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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 289 records · Page 16

Global Reference Atmosphere Model (GRAM)

4D model provides atmospheric parameter values either automatically at positions along linear path or along any set of connected positions specified by user. Based on actual data, GRAM provides thermal wind shear for monthly mean winds, percent deviation from standard atmosphere, mean vertical wind, and perturbation data for each position.

Johnson, D. L.↗

Global Reference Atmospheric Model - 1990

GRAM-90, improvement on original model, provides values of atmospheric parameters, either automatically at positions along linear path or along any set of connected positions specified by user. Parameters produced include monthly total and mean values of pressure, density, temperature, and wind, as well as perturbation values and magnitudes. Also provides thermal wind shear for monthly mean winds, percent deviation from standard atmosphere, mean vertical wind, and perturbation data for each position. Perturbation data consist of values of quasi-biennial and random perturbations. Written in FORTRAN 77.

Justus, C. G.↗

Earth Global Reference Atmospheric Model (GRAM99): Short Course

Earth-GRAM is a FORTRAN software package that can run on a variety of platforms including PC's. For any time and location in the Earth's atmosphere, Earth-GRAM provides values of atmospheric quantities such as temperature, pressure, density, winds, constituents, etc.. Dispersions (perturbations) of these parameters are also provided and have realistic correlations, means, and variances - useful for Monte Carlo analysis. Earth-GRAM is driven by observations including a tropospheric database available from the National Climatic Data Center. Although Earth-GRAM can be run in a "stand-alone" mode, many users incorporate it into their trajectory codes. The source code is distributed free-of-charge to eligible recipients.

Leslie, Fred W.↗

Empirical Storm-Time Correction to the International Reference Ionosphere Model E-Region Electron and Ion Density Parameterizations Using Observations from TIMED/SABER

The response of the ionospheric E-region to solar-geomagnetic storms can be characterized using observations of infrared 4.3 micrometers emission. In particular, we utilize nighttime TIMED/SABER measurements of broadband 4.3 micrometers limb emission and derive a new data product, the NO+(v) volume emission rate, which is our primary observation-based quantity for developing an empirical storm-time correction the IRI E-region electron density. In this paper we describe our E-region proxy and outline our strategy for developing the empirical storm model. In our initial studies, we analyzed a six day storm period during the Halloween 2003 event. The results of this analysis are promising and suggest that the ap-index is a viable candidate to use as a magnetic driver for our model.

Mertens, Christoper J.↗

Earth Global Reference Atmospheric Model 2007 (Earth-GRAM07)

GRAM is a Fortran software package that can run on a variety of platforms including PC's. GRAM provides values of atmospheric quantities such as temperature, pressure, density, winds, constituents, etc. GRAM99 covers all global locations, all months, and heights from the surface to approx. 1000 km). Dispersions (perturbations) of these parameters are also provided and are spatially and temporally correlated. GRAM can be run in a stand-alone mode or called as a subroutine from a trajectory program. GRAM07 is diagnostic, not prognostic (i.e., it describes the atmosphere, but it does not forecast). The source code is distributed free-of-charge to eligible recipients.

Leslie, Fred W.↗

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

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. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: a) TES Mapping Years 1 and 2, with Mars-GRAM data coming from MGCM model results driven by observed TES dust optical depth; and b) 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. From the surface to 80 km altitude, Mars-GRAM is based on 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. Mars-GRAM 2005 has been validated2 against Radio Science data, and both nadir and limb data from the Thermal Emission Spectrometer (TES)

Justh, Hilary L.↗

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

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

Justh, H. L.↗

Effective Power-Law Dependence of Lyapunov Exponents on the Central Mass in Galaxies

Using both numerical and analytical approaches, we demonstrate the existence of an effective power-law relation L alpha m(sup p) between themean Lyapunov exponent L of stellar orbits chaotically scattered by a supermassive black hole (BH) in the centre of a galaxy and the mass parameter m, i.e. ratio of the mass of the BH over the mass of the galaxy. The exponent p is found numerically to obtain values in the range p approximately equals 0.3-0.5. We propose a theoretical interpretation of these exponents, based on estimates of local 'stretching numbers', i.e. local Lyapunov exponents at successive transits of the orbits through the BH's sphere of influence. We thus predict p = 2/3 − q with q approximately equaling 0.1-0.2. Our basic model refers to elliptical galaxy models with a central core. However, we find numerically that an effective power-law scaling of L with m holds also in models with central cusp, beyond a mass scale up to which chaos is dominated by the influence of the cusp itself. We finally show numerically that an analogous law exists also in disc galaxies with rotating bars. In the latter case, chaotic scattering by the BH affects mainly populations of thick tube-like orbits surrounding some low-order branches of the x(sub 1) family of periodic orbits, as well as its bifurcations at low-order resonances, mainly the inner Lindblad resonance and the 4/1 resonance. Implications of the correlations between L and m to determining the rate of secular evolution of galaxies are discussed.

chaos aEuro" galaxies:↗

Earth Global Reference Atmospheric Model (Earth-GRAM) GRAM Virtual Meeting

What is Earth-GRAM? Provide monthly mean and standard deviation for any point in atmosphere; Monthly, Geographic, and Altitude Variation. Earth-GRAM is a C++ software package; Currently distributed as Earth-GRAM 2016. Atmospheric variables included: pressure, density, temperature, horizontal and vertical winds, speed of sound, and atmospheric constituents. Used by engineering community because of ability to create dispersions inatmosphere at a rapid runtime; Often embedded in trajectory simulation software. Not a forecast model. Does not readily capture localized atmospheric effects.

White, Patrick↗