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Jupiter Global Reference Atmospheric Model (Jupiter-GRAM): User Guide

This Technical Memorandum (TM) presents the Jupiter Global Reference Atmospheric Model (Jupiter-GRAM) and the updated features of the GRAMs. Jupiter-GRAM is an engineering-oriented atmospheric model that estimates mean values of atmospheric properties for Jupiter. This TM summarizes the atmospheric data model in Jupiter-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Jupiter-GRAM input and output files and how to interpret Jupiter-GRAM results are also provided.

atmospheric models↗

New Releases and Upgrades of Giant Planet Global Reference Atmospheric Models (GRAMs)

• GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of atmospheric properties for numerous planetary destinations – Outputs atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path – Provide mean values and variability for any point in an atmosphere – Includes seasonal, geographic, and altitude variations – Used by engineering community because of their ability to create realistic atmospheric dispersions; can be integrated into high fidelity flight dynamic simulations of launch, entry, descent, and landing (EDL), aerobraking, and aerocapture • GRAMs are not forecast models • GRAMs are available for: Earth, Mars, Venus, Neptune, Titan, Jupiter, and Uranus • Available through the NASA Software Catalog https://software.nasa.gov/software/MFS-33888-1

atmospheric models↗

Global Reference Atmospheric Model (GRAM) Suite Overview and Current Status

• Engineering-oriented atmospheric models that estimate mean values and statistical variations of atmospheric properties for numerous planetary destinations • Currently available for Earth, Mars, Venus, Titan, Neptune, Uranus, and Jupiter • Outputs include atmospheric density, temperature, pressure, chemical composition, radiative fluxes (for Mars-GRAM), and wind components along a user-defined path – Includes seasonal, diurnal, geographic, and altitude variations • Widely used by the engineering community because of their ability to create realistic atmospheric dispersions • Can be integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture • Optional trajectory input file consisting of time, height, latitude, and longitude can be used to provide the GRAM trajectory path • Optional auxiliary profile consisting of height, latitude, longitude, temperature, pressure, density, eastward wind, and northward wind may be used to replace model data in the GRAMs • Not a forecast model

atmospheric models↗

Enhancing the Uranus PlanetGRAM with 2D Zonally-Averaged Atmospheric Variabilities

We developed an open-source Python package (tweModel.py1) that generates the 2D zonally-averaged atmospheric structure of Jupiter, Saturn, Uranus, and Neptune to be used as reference bases for NASA’s Planetary Global Reference Atmospheric Model (PlanetGRAM) Suite. The package outputs temperatures, pressures, densities, and zonal winds as functions of altitude and latitude given an input cloud-top zonal wind profile and a zonally averaged temperature map using a discretized form of the geostrophic thermal wind equation (TWE). We present 2D atmospheric structure outputs for Uranus in detail. Our results will be incorporated in the PlanetGRAM Suite to aid in the development of future in-situ missions in the outer solar system including the recently prioritized Uranus Flagship mission.

Uranus↗

GLOBAL REFERENCE ATMOSPHERIC MODELS FOR AEROASSIST APPLICATIONS

Aeroassist is a broad category of advanced transportation technology encompassing aerocapture, aerobraking, aeroentry, precision landing, hazard detection and avoidance, and aerogravity assist. The eight destinations in the Solar System with sufficient atmosphere to enable aeroassist technology are Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Saturn's moon Titan. Engineering-level atmospheric models for five of these targets - Earth, Mars, Titan, Neptune, and Venus - have been developed at NASA's Marshall Space Flight Center. These models are useful as tools in mission planning and systems analysis studies associated with aeroassist applications. The series of models is collectively named the Global Reference Atmospheric Model or GRAM series. An important capability of all the models in the GRAM series is their ability to simulate quasi-random perturbations for Monte Carlo analysis in developing guidance, navigation and control algorithms, for aerothermal design, and for other applications sensitive to atmospheric variability. Recent example applications are discussed.

Duvall, Aleta↗

Status of Venus Global Reference Atmospheric Model (Venus-GRAM) Upgrades

Venus-GRAM Overview - Engineering-oriented atmospheric model that estimates mean values and statistical variations of Venus atmospheric properties - Outputs include atmospheric density, temperature, pressure, chemical composition, and wind components along a user-defined path - Widely used by the engineering community because of its ability to create realistic atmospheric dispersions - Can be integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture - Has been used in multiple studies and proposals including NASA Engineering and Safety Center (NESC) Autonomous Aerobraking Study and various Discovery proposals - Optional trajectory input file consisting of time, height, latitude, and longitude can be used to provide the Venus-GRAM trajectory path - Optional auxiliary profile consisting of height, latitude, longitude, temperature, pressure, density, eastward wind, and northward wind may be used to replace model data in Venus-GRAM - Not a forecast model - GRAMs are also available for Earth, Mars, Titan, Neptune, Uranus, and Jupiter - Available through the NASA Software Catalog https://software.nasa.gov/software/MFS-33888-1

atmospheric models↗

GRAM Series of Atmospheric Models for Aeroentry and Aeroassist

The eight destinations in the Solar System with sufficient atmosphere for either aeroentry or aeroassist, including aerocapture, are: Venus, Earth, Mars, Jupiter, Saturn; Uranus. and Neptune, and Saturn's moon Titan. Engineering-level atmospheric models for four of these (Earth, Mars, Titan, and Neptune) have been developed for use in NASA's systems analysis studies of aerocapture applications in potential future missions. Work has recently commenced on development of a similar atmospheric model for Venus. This series of MSFC-sponsored models is identified as the Global Reference Atmosphere Model (GRAM) series. An important capability of all of the models in the GRAM series is their ability to simulate quasi-random perturbations for Monte Carlo analyses in developing guidance, navigation and control algorithms, and for thermal systems design. Example applications for Earth aeroentry and Mars aerocapture systems analysis studies are presented and illustrated. Current and planned updates to the Earth and Mars atmospheric models, in support of NASA's new exploration vision, are also presented.

Duvall, Aleta↗

Outer Planet Global Reference Atmospheric Model (GRAM) Upgrades

Introduction: The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of planetary atmospheres. The GRAM upgrades are being developed by NASA Marshall Space Flight Center and NASA Langley Research Center. This presentation will provide details regarding the upgrades to the existing GRAMs, the development of new GRAMs, and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations. They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. They are extensively used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. Outer Planet GRAM Upgrade Status: Code Modernization. The outer planet GRAMs have been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the rearchitected legacy outer planet GRAMs (Neptune and Titan-GRAM) are straight conversions from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established a contract with Hampton University to develop empirical global models for Jupiter, Saturn, Uranus, Neptune, and Titan. Upgraded Outer Planet GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and added the rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manual are released with all GRAMs. New Outer Planet GRAM Releases. New GRAMs have been developed for Uranus and Jupiter. Uranus-GRAM is based on the NASA Ames Research Center (ARC) Uranus Atmospheric Model [1,2] and was released in GRAM Suite Version 1.2 in July 2021. Jupiter-GRAM is based on Galileo probe Atmospheric Structure Instrument (ASI) data from Seiff et al. [3] Saturn-GRAM is also under development. Both Jupiter and Saturn-GRAM will be released in future versions of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Titan-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Allen Jr., G.A. et al. (2014) 11th International Planetary Probe Workshop, Abstract #8023. [2] Allen Jr., G.A. et al. (2014) Workshop on the Study of the Ice Giant Planets, Abstract #2001. [3] Seiff, A. et al. (1998) JGR, 103, 22,857 -22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.

atmospheric models↗

Global Reference Atmospheric Model (GRAM) Advancements and Additions

Introduction:The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of the atmosphere. GRAM development and maintenance has been led by NASA Marshall Space Flight Center (MSFC). The NASA Science Mission Directorate (SMD) has provided funding support to upgrade the GRAMs since Fiscal Year 2018. NASA Langley Research Center has been working with MSFC on the upgrades.This presentation will provide details regarding the upgrades that have been made to the existing GRAMs, the development of new GRAMs, as well as the ongoing objectives, tasks, and milestones re-lated to the GRAM upgrades funded by NASA SMD. GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations.They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path.Theyhave been widely used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. MSFC has been developing and updating GRAMs since 1974; GRAMs are currently available for Earth, Mars, Venus, Neptune, and Titan. GRAM Upgrade Status: Code Moderization.The planetary GRAMs are being rearchitected from Fortran to a common object-ori-entedC++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the existing planetary GRAMs (Mars, Venus, Neptune, and Titan-GRAM) in the GRAM Suite are straight conversions from the latest Fortran version. Model Upgrades.The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemerishas been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. The GRAM team has received updated Mars General Circulation Model (MGCM) datasets from NASA Ames Research Center. Mars Global Ionosphere-Thermosphere Model (M-GITM) data is being obtained toreplace the Mars Thermospheric General Circulation Model (MTGCM) data in legacy Mars-GRAM. M-GITM and updated MGCM data will be incorporated into a future GRAM Suite release.Twoprojects that will improve the atmospheric model data in the GRAMs have been funded by the GRAM team since Fiscal Year 2020. Sanjay Limaye and Patrick Fry at the University of Wisconsin are reanalyzing the Venus Express radio occultation observations and analyzing the Akatsukiradio occultation observations.This will lead tothe calculation of number density, temperature, and pressure profiles for the 40-90 km altitude range. Kunio Sayanagi, Justin Garland, and Ryan McCabeat Hampton University are developing empirical global models for Venus,Jupiter, Saturn, Uranus, Neptune, and Titan that incorporates the latest data available for each of these planetary destinations. Upgraded GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and addedthe rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manualarereleased with all GRAMs. The rearchitected Mars and Venus-GRAMs will be released in upcomingversions of the GRAM Suite. New GRAM Releases. New GRAMs have been de-veloped for Uranus and Jupiter. Uranus-GRAM is based on an individual profile generated by Gary Allen (ARC) from Voyager 2 occultation data and will be released in GRAM Suite Version 1.2. Jupiter-GRAM is based on individual profile produced from Al Seiff’s Ju-piter model[1].Jupiter-GRAM will be released in GRAM Suite Version 1.3. Saturn-GRAM is currently under development and will be released in a future version of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. NASA SMD funding has been essential to addressing current limitations and accomplishing GRAM developmental goals. Continua-tion ofSMD fundingwill ensure the development, up-grades, and maintenance of the GRAMs. References: [1] Seiff, A., et al. (1998) JGR, 103, 22,857-22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD

atmospheric models↗

Understanding of Jupiter's Atmosphere After the Galileo Probe Entry

Instruments on the Galileo probe measured composition, cloud properties, thermal structure. winds, radiative energy balance, and electrical properties of the Jovian atmosphere. As expected the probe results confirm some expectations about Jupiter's atmosphere, refute others, and raise new questions which still remain unanswered. This talk will concentrate on those aspects of the probe observations which either raised new questions or remain unresolved. The Galileo probe observations of composition and clouds provided some of the biggest surprises of the mission. Helium abundance measured by the probe differed significantly from the remote sensing derivations from Voyager. discrepancy between the Voyager helium abundance determinations for Jupiter and the Galileo probe value have now led to a considerably increased helium determination for Saturn. Global abundance of N in the form of ammonia was observed to be supersolar by approximately the same factor as carbon, in contrast to expectations that C/N would be significantly larger than solar. This has implications for the formation and evolution of Jupiter. The cloud structure was not what was generally anticipated, even though most previous remote sensing results below the uppermost cloud referred to 5 micron hot spots, local regions with reduced cloud opacity. The Galileo probe descended in one of these hot spots. Only a tenuous, presumed ammonium hydrosulfide, cloud was detected, and no significant water cloud or super-solar water abundance was measured. The mixing ratios as a function of depth for the condensibles ammonia, hydrogen sulfide, and water, exhibited no apparent correlation with either condensation levels or with each other, an observation that is still a puzzle, although there are now dynamical models of hot spots which show promise in being able to explain such behavior. Probe tracked zonal winds show that wind magnitude increases with depth to pressures of about 4 bars, with the winds extending to at least as deep as the probe made measurements, 22 bars. Models of hot spot dynamics raise the possibility that the variation with depth of the probe measured zonal winds between 0.4 and 4 bars reflect the dynamics of the hot spot rather than the global wind pattern. Galileo upper atmosphere measurements established that there is a sharp temperature rise with altitude between about 350 and 800 km above the 1 bar pressure level, with the upper atmosphere reaching, temperatures near 900 K. The energy sources for this upper atmosphere heating are not clearly established, but various mechanisms have been proposed. These and other aspects of the Galileo probe data will be discussed.

Young, Richard E.↗

Understanding of Jupiter's Atmosphere after the Galileo Probe Entry

Instruments on the Galileo probe measured composition, cloud properties, thermal structure, winds, radiative energy balance, and electrical properties of the Jovian atmosphere. As expected the probe results confirm some expectations about Jupiter's atmosphere, refute others, and raise new questions which still remain unanswered. This talk will concentrate on those aspects of the probe observations which either raised new questions or remain unresolved. The Galileo probe observations of composition and clouds provided some of the biggest surprises of the mission. Helium abundance measured by the probe differed significantly from the remote sensing derivations from Voyager. Discrepancy between the Voyager helium abundance determinations for Jupiter and the Galileo probe value have now led to a considerably increased helium determination for Saturn. Global abundance of N in the form of ammonia was observed to be super-solar by approximately the same factor as carbon, in contrast to expectations that C/N would be significantly larger than solar. This has implications for the formation and evolution of Jupiter. The cloud structure was not what was generally anticipated, even though most previous remote sensing results below the uppermost cloud referred to 5 micron hot spots, local regions with reduced cloud opacity. The Galileo probe descended in one of these hot spots. Only a tenuous, presumed ammomium hydrosulfide, cloud was detected, and no significant water cloud or super-solar water abundance was measured. The mixing ratios as a function of depth for the condensibles ammonia, hydrogen sulfide, and water, exhibited no apparent correlation with either condensation levels or with each other, an observation that is still a puzzle, although there are now dynamical models of hot spots which show promise in being able to explain such behavior. Probe tracked zonal winds show that wind magnitude increases with depth to pressures of about 4 bars, with the winds extending to at least as deep as the probe made measurements, 22 bars. Models of hot spot dynamics raise the possibility that the variation with depth of the probe measured zonal winds between 0.4 and 4 bars reflect the dynamics of the hot spot rather than the global wind pattern. Galileo upper atmosphere measurements established that there is a sharp temperature rise with altitude between about 350 and 800 km above the 1 bar pressure level, with the upper atmosphere reaching temperatures near 900 K. The energy sources for this upper atmosphere heating are not clearly established, but various mechanisms have been proposed. These and other aspects of the Galileo probe data will be discussed.

Fonda, Mark↗

Trajectory Engineering with Modular Patched Conics for Entry Systems and TPS (TEMPEST)

Brief Presenter Biography (35 word limit): Bohdan Wesely is an Aerospace Engineer in the Entry Systems and Technology Division at Ames. He has worked on a variety of projects for NASA including integrated TPS (thermal protection system) flight hardware deliveries and testing services for commercial partners. Introduction: TEMPEST is a new trajectory analysis framework that is designed to fill the gap between dedicated flight mechanics tools and aerothermal and TPS sizing tools. The project started as an SJSU master’s thesis and has since evolved into a general conceptual design tool capable of studying a wide variety of entry problems. Development is ongoing in the Entry Systems and Technology Division at NASA ARC. Why TEMPEST: Space missions involving entry into a planetary atmosphere involve a series of unique requirements across multiple disciplines. Whether it is traditional entry descent and landing (EDL), or aerocapture, the vehicle must navigate to its target landing location or orbit state, and the TPS must protect the payload during entry. The design process typically involves iterative handoffs between various flight mechanics, flow solver, and material response level tools. During the early conceptual phase, a wide variety of feasible trajectories are simulated in a Monte Carlo scenario which broadly satisfy the mission or landing requirements. Next, computational fluid dynamics (CFD), direct simulation Monte Carlo (DSMC), and other flow solver analyses are performed at various key trajectory points to generate an aero-database, heating and TPS design requirements also emerge at this stage. At this point, with updated aerodynamics from the various flow solvers, trajectories can be re-run, this in turn can change the required freestream conditions for the CFD tools, and as a project progresses, these analyses converge, and uncertainty is reduced. However, there is always a “hand-off” occurring between two inherently coupled phenomena. Analysis Description: One of the goals with TEMPEST is to use a variety of first principles estimation methods coupled with an atmosphere model to predict vehicle aerothermodynamics across the entire flight regime while propagating a 3 or 6 degree of freedom (DoF) trajectory. Aerodynamics methods include modified Newtonian, Maxwell and Cercignani- Lampis-Lord (CLL) for continuum, transitional, and free molecular flow regimes. Aerothermodynamics include boundary layer and reference enthalpy methods, and Mutation++ for non-equilibrium chemistry modeling. TEMPEST is also capable of stitching multiple trajectory segments together to study mission scenarios like multi-pass aerocapture and aero-gravity assists. Most of the program is implemented in MATLAB using modern system objects, it relies on several C++ shared libraries for supporting tools like Gmsh, the Global Reference Atmospheric Model (GRAM), and Mutation++. The various first principles aerothermal estimation methods are discretized across either a structured axisymmetric panel mesh or an unstructured tri-mesh generated from an open-source tool such as Gmsh, this allows solutions on the same mesh to be compared across tools such as CB-Aero. CFD Coupling. A physics-aware, gaussian process CFD anchoring scheme is proposed to adjust the various first principles methods as a CFD database is populated. One goal for this anchoring module is to inform the project where CFD should be run. Full knowledge of the entire trajectory, atmosphere, and aerothermodynamics allows for easier identification of high sensitivity areas and uncertainty quantification. While the first principles effects are well known and proven accurate in existing tools such as CB- Aero and Cart3D, a physics aware CFD anchoring scheme increases tool credibility across a project lifecycle. Material Response Modeling. Correct TPS sizing is critical for optimizing mass for science payloads and ensuring mission success. The process typically involves a thermal analysis along the trajectory with surface heating environments as a boundary condition. Several design constraints are maximum bondline temperature and maximum recession with various margining techniques. The material response tool FIAT, developed out of NASA Ames, is currently being integrated into the TEMPEST environment. TPS recession, shape change, mass loss, and mass property alteration are all factors that can perturb an entry trajectory. For missions like Mars 2020, recession was minimal and was safely handled separately as a post process. For missions such as Jupiter Galileo with a high TPS mass fraction or asteroid entries, recession plays a major role. The proposed fully coupled scheme is to use an epoch-based approach where the trajectory integration is halted after a recession threshold, the energy balance and FIAT are solved at each panel, the mesh, aerodynamics, and mass properties are updated, and the trajectory continues. Several computational tradeoffs have been made during the development of TEMPEST to limit the cost of a single trajectory and preserve its utility as a conceptual, rapid iteration tool. Conclusion: Development of TEMPEST is ongoing and the project is still in its infancy. This talk aims to showcase its unique capabilities to support future NASA entry systems missions.

Bohdan O Wesely↗

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗