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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Development and analysis of prognostic equations for mesoscale kinetic energy and mesoscale (subgrid scale) fluxes for large-scale atmospheric models

Generated by landscape discontinuities (e.g., sea breezes) mesoscale circulation processes are not represented in large-scale atmospheric models (e.g., general circulation models), which have an inappropiate grid-scale resolution. With the assumption that atmospheric variables can be separated into large scale, mesoscale, and turbulent scale, a set of prognostic equations applicable in large-scale atmospheric models for momentum, temperature, moisture, and any other gaseous or aerosol material, which includes both mesoscale and turbulent fluxes is developed. Prognostic equations are also developed for these mesoscale fluxes, which indicate a closure problem and, therefore, require a parameterization. For this purpose, the mean mesoscale kinetic energy (MKE) per unit of mass is used, defined as E-tilde = 0.5 (the mean value of u'(sub i exp 2), where u'(sub i) represents the three Cartesian components of a mesoscale circulation (the angle bracket symbol is the grid-scale, horizontal averaging operator in the large-scale model, and a tilde indicates a corresponding large-scale mean value). A prognostic equation is developed for E-tilde, and an analysis of the different terms of this equation indicates that the mesoscale vertical heat flux, the mesoscale pressure correlation, and the interaction between turbulence and mesoscale perturbations are the major terms that affect the time tendency of E-tilde. A-state-of-the-art mesoscale atmospheric model is used to investigate the relationship between MKE, landscape discontinuities (as characterized by the spatial distribution of heat fluxes at the earth's surface), and mesoscale sensible and latent heat fluxes in the atmosphere. MKE is compared with turbulence kinetic energy to illustrate the importance of mesoscale processes as compared to turbulent processes. This analysis emphasizes the potential use of MKE to bridge between landscape discontinuities and mesoscale fluxes and, therefore, to parameterize mesoscale fluxes generated by such subgrid-scale landscape discontinuities in large-scale atmospheric models.

Avissar, Roni↗
NASA NTRS2021

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

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

Atmospheric Models↗
NASA NTRS2021

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

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

atmospheric models↗
NASA NTRS2021

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

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

atmospheric models↗
DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Intraseasonal and interannual oscillations in coupled ocean-atmosphere models

An investigation is presented of coupled ocean-atmosphere models' behavior in an environment where atmospheric wave speeds are substantially reduced from dry atmospheric values by such processes as condensation-moisture convergence. Modes are calculated for zonally periodic, unbounded ocean-atmosphere systems, emphasizing the importance of an inclusion of prognostic atmosphere equations in simple coupled ocean-atmosphere models with a view to simulations of intraseasonal variability and its possible interaction with interannual variability. The dynamics of low and high frequency modes are compared; both classes are sensitive to the degree to which surface wind anomalies are able to affect the evaporation rate.

Hirst, Anthony C.↗
NASA NTRS2002

Global Reference Atmospheric Model and Trace Constituents

Global Reference Atmospheric Model (GRAM-99) is an engineering-level model of the Earth's atmosphere. It provides both mean values and perturbations for density, temperature, pressure, and winds, as well as monthly- and geographically-varying trace constituent concentrations. From 0-27 km, thermodynamics and winds are based on National Oceanic and Atmospheric Administration Global Upper Air Climatic Atlas (GUACA) climatology. Above 120 km, GRAM is based on the NASA Marshall Engineering Thermosphere (MET) model. In the intervening altitude region, GRAM is based on Middle Atmosphere Program (MAP) climatology that also forms the basis of the 1986 COSPAR Intemationa1 Reference Atmosphere (CIRA). MAP data in GRAM are augmented by a specially-derived longitude variation climatology. Atmospheric composition is represented in GRAM by concentrations of both major and minor species. Above 120 km, MET provides concentration values for N2, O2, Ar, O, He, and H. Below 120 km, species represented also include H2O, O3, N2O, CO, CH, and CO2. Water vapor in GRAM is based on a combination of GUACA, Air Force Geophysics Laboratory (AFGL), and NASA Langley Research Center climatologies. Other constituents below 120 km are based on a combination of AFGL and h4AP/CIRA climatologies. This report presents results of comparisons between GRAM Constituent concentrations and those provided by the Naval Research Laboratory (NRL) climatology of Summers (NRL,/MR/7641-93-7416, 1993). GRAM and NRL concentrations were compared for seven species (CH4, CO, CO2, H2O, N2O, O2, and O3) for months January, April, July, and October, over height range 0-115 km, and latitudes -90deg to + 90deg at 10deg increments. Average GRAM-NRL correlations range from 0.878 (for CO) to 0.975 (for O3), with an average over all seven species of 0.936 (standard deviation 0.049).

Justus, C.↗
NASA NTRS2008

Evaluating Mars Science Laboratory Landing Sites with the Mars Global Reference Atmospheric Model (Mars-GRAM 2005)

The Mars Global Reference Atmospheric Model (Mars-GRAM) is an engineering-level atmospheric model widely used for diverse mission applications. Mars-GRAM s perturbation modeling capability is commonly used, in a Monte-Carlo mode, to perform high fidelity engineering end-to-end simulations for entry, descent, and landing (EDL) [1]. From the surface to 80 km altitude, Mars-GRAM is based on the NASA Ames Mars General Circulation Model (MGCM). Mars-GRAM and MGCM use surface topography from Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA), with altitudes referenced to the MOLA areoid, or constant potential surface. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: (1) Thermal Emission Spectrometer (TES) mapping years 1 and 2, with Mars-GRAM data coming from NASA Ames Mars General Circulation Model (MGCM) results driven by observed TES dust optical depth or (2) TES mapping year 0, with user-controlled dust optical depth and Mars-GRAM data interpolated from MGCM model results driven by selected values of globally-uniform dust optical depth. Mars-GRAM 2005 has been validated [2] against Radio Science data, and both nadir and limb data from TES [3]. There are several new features included in Mars-GRAM 2005. The first is the option to use input data sets from MGCM model runs that were designed to closely simulate conditions observed during the first two years of TES observations at Mars. The TES Year 1 option includes values from April 1999 through January 2001. The TES Year 2 option includes values from February 2001 through December 2002. The second new feature is the option to read and use any auxiliary profile of temperature and density versus altitude. In exercising the auxiliary profile Mars-GRAM option, values from the auxiliary profile replace data from the original MGCM databases. Some examples of auxiliary profiles include data from TES nadir or limb observations and Mars mesoscale model output at a particular location and time. The final new feature is the addition of two Mars-GRAM parameters that allow standard deviations of Mars-GRAM perturbations to be adjusted. The parameter rpscale can be used to scale density perturbations up or down while rwscale can be used to scale wind perturbations.

Justh, H. L.↗
NASA NTRS2021

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

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

Uranus Global Reference Atmospheric Model↗
NASA NTRS2002

Atmospheric Models for Engineering Applications

This paper will review the historical development of reference and standard atmosphere models and their applications. The evolution of the U.S. Standard Atmosphere will be addressed, along with the Range Reference Atmospheres and, in particular, the NASA Global Reference Atmospheric Model (GRAM). The extensive scope and content of the GRAM will be addressed since it represents the most extensive and complete 'Reference' atmosphere model in use today. Its origin was for engineering applications and that remains today as its principal use.

Johnson, Dale L.↗
NASA NTRS1988

Vacillations in a coupled ocean-atmosphere model

Results are presented from a 35-year integration of a coupled ocean-atmosphere model. Both ocean and atmosphere are two-level, nonlinear primitive equation models. The global atmospheric model is forced by a steady, zonally symmetric Newtonian heating. The ocean model is solved in a rectangular tropical basin. Heat fluxes between ocean and atmosphere are linear in air-sea temperature differences, and the interfacial stress is proportional to lower-level atmospheric winds. The coupled models produce ENSO-like variability on time scales of 3 to 5 years. Since there is no external time-dependent forcing, these are self-sustained vacillations of the nonlinear system. It is argued that the energetics of the vacillations is that of unstable coupled modes and that the time scale is crucially dependent on the effects of ocean waves propagating in a closed basin.

Schopf, Paul S.↗
NASA NTRSDate not supplied

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↗
NASA NTRS1977

Atmospheric model development in support of SEASAT. Volume 4: Forecast model sensitivity study

Atmospheric analysis and prediction models of varying (grid) resolution were developed. The models were tested using real observational data for the purpose of assessing the impact of grid resolution on short range numerical weather prediction. The work statement was amended to include the performance of sensitivity tests using a coarse mesh (63 x 63 x 5 level) prediction model in order to identify and order factors which might mask or impair the utility of SEASAT data on short range weather prediction. Such factors included: initial conditions; topography; surface friction; latent heating; diffusion of momentum and temperature; and computational devices such as tendency truncators, pressure smoothers, and temporal filters.

Lewit, H. L.↗
NASA NTRS2008

Utilizing Mars Global Reference Atmospheric Model (Mars-GRAM 2005) to Evaluate Entry Probe Mission Sites

The Mars Global Reference Atmospheric Model (Mars-GRAM 2005) is an engineering-level atmospheric model widely used for diverse mission applications. An overview is presented of Mars-GRAM 2005 and its new features. The "auxiliary profile" option is one new feature of Mars-GRAM 2005. This option uses an input file of temperature and density versus altitude to replace the mean atmospheric values from Mars-GRAM's conventional (General Circulation Model) climatology. Any source of data or alternate model output can be used to generate an auxiliary profile. Auxiliary profiles for this study were produced from mesoscale model output (Southwest Research Institute's Mars Regional Atmospheric Modeling System (MRAMS) model and Oregon State University's Mars mesoscale model (MMM5) model) and a global Thermal Emission Spectrometer (TES) database. The global TES database has been specifically generated for purposes of making Mars-GRAM auxiliary profiles. This data base contains averages and standard deviations of temperature, density, and thermal wind components, averaged over 5-by-5 degree latitude-longitude bins and 15 degree Ls bins, for each of three Mars years of TES nadir data. The Mars Science Laboratory (MSL) sites are used as a sample of how Mars-GRAM' could be a valuable tool for planning of future Mars entry probe missions. Results are presented using auxiliary profiles produced from the mesoscale model output and TES observed data for candidate MSL landing sites. Input parameters rpscale (for density perturbations) and rwscale (for wind perturbations) can be used to "recalibrate" Mars-GRAM perturbation magnitudes to better replicate observed or mesoscale model variability.

Justh, Hilary L.↗
NASA NTRS1975

Revised Perturbation Statistics for the Global Scale Atmospheric Model

Magnitudes and scales of atmospheric perturbations about the monthly mean for the thermodynamic variables and wind components are presented by month at various latitudes. These perturbation statistics are a revision of the random perturbation data required for the global scale atmospheric model program and are from meteorological rocket network statistical summaries in the 22 to 65 km height range and NASA grenade and pitot tube data summaries in the region up to 90 km. The observed perturbations in the thermodynamic variables were adjusted to make them consistent with constraints required by the perfect gas law and the hydrostatic equation. Vertical scales were evaluated by Buell's depth of pressure system equation and from vertical structure function analysis. Tables of magnitudes and vertical scales are presented for each month at latitude 10, 30, 50, 70, and 90 degrees.

Justus, C. G.↗