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NASA NTRSDate not supplied

MODIS Reflective Solar Band Calibration Improvements using Pseudo-Invariant Desert Targets

To provide the best science data quality, an accurate characterization of the response versus scan angle (RVS) function is critical for the MODIS reflective solar bands (RSB) on-orbit calibration. In every MODIS operational scan, the Earth’s surface, referred to here as Earth view (EV), the space view (SV) port, and the onboard calibrators are viewed via a two-sided scan mirror. The RVS is defined as the sensor’s relative response as a function the angle of incidence (AOI) to the scan mirror. Many different approaches have been developed to derive the time-dependent RVS and its look-up table (LUT) applied to MODIS Level 1B (L1B) products since calibration Collection 4. For most MODIS RSB, the on-board calibrators can reasonably track the RVS change with time. In practice, their RVS is derived using data from on-board calibrators and the EV mirror side ratio (for mirror side 2). For Terra bands 1-4, 8-10 and Aqua bands 1-4, 8-9, an enhancement has been employed in Collections 6 and 6.1 (C6/C6.1) by using Earth scene response trending from pseudo-invariant desert sites in addition to the onboard calibrators. The current C6/C6/1 RVS algorithm is focused on fitting the EV data at each AOI over time and then deriving the relative change at different AOI. The EV response trending is currently fitted with multiple segments over time. Alternatively, the EV responses can be fit first as a function of AOI before fitting temporally in order to reduce the dependence on the stability of the desert site. These pre-treatment methods on the EV data provide improvement in the derived calibration coefficients. However, evidence of insufficient calibration is still observed in the MODIS L1B reflectance data, especially in the form of differences between the mirror sides. In this paper, we review the current methodologies that utilize the EV response trends from the pseudo-invariant Libyan desert targets to supplement the gain derived from the onboard calibrators. An improvement is then proposed and investigated such that a sliding window average (SWA) is used to pre-process the raw EV data. The SWA parameters are carefully selected using trade-off studies to accurately track the Earth scene response trending in multiple cases to overcome the reflectance differences between two mirror sides. Calibration results show improvements for both Aqua and Terra MODIS RSB L1B data products. This new adjustment has been included in the recently delivered Collection 7 LUT that will be evident in the L1B products expected to be released in late 2021.

MODIS↗

NASA NTRSDate not supplied

Neo – Mars Adaptive Training Integrative Knowledge System (MATRIKS) to Improve Operational Performance and its Neural Basis for Spaceflight

With prolonged mission durations, spaceflight crews will become increasingly dependent on onboard technologies for knowledge acquisition and maintenance. It is expected that not all skills and knowledge required for these missions can be retained and retrieved based on pre-mission training alone. Limited and delayed communication will significantly constrain support from Mission Control and crews will increasingly rely on autonomous onboard technologies to successfully perform post-landing operations. With the present project we will target NASA’s particular interest in developing and assessing an adaptive, just-in-time countermeasure that will consolidate and improve skills that are most relevant to space flight operations. To achieve this aim, NASA established a Virtual NASA Specialized Center of Research (VNSCOR)referred to as “Mars Adaptive Training Integrative Knowledge System (MATRIKS)”, comprising the following three projects: (1) “Trinity–Multi-Environment Virtual Training for Long Duration Exploration Missions”, PI: A. Anderson (UC Boulder); (2) “Morpheus–A Haptic Sensory Supplement to Optimize In-Flight Adaptive Training for Human Control of Spacecraft Robotic Arms”, PI: S. Robinson, UC Davis); and the present project “Neo–Adaptive Training integrative knowledge System to Improve Operational Performance and its Neural Basis for Spaceflight” (UPenn, PI: A.C. Stahn). Neo leverages a validated workstation called 6DF that simulates a rendezvous and docking maneuver using real spacecraft flight dynamics. It is designed to (1) train and improve sensorimotor skills relevant for inflight and post-landing operational tasks; (2) feature an autonomous and adaptive training approach that does not rely on feedback from flight operations on the ground; (3) maximize the transfer of mission-relevant motor skills; (4) allow the assessment of the neural circuitry underlying the task; and (5) deliver the training in a motivating and meaningful way to astronauts. Neocomprises two overarching aims: First, we will identify the neural circuitry underlying spaceflight relevant tasks by performing a subset of the 6DFtaskduring functional magnetic resonance imaging (MRI)in a total of up to N=30 subjects with varying levels of 6DF training experience. Second, as part of the above-mentioned VNSCOR MATRIKS the proposed 6DF autonomous intelligent tutor system will be integrated in an additive manner with a haptic feedback intervention (Morpheus), and a multi-environment virtual trainer(Trinity).It is expected that Neo, Morpheus and Trinity mutually complement each other to facilitate an effective countermeasure tool to acquire and retain operational skills that are critical for exploration class missions. To assess the efficacy of this combined effort, the VNSCOR MATRIKS will collect data inN=16 crew members in one HERA campaign of 45 days duration with N=16 crew members(four missions with N=4 crew member seach).The primary goal is to identify changes in operational performance as assessed by NASA’s simulator of Canadarm2 operations, i.e., Robotic On-board Trainer (ROBoT-r) in response to MATRIKS. As part of Neo we will also identify if, and to what extent MATRIKS will promote transfer to general cognitive performance (Cognition battery), distinctive visuo-spatial tasks critical for telerobotic tasks (Spatial Cognition battery), and affect brain structural changes and the neural circuitry of key brain networks expected to be relevant for spaceflight-related performance. At the conclusion of the research, we will have defined and demonstrated the use of a neuroscience-based, adaptive training integrative knowledge system to potentially mitigate visuo-spatial and sensorimotor brain changes associated with prolonged isolation and confinement to reduce the likelihood or impact of potential decrements in human performance capabilities during long-duration space missions. The expected significance of this 4-year project relates to its relevance for facilitating effective countermeasure tools to acquire and retain operational skills that are critical for exploration class missions. This will support the development of necessary countermeasures and technologies in support of human space exploration, focusing on mitigating operational performance risks.

A C Stahn↗

NASA NTRSDate not supplied

In Search of Data-Driven Improvements to RANS Models Applied to Separated Flows

The goal of this work is to improve the capability of Reynolds-averaged Navier-Stokes turbulence models for separated flows using data-driven enhancements. The resulting model should be “universal” in the sense that it can be used by anyone and applied to as many flows as possible without concern for unusual or detrimental behavior. At worst, the data-driven corrections should not degrade the accuracy of the baseline model (in this case the Spalart-Allmaras one-equation model), while preserving the Galilean invariance and similar theoretical qualities of the original model. In the literature, most current data-driven improvements to turbulence models are only applicable to very similar types of cases as those used to train the model for a specific class of flows. In this work, the impact of using a wide array of cases in the machine-learning training is described. Unwanted behaviors from trained neural networks are examined, and possible mitigation strategies are proposed. However, to date, consistent and broadly applicable data-driven improvements for separated flows have not been achieved.

turbulence modeling↗

NASA NTRSDate not supplied

Load Schedule Design Improvements for the Calibration of NASA's Semi-span Balances

Improvements of the load schedule design for the calibration of NASA's semi-span balances are discussed. These improvements became possible after a minor hardware change was made to Calspan's Large Load Rig. This change moves normal and axial force load points 12 inches closer to the balance moment center. Resulting data quality improvements are significant because Calspan's Large Load Rig cannot currently apply forces at the balance moment center of a semi-span balance. Now, the supported set of rolling and yawing moment arms is 28 inches, 38 inches, and 48 inches. Consequently, the new load schedule design better supports expected normal and axial force locations on semi-span models that are tested in the NASA Ames 11-ft Transonic Wind Tunnel. In addition, estimates of the primary sensitivities of the normal force, axial force, rolling moment, and yawing moment have become more accurate as the applied normal and axial forces have moved closer to the balance moment center. Fundamental moment arm range requirements and Calspan's hardware updates are discussed in detail. In addition, calibration analysis results of NASA's ARC30K semi-span balance are presented to illustrate benefits of the hardware changes.

wind tunnel test↗

NASA NTRS2021

Optimizing GRACE/GRACE-FO Data and A priori Hydrological Knowledge for Improved Global Terrestial Water Storage Component Estimates

The comprehensive information of global terrestrial water storage (TWS) components (soil moisture, groundwater, snow, surface water) is essential for effective assessment of water resource availability, climate variation, and disaster mitigation measures. Observational data provided by the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On satellite missions offer global TWS variation (ΔTWS) in terms of an integrated water column. However, GRACE spatial resolution is relatively coarse (i.e., 3°), and the vertically integrated value cannot be separated into ΔTWS components directly. This study demonstrates the feasibility to estimate ΔTWS components at any desired spatial-vertical resolution by effectively maintaining the native resolution of the employed hydrological knowledge. It utilizes a least-squares with constraints (LSC) approach to rigorously incorporate GRACE and GRACE-FO data and a priori hydrological knowledge, with the aim to improve global ΔTWS components’ accuracy and spatial resolution. The 3°×3° GRACE mascon derived ΔTWS data is disaggregated into the 0.5°×0.5° anomalous soil moisture storage (ΔSMS), groundwater storage (ΔGWS), snow water equivalent (ΔSWE), and surface water storage (ΔSWS) based on the covariance information obtained from the Community Atmosphere Biosphere Land Exchange (CABLE) and the PCRaster Global Water Balance (PCR-GLOBWB) models. Evaluation with different ground measurements and satellite products between 2002 and 2019 exhibits significantly improved accuracy in all individual ΔTWS components. This improvement is of particular note in ΔGWS and ΔSWS, where the LSC approach increases the globally averaged correlation values by approximately 0.13 and 0.05, respectively. Reliable prior knowledge leads to a more accurate ΔTWS component estimate, and the use of ensemble-mean knowledge yields the best result.

GRACE↗

NASA NTRSDate not supplied

Load Schedule Design Improvements for the Calibration of NASA's Semi-span Balances

Improvements of the load schedule design for the calibration of NASA's semi-span balances are discussed. These improvements became possible after a minor hardware change was made to Calspan's Large Load Rig. This change moves normal and axial force load points 12 inches closer to the balance moment center. Resulting data quality improvements are significant because Calspan's Large Load Rig cannot currently apply forces at the balance moment center of a semi-span balance. Now, the supported set of rolling and yawing moment arms is 28, 38, and 48 inches. Consequently, the new load schedule design better supports expected normal and axial force locations on semi-span models that are tested in the NASA Ames 11-ft Transonic Wind Tunnel. In addition, estimates of the primary sensitivities of the normal force, axial force, rolling moment, and yawing moment have become more accurate as the applied normal and axial forces have moved closer to the balance moment center. Fundamental moment arm range requirements and Calspan's hardware updates are discussed. In addition, calibration and check load analysis results of NASA's ARC30K semi-span balance are presented to illustrate benefits of the hardware changes.

wind tunnel test↗

NASA NTRSDate not supplied

Improving Regional Air Quality Forecasting Through Chemical Data Assimilation and Dynamic Emissions Adjustment

Poor air quality (AQ) is one of the most important human-health and environmental problems facing the United States (US). In addition to the detrimental impacts on human- and environmental-health, poor AQ has an economic cost of ~5% of the US gross domestic product (~$790 billion). AQ managers use AQ analyses and modeling to better understand, anticipate, and avoid poor AQ events. Our research focuses on improving AQ analysis/forecast skill, predictability, and emission estimates through improved and more efficient: (i) modeling and data assimilation strategies; (ii) dynamic emissions adjustment strategies; and (iii) use of satellite remote-sensing Earth observations (e.g., MOPITT, IASI, MODIS, OMI, TROPOMI, TEMPO, etc.). This seminar will review: (i) regional chemical weather forecasting/data assimilation with dynamic emissions adjustment with WRF-Chem/DART; (ii) strategies for efficiently assimilating satellite retrieval profiles with ‘compact phase space retrievals’ (CPSRs); (iii) results from joint assimilation of multiple satellite retrievals at medium (12 km × 12 km) and high (4 km × 4 km) spatial resolutions; and (iv) results from observing system simulation experiments (OSSEs) to investigate whether we can recover COVID-period anthropogenic emissions by assimilating synthetic TEMPO NO2 tropospheric column retrievals with dynamic emissions adjustment. Biographical Sketch: Dr. Mizzi is a Senior Research Fellow working and Dr. Johnson at the NASA Ames Research Center. He holds BA and MS degrees in Environmental Science from the University of Virginia, MS and PhD degrees in Applied Mathematics from the University of Colorado at Boulder (CUB), and a JD degree (with an emphasis in Environmental Law) from the University of Colorado School of Law. He worked at the National Center for Atmospheric Research for nearly 25 years on global atmospheric modeling, dynamic and physical initialization, regional hybrid data assimilation, and most recently on regional chemical data assimilation. He also worked as an environmental attorney and consultant for nearly 15 years. He is an expert in numerical modeling and is recognized internationally as a leading expert in regional, chemical data assimilation with dynamic emissions adjustment. Dr. Mizzi became affiliated with NASA Ames in March 2020 to work on improving AQ analysis/forecast skill, predictability, and ‘top-down’ emissions adjustment though the assimilation of Earth observations. An emphasis of his current work is developing methods for assimilating synthetic TEMPO retrievals to quantify the expected benefits of TEMPO relative to existing AQ observations.

Arthur P. Mizzi↗

NASA NTRS2019

Isotopologue Consistency of Semi-Empirically Computed InfraRed Line Lists and Further Improvement for Rare Isotopologues: CO2 and SO2 Case Studies.

The semi-empirical molecular rovibrational IR line lists, such as ExoMol, TheoReTs, and Ames, combine the experimental accuracy and theoretical power to reach better than 0.1 cm-1 accuracy for line positions and better than 80-90% agreement for line intensities. The quality of these existing semi-empirical IR lists allows further improvements of intensity and line positions for those unobserved minor isotopologues. This paper presents our new BTRHE (Best Theory + Reliable High-resolution Experiment) strategy implementation. For line intensity, the isotopologue consistency and the patterns of mass dependence in the Ames-296K SO2 and CO2 IR lists are quantitatively presented along the mass-inverse coordinates. The consistency and patterns are better than those in existing experimental data. The methodology proposed here can be used to identify inconsistencies, outliers, and mistakes in intensities, and help improve Effective Dipole Model (EDM) and molecular IR databases. We call for an experimental study on the 50006 and 60007 bands of CO2 628. For line position predictions, a simple approach combining the variational IR line lists with Effective Hamiltonian (EH) model may refine the effective rotational constants A0/B0/C0 and quartic centrifugal distortion constants of minor isotopologues. The prediction accuracy may be improved by two orders of magnitude, i.e. reaching 0-5 MHz prediction accuracy in the range of J<20-30, Ka<10-20, and 0.01-0.02 MHz accuracy for A0/B0/C0. Several important factors have been systematically investigated and discussed, e.g. convergence, uncertainties, higher order terms, fixing EH parameters, mass coordinates, etc. Amicrowave (MW) line set consisting of 644,636 strong transitions for all 30 isotopologues and corresponding refined EH(Ames) parameters are reported in the supplementary material. This approach may be easily extended to rovibrational bands, hot bands, and other molecular systems.

Xinchuan Huang↗

NASA NTRSDate not supplied

TROPOMI Geometry-dependent Lambertian-Equivalent surface Reflectivity (GLER) Product for Improved Trace-Gas Retrieval

Accurate information about the reflectivity of the Earth's surface is required for most satellite retrievals of atmospheric composition, and this information is generally taken from monthly surface reflectivity climatology that neglects angular dependence. Previously we introduced Geometry-dependent Lambertian-equivalent surface reflectivity (GLER) which captures solar and satellite viewing angle dependence as well as daily and seasonal changes. GLER is calculated from simulations of Rayleigh-only top-of-atmosphere (TOA) radiances over non-Lambertian surfaces. We use NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) product over land and the wind-dependent Cox–Munk wave-facet slope distribution including water-leaving radiance over water to accounts for surface BRDF. We have developed global GLER product, previously for the Ozone Monitoring Instrument (OMI) and recently for Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) with several new improvements and updates. We have implemented the near real time daily V006 MODIS MCD43C1 BRDF data and gap-filled with a daily BRDF coefficient climatology created from 2002-2017 V006 MCD43GF data. The NASA’s Global Modelling Initiative hourly 0.25 x 0.25 deg Replay simulations are used for more accurate determination of pixel specific terrain pressure. To improve detection of seasonal snow/ice scenes, we use the 4-km snow cover product from the Interactive Multi-sensor Snow and Ice Mapping System (IMS). Finally, we use an improved version of the vector linearized discrete ordinate radiative transfer (VLIDORT) for update of the top-of-atmosphere (TOA) radiance look-up-tables (LUTs). We demonstrate how the use of GLER is beneficial to TROPOMI’s high spatial resolution (up to 3.5 km x 3.5 km) measurements to monitor atmospheric trace gas pollutants down to the sub-city scale.

TROPOMI↗

NASA NTRSDate not supplied

Improved Peatland Hydrology and L-Band Microwave Radiative Transfer Modeling in Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model. The recently released Version 7 of L4_SM comprises two key advances. First, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Second, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, the L-band scattering albedo, soil roughness, and vegetation opacity climatology are obtained from the SMAP Level-2 dual-channel soil moisture retrieval product. In this presentation, we examine the improvements gained with the model and parameter changes. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved in L4_SM Version 7 over Version 6 when evaluated against in situ measurements. These improvements are also manifested in smaller Tb observation-minus-forecast residuals.

SMAP↗

NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗

NASA NTRS2022

Improved Global Wetland Carbon Isotopic Signatures Support Post-2006 Microbial Methane Emission Increase

Atmospheric concentrations of methane, a powerful greenhouse gas, have strongly increased since 2007. Measurements of stable carbon isotopes of methane can constrain emissions if the isotopic compositions are known; however, isotopic compositions of methane emissions from wetlands are poorly constrained despite their importance. Here, we use a process-based biogeochemistry model to calculate the stable carbon isotopic composition of global wetland methane emissions. We estimate a mean global signature of −61.3 ± 0.7‰ and find that tropical wetland emissions are enriched by ~11‰ relative to boreal wetlands. Our model shows improved resolution of regional, latitudinal and global variations in isotopic composition of wetland emissions. Atmospheric simulation scenarios with the improved wetland isotopic composition suggest that increases in atmospheric methane since 2007 are attributable to rising microbial emissions. Our findings substantially reduce uncertainty in the stable carbon isotopic composition of methane emissions from wetlands and improve understanding of the global methane budget.

Methane↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

Carter Hulsey↗

NASA NTRS2023

PCRTM-RA Enhancements for Improving CO Retrievals Using NAST-I Measurements From the FIREX-AQ Field Campaign

The Principal Component-based Radiative Transfer Model Retrieval Algorithm (PCRTM-RA) for carbon monoxide (CO) retrieval has been improved for better use of National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. One of the explicit purposes of the campaign was to characterize wildfire-induced atmospheric changes. Coincidental measurements from various airborne instruments including NAST-I infrared hyperspectral measurements from the NASA ER-2 aircraft provided us an opportunity to test and improve the PCRTM-RA CO retrieval. By relaxing the vertical correlation of CO profiles in the a-priori covariance constraint, a significant improvement in the vertical structure of the CO retrieval has been confirmed. The methodology is validated using a synthetic testing dataset that covers observations associated with various CO vertical profiles including that of an extremely polluted atmosphere. The methodology is also applied to real NAST-I measurements, and the results have successfully demonstrated the capability of using PCRTM-RA retrieval results for CO plume evolution and transport monitoring.

Carbon monoxide (CO)↗

NASA NTRSDate not supplied

Open Science for Plants in Space: Improvements in NASA's Open Science Data Repository

Upcoming deep space missions will rely on plants for crew and ecosystem health. Open access space biology data enables scientists to examine the biological responses of plants to ionizing radiation, altered gravity, elevated CO2, and many other abiotic stressors. NASA has declared 2023 as the ‘Year of Open Science’ and created a 5-year Transform to Open Science (TOPS) initiative designed to rapidly transform the agency toward an inclusive culture of open science. NASA’s Open Science Data Repository (OSDR) combines two databases, GeneLab and Ames Life Sciences Data Archive (ALSDA) to maximize access to standardized ‘omics (e.g., transcriptomics, proteomics) and phenotypic data (e.g., microscopy, biomass), respectively. Current OSDR standards include the ISA (Investigation-Study-Assay) experiment model, assay metadata configurations, and standardized terminology and ontologies. In 2024 OSDR will include a new suite of features for improved FAIR compliance including downloadable plant metadata templates, data submission tools and overall improved AI-readiness of plant datasets. AI/ML methods can be helpful tools to overcome the inherent challenges of space biology research (small sample size, sparse and heterogeneous data etc.). However these methods are built on an assumption of normalized and well-curated data. OSDR’s new curation tools will improve users ability to leverage ML and AI methods to model space biology data and better understand the complex effects of spaceflight on living systems across hierarchical biological levels. We look forward to sharing our advances with the spaceflight community.

FAIR↗

NASA NTRSDate not supplied

An Improved Approach to the Predictability & Reliability of the Onset of Turbulence With Shocks

The construction of numerical schemes for (a) stable and accurate simulation of turbulence with strong shocks, and for (b) obtaining correct propagation speed of discontinuities in the presence of stiff source terms share one important ingredient – minimization of numerical dissipation while maintaining numerical stability. The dual requirements to achieve both numerical stability and minimal numerical dissipation are often conflicting since existing shock capturing schemes were designed mainly to be robust for rapidly developed turbulence-free flows and for shock waves without stiff source term. For the past two decades, Yee and collaborators have focused on an improved understanding of the nonlinear behavior of different high order shock-capturing methods. It was found that even very high order methods without proper nonlinear stability and numerical dissipation control can either numerically smear the onset of turbulence due to excess numerical dissipation, or induce (onset) numerical turbulence that is not physical turbulence due to lack of proper numerical dissipation to improve nonlinear stability for long time integration. Our approach is to combine (I) and (II) below for obtaining the physically correct onset of turbulence with shocks, including problems with stiff source terms: (I) Nonlinear dynamics is utilized to complement the traditional linearized stability theory (Yee & Sweby, Yee et al., Griffiths et al., Lafon & Yee, Yee, Wang et al., Kotov et al. 1990- 2015) in order to (i) Minimize numerically induced false transition to turbulence, (ii) Minimize numerical instability due to long time integration of turbulent flows, (iii) Minimize numerically induced standing wave solutions, and (iv) Minimize wrong propagation of speed of discontinuities due to the presence of stiff source terms. (II) Our recently developed physical preserving (structural preserving) high order methods with improved nonlinear stability & accuracy that are essential in minimizing spurious numerics are used.

HECC↗

NASA NTRS2023

Improved Space Weather Observations and Modeling for Aviation Radiation

In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

space weather↗