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At least 19 records

A Global Atmospheric Model of Meteoric Iron

The first global model of meteoric iron in the atmosphere (WACCM-Fe) has been developed by combining three components: the Whole Atmosphere Community Climate Model (WACCM), a description of the neutral and ion-molecule chemistry of iron in the mesosphere and lower thermosphere (MLT), and a treatment of the injection of meteoric constituents into the atmosphere. The iron chemistry treats seven neutral and four ionized iron containing species with 30 neutral and ion-molecule reactions. The meteoric input function (MIF), which describes the injection of Fe as a function of height, latitude, and day, is precalculated from an astronomical model coupled to a chemical meteoric ablation model (CABMOD). This newly developed WACCM-Fe model has been evaluated against a number of available ground-based lidar observations and performs well in simulating the mesospheric atomic Fe layer. The model reproduces the strong positive correlation of temperature and Fe density around the Fe layer peak and the large anticorrelation around 100 km. The diurnal tide has a significant effect in the middle of the layer, and the model also captures well the observed seasonal variations. However, the model overestimates the peak Fe+ concentration compared with the limited rocket-borne mass spectrometer data available, although good agreement on the ion layer underside can be obtained by adjusting the rate coefficients for dissociative recombination of Fe-molecular ions with electrons. Sensitivity experiments with the same chemistry in a 1-D model are used to highlight significant remaining uncertainties in reaction rate coefficients, and to explore the dependence of the total Fe abundance on the MIF and rate of vertical transport.

meteoric inputfunction↗

Improvements in the perturbation simulations of the global reference atmospheric model

The Global Reference Atmospheric Model (GRAM) program includes the capability for simulating pseudo-random perturbations in density, temperature, pressure, or wind components along a simulated reentry trajectory or other path through the atmosphere. Some concerns were expressed by GRAM users, however, that the mean-square perturbation gradients may be too large for small values of the vertical separation Delta z. The present GRAM perturbation simulations, based on a one-step autoregressive model, yield a power spectrum versus wavenumber k which is proportional to k sup -2 at high wavenumbers. This feature also produces mean-square perturbation differences which are directly proportional to Delta z, and mean-square perturbation gradients which are inversely proportional to Delta z. Thus, root-mean-square gradients, (Delta f/Delta z) sub rms, increase with decreasing Delta a as Delta z sup -1/2. A simple modification to GRAM is suggested which overcomes this problem, i.e., which produce root-mean-square gradient that remain bound as Delta z approaches zero. Possible applications of more sophisticated simulation approaches, e.g., second order autoregressive models, or fractal model techniques, were also explored briefly but found to yield improvements which appear too small to justify their considerable added complexity for use in the GRAM programs.

Justus, C. G.↗

Venus Global Reference Atmospheric Model

Venus Global Reference Atmospheric Model (Venus-GRAM) is an engineering-level atmospheric model developed by MSFC that is widely used for diverse mission applications including: Systems design; Performance analysis; Operations planning for aerobraking, Entry, Descent and Landing, and aerocapture; Is not a forecast model; Outputs include density, temperature, pressure, wind components, and chemical composition; Provides dispersions of thermodynamic parameters, winds, and density; Optional trajectory and auxiliary profile input files Has been used in multiple studies and proposals including NASA Engineering and Safety Center (NESC) Autonomous Aerobraking and various Discovery proposals; Released in 2005; Available at: https://software.nasa.gov/software/MFS-32314-1.

Justh, Hilary L.↗

Progress in Modeling Global Atmospheric CO2 Fluxes and Transport: Results from Simulations with Diurnal Fluxes

Progress in better determining CO2 sources and sinks will almost certainly rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. Use of advanced data requires improved modeling and analysis capability. Under NASA Carbon Cycle Science support we seek to develop and integrate improved formulations for 1) atmospheric transport, 2) terrestrial uptake and release, 3) biomass and 4) fossil fuel burning, and 5) observational data analysis including inverse calculations. The transport modeling is based on meteorological data assimilation analysis from the Goddard Modeling and Assimilation Office. Use of assimilated met data enables model comparison to CO2 and other observations across a wide range of scales of variability. In this presentation we focus on the short end of the temporal variability spectrum: hourly to synoptic to seasonal. Using CO2 fluxes at varying temporal resolution from the SIB 2 and CASA biosphere models, we examine the model's ability to simulate CO2 variability in comparison to observations at different times, locations, and altitudes. We find that the model can resolve much of the variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The influence of key process representations is inferred. The high degree of fidelity in these simulations leads us to anticipate incorporation of realtime, highly resolved observations into a multiscale carbon cycle analysis system that will begin to bridge the gap between top-down and bottom-up flux estimation, which is a primary focus of NACP.

Collatz, G. James↗

A global atmospheric model for aerospace applications

An empirical 4-D atmospheric model is described, whose ultimate goal is to give pressure, temperature, and density variables and their structure as a function of latitude, longitude, altitude, and the time domain (seasonal and perhaps time of day) over the altitude range from sea level to 185 km. The computer program combines the 4-D model of Siegler and Fowler (1972) for the range 0-25 km altitude, the modified Groves (1971) model for the range 30-90 km, and the Jacchia (1970) model for the range 115-185 km. Between 90 and 115 km, a smooth transition between the Groves values and Jacchia values was obtained by a fairing technique, while between 25 and 30 km an interpolation scheme was used between the 4-D results and the Groves values. The program is designed to give atmospheric parameter values along a simulated trajectory and a profile, such as a vertical profile at a single location. A set of trajectory positions for any desired trajectory, ascent or re-entry, can be input to the program for evaluation of atmospheric parameters for design purposes.

Justus, C. G.↗

Monthly mean simulation experiments with a course-mesh global atmospheric model

Substitution of observed monthly mean sea-surface temperatures (SSTs) as lower boundary conditions, in place of climatological SSTs, failed to improve the model simulations. While the impact of SST anomalies on the model output is greater at sea level than at upper levels the impact on the monthly mean simulations is not beneficial at any level. Shifts of one and two days in initialization time produced small, but non-trivial, changes in the model-generated monthly mean synoptic fields. No improvements in the mean simulations resulted from the use of either time-averaged initial data or re-initialization with time-averaged early model output. The noise level of the model, as determined from a multiple initial state perturbation experiment, was found to be generally low, but with a noisier response to initial state errors in high latitudes than the tropics.

Spar, J.↗

Global Atmospheric Aerosol Modeling

Global aerosol models are used to study the distribution and properties of atmospheric aerosol particles as well as their effects on clouds, atmospheric chemistry, radiation, and climate. The present article provides an overview of the basic concepts of global atmospheric aerosol modeling and shows some examples from a global aerosol simulation. Particular emphasis is placed on the simulation of aerosol particles and their effects within global climate models.

Hendricks, Johannes↗

Planned Improvements to the Venus Global Reference Atmospheric Model

The Venus Global Reference Atmospheric Model (Venus-GRAM) is an engineering-level atmospheric model applicable for engineering design analyses, mission planning, and operational decision making. Missions to Venus have generated a wealth of atmospheric data, however, Venus-GRAM has not been updated since its development and release in 2005. GRAM upgrades and maintenance have depended on inconsistent and waning project-specific support. The NASA Science Mission Directorate (SMD) has agreed to provide funding support in Fiscal Year 2018 and 2019 to upgrade the GRAMs. This presentation will provide an overview of Venus-GRAM and the objectives, tasks, and milestones related to the GRAM upgrades.

Justh, H. L.↗

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.↗

A satellite-borne radar wind sensor (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve if worldwide information on winds aloft were available. Accurate prediction of weather is important to agriculture, shipping, air traffic, and many other fields. Global system models of climate are of great importance. Current global atmospheric models use pressure measurements and thermodynamic properties to calculate the effects of wind for use in Numerical Weather Prediction (NWP) models. Inputs to the NWP models are temperature, pressure and wind velocities at different heights. Clearly direct wind measurements could significantly improve the NWP model performance. The RAdar Wind Sounder (RAWS) program at the University of Kansas is a study of the feasibility and the trade-offs in the design of a space-based radar system to measure wind vectors. This can be done by measuring the Doppler shift of cloud and rain returns from three or more points and calculating the components of the wind vector. The RAWS study to date uses the candidate system selected after preliminary study of frequencies and sensitivities. Two frequencies chosen, 10 and 35 GHz, allow higher sensitivity for clouds and more penetration for rain. The past year was devoted to modeling the signal-to-noise ratio (SNR) achievable for the two frequencies. The determination of SNR versus cloud penetration depth used a cloud backscattering and attenuation model in the appropriate radar equation. Calculations assumed reasonable losses in reception and transmission, in addition to the atmospheric attenuation. We discovered that ice clouds provide a higher SNR than previously calculated, but some water clouds give lower SNRs than we calculated before. One of the primary issues in the SNR calculation was the choice of the drop size distribution. Although Xin used several distributions (e.g., log normal, Khrigian and Mazin), this year we used the Deirmendjian cloud model. SNR versus cloud penetration plots were generated to validate the candidate system. Rain, which appears in the cloud models at the lower altitudes, provides ample SNR, as do the higher clouds composed of ice particles. However, in some cloud situations we found the sensitivity for the clouds was marginal or inadequate. At 35 GHz, two of the cloud models characterized by 1 to 2 g/cu m of water content at altitudes extending from 150 to 1500 meters, produced a sufficient SNR. Other models, however, with water contents ranging from 0.5 to 4 g/cu m and altitudes up to 4000 meters, exhibit SNR of -3 to -23 dB, largely because of attenuation in the upper cloud layers. These results coupled with the lower SNR at 10 GHz, led to an investigation of alternate frequencies. The rain present beneath these clouds provides adequate SNR at 10 GHz, and in most cases, at GHz.

Moore, Richard K.↗

Extensive middle atmosphere (20-120 KM) modification in the Global Reference Atmospheric Model (GRAM-90)

The Global Reference Atmospheric Model (GRAM) is currently available in the 'GRAM-88' version (Justus, et al., 1986; 1988), which includes relatively minor upgrades and changes from the 'MOD-3' version (Justus, et al., 1980). Currently a project is underway to use large amounts of data, mostly collected under the Middle Atmosphere Program (MAP) to produce a major upgrade of the program planned for release as the GRAM-90 version. The new data and program revisions will particularly affect the 25-90 km height range. Sources of data and preliminary results are described here in the form of cross-sectional plots.

Justus, C. G.↗

The global reference atmospheric model, mod 2 (with two scale perturbation model)

The Global Reference Atmospheric Model was improved to produce more realistic simulations of vertical profiles of atmospheric parameters. A revised two scale random perturbation model using perturbation magnitudes which are adjusted to conform to constraints imposed by the perfect gas law and the hydrostatic condition is described. The two scale perturbation model produces appropriately correlated (horizontally and vertically) small scale and large scale perturbations. These stochastically simulated perturbations are representative of the magnitudes and wavelengths of perturbations produced by tides and planetary scale waves (large scale) and turbulence and gravity waves (small scale). Other new features of the model are: (1) a second order geostrophic wind relation for use at low latitudes which does not "blow up" at low latitudes as the ordinary geostrophic relation does; and (2) revised quasi-biennial amplitudes and phases and revised stationary perturbations, based on data through 1972.

Justus, C. G.↗

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↗

Mars Global Reference Atmospheric Model (Mars-GRAM) 2024: User Guide

This Technical Memorandum (TM) presents the Mars Global Reference Atmospheric Model (Mars-GRAM) 2024 and its updated features. 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 density↗

Clouds in ECMWF's 30 KM Resolution Global Atmospheric Forecast Model (TL639)

Global models of the general circulation of the atmosphere resolve a wide range of length scales, and in particular cloud structures extend from planetary scales to the smallest scales resolvable, now down to 30 km in state-of-the-art models. Even the highest resolution models do not resolve small-scale cloud phenomena seen, for example, in Landsat and other high-resolution satellite images of clouds. Unresolved small-scale disturbances often grow into larger ones through non-linear processes that transfer energy upscale. Understanding upscale cascades is of crucial importance in predicting current weather, and in parameterizing cloud-radiative processes that control long term climate. Several movie animations provide examples of the temporal and spatial variation of cloud fields produced in 4-day runs of the forecast model at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, England, at particular times and locations of simultaneous measurement field campaigns. model resolution is approximately 30 km horizontally (triangular truncation TL639) with 31 vertical levels from surface to stratosphere. Timestep of the model is about 10 minutes, but animation frames are 3 hours apart, at timesteps when the radiation is computed. The animations were prepared from an archive of several 4-day runs at the highest available model resolution, and archived at ECMWF. Cloud, wind and temperature fields in an approximately 1000 km X 1000 km box were retrieved from the archive, then approximately 60 Mb Vis5d files were prepared with the help of Graeme Kelly of ECMWF, and were compressed into MPEG files each less than 3 Mb. We discuss the interaction of clouds and radiation in the model, and compare the variability of cloud liquid as a function of scale to that seen in cloud observations made in intensive field campaigns. Comparison of high-resolution global runs to cloud-resolving models, and to lower resolution climate models is leading to better understanding of the upscale cascade and suggesting new cloud-radiation parameterizations for climate models.

Cahalan, R. F.↗