GFR to efficiently establish a fan performance map and identify operating points for further analysis at high orders of accuracy.

High-Order Methods↗

NASA NTRSDate not supplied

SAGE III/ISS Data Product Improvements

In 2024 the Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) entered its eighth year of operations. Using a CCD spectrometer to observe solar and lunar occultations through Earth’s limb, the mission has retrieved tens of thousands of vertical profiles for ozone, aerosol, water vapor, and nitrogen dioxide. Retrieval algorithms are constantly under development to enhance these products. The next product version will improve upon previous versions by addressing problems with the aerosol extinction spectrum and recovering hundreds of profiles impeded by the recent proliferation of sunspots. Users of SAGE III/ISS data can also expect improvements to uncertainty estimates and a continued expansion of ancillary products.

Robert Manion↗

NASA NTRSDate not supplied

Evaluation of the Recent Improvements of the Nowcast of Aerospace Ionizing Radiation System (NAIRAS)

The Nowcast of Aerospace Ionizing Radiation System (NAIRAS) model is a real-time, global, physics-based model used to assess radiation exposure now running in real-time and in run on request (RoR) mode at NASA Goddard’s Community Coordinated Modeling Center. NAIRAS was recently updated to extend the galactic cosmic ray (GCR) model to include ultra-heavy nuclei (Z=29-92, A=64-238) for single event effects assessment from high linear energy transfer processes, to expand the geomagnetic cutoff rigidity model to use the either the TS05 (Tsyganenko and Sitnov, 2005), T89 (Tsyganenko, 1989), or the International Geomagnetic Reference Field model (IGRF) magnetic field models, and to improve the solar energetic particle (SEP) proton spectral fitting to better represent relativist protons during ground level enhancements. Here, we evaluate the recent NAIRAS improvements for a range of conditions. First, we demonstrate the effect of choice in magnetic field model to the NAIRAS computed dosimetric quantities for a United States domestic flight and a transatlantic flight during the May 11, 2024 SEP event. Second, the effect of ultra-heavy ions on SEP dose rate is examined for two different flight trajectories during the top SEP events. Lastly, the effect of ultra-heavy ions on NAIRAS computed GCR dose rates at solar minimum and maximum is demonstrated.

Daniel B Phoenix↗

NASA NTRS2024

Improved alternate wetting and drying irrigation increases global water productivity

Rice is the staple food for half of the world’s population but also has the largest water footprint among cereal crops. Alternate wetting and drying (AWD) is a promising irrigation strategy to improve paddy rice’s water productivity—defined as the ratio of rice yield to irrigation water use. However, its global adoption has been limited due to concerns about potential yield losses and uncertainties regarding water productivity improvements. Here, using 1,187 paired field observations of rice yield under AWD and continuous flooding to quantify AWD effects (ΔY), we found that variation in ΔY is predominantly explained by the lowest soil water potential during the drying period. We estimate that implementing a soil water potential-based AWD scheme could increase water productivity across 37% of the global irrigated rice area, particularly in India, Bangladesh and central China. These findings highlight the potential of AWD to promote more sustainable rice production systems and provide a pathway toward the sustainable intensification of rice cultivation worldwide.

Irrigation↗

NASA NTRSDate not supplied

Machine Learning Application for Improving Cloud Detection and Phase Determination Over Sunglint Regions for Geostationary Satellites

Cloud detection and phase determination over sunglint regions has been a challenge, especially for geostationary (GEO) satellites. Sunglint is observed when the sunlight specular reflection is at the same viewing angle of the satellite sensor. This intense reflection in the visible channels (VIS) is often comparable to that from optically thick clouds. It also contaminates the shortwave infrared channels (SWIR). Consequently, VIS and SWIR channels become less useful - or not useful- when they are saturated, hampering the detection of cloudy and clear-sky pixels. Sunglint contamination happens frequently and exists nearly in every daytime GEO full disk satellite images. However, sunglint intensity and region are difficult to model due to variable viewing geometry and ocean surface conditions. Moreover, existing physical models do not meet the accuracy required for operational GEO satellite cloud detection. We developed a machine learning algorithm to improve cloud detection in sunglint conditions for the NASA Langley’s Satellite ClOud and radiation Property retrieval System (SatCORPS). This poster presents our recent progress in the algorithm development, validation and applications. The algorithm is validated using collocated SatCORPS GOES-East and GOES-West cloud products. We demonstrate that the machine learning cloud detection in sunglint regions is superior to the traditional approach by improving temporal consistency between sunglint and non-sunglint conditions.

Machine Learning, Cloud detection, Sunglint, SatCO↗

NASA NTRSDate not supplied

New Capabilities and Improvements to the High-Order Glenn Flux Reconstruction Code

Direct Numerical Simulations↗

NASA NTRS2026

Improvement in the Thermal-to-Structural Model Mapping Process for Integrated Modeling for the Roman Space Telescope

Integrated Modeling has been a key component of verifying optical requirements for the Nancy Grace Roman Space Telescope (RST) that are either impossible or impractical to verify exclusively through ground testing. Two major areas for integrated Modeling are Jitter and Thermal Distortion that require the exchanges of model performance predictions across disciplines. In both cases, distortions are impressed on optical models to evaluate the impact on boresight alignment and wave front error. In the case of Jitter, the disturbances are driven by reactions to motions most often from actuators; however, in the case of thermal distortion, the motions are driven by thermal expansion or contraction as a result of changing temperatures. This then requires a link further upstream to the thermal model, which is used to predict the thermal performance and temperature gradients and stability. The process for mapping temperatures from a thermal model to a corresponding structural model has been performed numerous times through the RST project lifecycle, with improvements in the accuracy, verification, and effort sought throughout. This paper describes some of the recent improvements to the process, including: capture of the visualization parameters, automatic generation of the mapped images for both the thermal and structural model groupings, and reduction in the effort to assemble the full set of mapped temperatures. These upgrades have greatly reduced the manual effort associated with thermal mapping and allowed for faster turn-around of Integrated Modeling predictions.

Thermal Mapping↗

NASA NTRS2026

The Significance of Water Vapor Isotopes in Improving Weather Prediction

Water vapor isotopes carry the integrated history of evaporation, condensation, mixing, and transport. Although previous studies have shown potential to improve forecasts under controlled conditions, real-world applications have been limited by systematic biases in both models and satellite retrievals arising from sparse measurements in the free troposphere. Here we assimilate mid‑tropospheric δD retrievals (peak sensitivity ~4.2 km) from the Infrared Atmospheric Sounding Interferometer into the Isotope‑incorporated Global Spectral Model and evaluate the added value beyond co‑assimilated temperature and specific humidity with identical spatial and temporal coverage. Assimilating δD improves 0–120 h forecasts of wind, temperature, specific humidity, and geopotential height, with the largest gains in the midlatitudes; heavy‑precipitation skill also increases for thresholds >3 mm per 6 h. Demonstrated in a coarse‑resolution configuration with limited observations, the results indicate that isotopic information strengthens transport tracking and hydrological constraints, motivating evaluation in operational high‑resolution forecasting systems.

Hydrology↗

NASA NTRS1995

Recent Improvements to the Airborne Visible/Infrared Imaging Spectrometer (AVRIS)

Several improvements have been made to the Airborne Visible/Infrared Imaging Spectrometer (AVRIS) since 1994--new focal plane arrays, a new analog and digital chain and an onboard calibration lamp controlled by radiance feedback. These changes increased the signal-to- noise ratio by 2 to 3 times, eliminated noise spikes and the need for spectral sampling, and greatly reduced dark-current noise.

AVRIS improvements spectrometer↗