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At least 235 records · Page 13

A Global Model for Estimating Atmospheric Phase Scintillation Statistics

Since 2007, the National Aeronautics and Space Administration (NASA) has been collecting atmospheric phase turbulence data from various NASA ground stations throughout the world. The goal of these measurement campaigns has been to generate statistics to characterize the local site turbulence conditions and their impact on widely distributed ground based antenna arrays. This is of critical importance for the situation of uplink arraying, in which a priori knowledge of the fast varying turbulent conditions of water vapor in the troposphere may not be known, and will impact the power combining efficiency of ground based transmitting arrays. Therefore, the design of these type of systems will be dependent on the local climatology of the particular ground station site. Based on the 30+ station years of data collected characterizing atmospheric phase scintillation statistics at various sites, a global model is presented which attempts to predict the average phase statistics of a generic site based on local surface weather data, such as surface pressure, temperature, relative humidity, wind speed, and median wind direction. A model is proposed and based on a standard log power distribution similar to amplitude scintillation models trained on the existing data sets and shows reasonable accuracy against existing data sets.

propagation↗

Numerical Modeling of the Global Atmosphere

Under this grant, we continued development and evaluation of the updraft downdraft model for cumulus parameterization. The model includes the mass, rainwater and vertical momentum budget equations for both updrafts and downdrafts. The rainwater generated in an updraft falls partly inside and partly outside the updraft. Two types of stationary solutions are identified for the coupled rainwater budget and vertical momentum equations: (1) solutions for small tilting angles, which are unstable; (2) solutions for large tilting angles, which are stable. In practical applications, we select the smallest stable tilting angle as an optimum value. The model has been incorporated into the Arakawa-Schubert (A-S) cumulus parameterization. The results of semi-prognostic and single-column prognostic tests of the revised A-S parameterization show drastic improvement in predicting the humidity field. Cheng and Arakawa presents the rationale and basic design of the updraft-downdraft model, together with these test results. Cheng and Arakawa, on the other hand gives technical details of the model as implemented in current version of the UCLA GCM.

Arakawa, Akio↗

The GRAM-3 model

The Global Reference Atmosphere Model (GRAM) is under continuous development and improvement. GRAM data were compared with Middle Atmosphere Program (MAP) predictions and with shuttle data. An important note: Users should employ only step sizes in altitude that give vertical density gradients consistent with shuttle-derived density data. Using too small a vertical step size (finer then 1 km) will result in what appears to be unreasonably high values of density shears but what in reality is noise in the model.

Justus, C. G.↗

3-D Atmospheric Modeling of the Global Budget of N2O and its Isotopologues for 1980-2019: The Impact of Anthropogenic Emissions

Nitrous oxide (N2O) is the third most important anthropogenic greenhouse gas (GHG) and a major ozone-depleting substance (ODS). Its main sources include anthropogenic activities (mostly agriculture) and natural emissions from ocean and soils. However, emission estimates for individual sources are highly variable due to uncertainties in N2O lifetime estimates and partitioning among sources. We derive annual global N2O emissions for 1990-2019 using NOAA Global Monitoring Laboratory (GML) surface N2O observations and the N2O lifetime calculated in the NASA GEOS-5 chemistry climate model (CCM). The inferred global mean N2O emissions has gradually increased from ~15.8 TgN/yr in the early 1990s to ~17.8 TgN/yr in the 2010s. This implies that anthropogenic N2O emissions have grown rapidly from ~6.7 TgN/yr in the 1990s to about ~8.7 TgN/yr in the 2010s, a ~30% increase. With specially designed N2O isotopic tracers in 3-D GEOSCCM, we estimate that, on global average, stratospheric enrichment contributes about +7.7‰/yr, +7.6‰/yr, +8.0‰/yr to tropospheric N(sup α)-δ(sup 15), N(sup β)-δ(sup 15), and O-δ(sup 18) budget, respectively. To balance the global mean isotopic signature for pre-industrial terrestrial sources of N(sup α)-δ(sup 15) ~ 6.7‰, N(sup β)-δ(sup 15) ~ -12.6‰, O-δ(sup 18) ~ 35.4‰, our 3-D isotopic budget simulation using the GEOSCCM suggests global mean anthropogenic isotopic signatures in the recent decades are N(sup α)-δ(sup 15) ~ -18‰, N(sup β)-δ(sup 15) ~ -20‰, O-δ(sup 18) ~ 19‰. These anthropogenic isotopic estimates are significantly lighter than results from one-box atmospheric model-based estimates with the largest difference seen for N(sup β)-δ(sup 15). More surface isotopic measurements are needed to better quantify the N2O isotopic signatures.

Nitrous oxide↗

Atmospheric disturbance model for aircraft and space capable vehicles

An atmospheric disturbance model (ADM) is developed that considers the requirements of advanced aerospace vehicles and balances algorithmic assumptions with computational constraints. The requirements for an ADM include a realistic power spectrum, inhomogeneity, and the cross-correlation of atmospheric effects. The baseline models examined include the Global Reference Atmospheric Model Perturbation-Modeling Technique, the Dryden Small-Scale Turbulence Description, and the Patchiness Model. The Program to Enhance Random Turbulence (PERT) is developed based on the previous models but includes a revised formulation of large-scale atmospheric disturbance, an inhomogeneous Dryden filter, turbulence statistics, and the cross-correlation between Dryden Turbulence Filters and small-scale thermodynamics. Verification with the Monte Carlo approach demonstrates that the PERT software provides effective simulations of inhomogeneous atmospheric parameters.

Chimene, Beau C.↗

Global Model Estimates of Atmospheric Al, Ca, Fe, Si, and Ti from Dust and Non-Dust Aerosols Informed by EMIT Surface Mineralogy and Evaluated Against Observations

Atmospheric deposition of micro-nutrients like Fe has been shown to be important for ocean biogeochemistry. The largest source of atmospheric Fe and other elements (e.g., Ca, Al, Si, and Ti) is desert dust, although there are significant non-dust sources in some regions. However, past estimates of these elements have been substantially uncertain due to limited information about the composition of the desert source regions. Here we use elemental distributions estimated from new Earth Surface Mineral Dust Source Investigation (EMIT) observations, which provide mineralogical composition at the surface of the Earth based on imaging spectroscopy measurements from the International Space Station. We add in other sources of these elements (anthropogenic and natural) and compare to a compilation of available surface concentration data from stations over land and from shipborne observations. Our results suggest that the modeled distribution is similar to available observations, but discrepancies still exist in both natural desert dust regions as well as regions dominated by anthropogenic sources. Global budgets for the elements Ca, Al, Fe, Si, and Ti suggest that desert dust remains the dominant source for these elements but anthropogenic or volcanic sources are also important for these elements. Changes in elemental distributions since preindustrial times were also estimated.

aerosols↗

Four-D global reference atmosphere

Model generates values for pressure, density, temperature, and winds from surface level to orbital altitudes. Program can define altitude profiles of atmospheric parameters for simulated trajectory.

Robertson, B.↗

Recent improvements in the GISS model of the global atmosphere

A simulation of the July climatology by the GISS GCM produced tropospheric temperatures which were systematically 1-2 C lower than the observed temperatures in the middle latitudes of the Northern Hemisphere. Convective cloud cover was nearly double the observed level in this region. Planetary albedo was much higher than the observed July value and showed the wrong seasonal trend. These problems have been solved through modifications of the GISS scheme for the parameterization of cumulus convection.

Helfand, H. M.↗

GMI-IPS: Python Processing Software for Aircraft Campaigns

NASA's Atmospheric Tomography Mission (ATom) seeks to understand the impact of anthropogenic air pollution on gases in the Earth's atmosphere. Four flight campaigns are being deployed on a seasonal basis to establish a continuous global-scale data set intended to improve the representation of chemically reactive gases in global atmospheric chemistry models. The Global Modeling Initiative (GMI), is creating chemical transport simulations on a global scale for each of the ATom flight campaigns. To meet the computational demands required to translate the GMI simulation data to grids associated with the flights from the ATom campaigns, the GMI ICARTT Processing Software (GMI-IPS) has been developed and is providing key functionality for data processing and analysis in this ongoing effort. The GMI-IPS is written in Python and provides computational kernels for data interpolation and visualization tasks on GMI simulation data. A key feature of the GMI-IPS, is its ability to read ICARTT files, a text-based file format for airborne instrument data, and extract the required flight information that defines regional and temporal grid parameters associated with an ATom flight. Perhaps most importantly, the GMI-IPS creates ICARTT files containing GMI simulated data, which are used in collaboration with ATom instrument teams and other modeling groups. The initial main task of the GMI-IPS is to interpolate GMI model data to the finer temporal resolution (1-10 seconds) of a given flight. The model data includes basic fields such as temperature and pressure, but the main focus of this effort is to provide species concentrations of chemical gases for ATom flights. The software, which uses parallel computation techniques for data intensive tasks, linearly interpolates each of the model fields to the time resolution of the flight. The temporally interpolated data is then saved to disk, and is used to create additional derived quantities. In order to translate the GMI model data to the spatial grid of the flight path as defined by the pressure, latitude, and longitude points at each flight time record, a weighted average is then calculated from the nearest neighbors in two dimensions (latitude, longitude). Using SciPya's Regular Grid Interpolator, interpolation functions are generated for the GMI model grid and the calculated weighted averages. The flight path points are then extracted from the ATom ICARTT instrument file, and are sent to the multi-dimensional interpolating functions to generate GMI field quantities along the spatial path of the flight. The interpolated field quantities are then written to a ICARTT data file, which is stored for further manipulation. The GMI-IPS is aware of a generic ATom ICARTT header format, containing basic information for all flight campaigns. The GMI-IPS includes logic to edit metadata for the derived field quantities, as well as modify the generic header data such as processing dates and associated instrument files. The ICARTT interpolated data is then appended to the modified header data, and the ICARTT processing is complete for the given flight and ready for collaboration. The output ICARTT data adheres to the ICARTT file format standards V1.1. The visualization component of the GMI-IPS uses Matplotlib extensively and has several functions ranging in complexity. First, it creates a model background curtain for the flight (time versus model eta levels) with the interpolated flight data superimposed on the curtain. Secondly, it creates a time-series plot of the interpolated flight data. Lastly, the visualization component creates averaged 2D model slices (longitude versus latitude) with overlaid flight track circles at key pressure levels. The GMI-IPS consists of a handful of classes and supporting functionality that have been generalized to be compatible with any ICARTT file that adheres to the base class definition. The base class represents a generic ICARTT entry, only defining a single time entry and 3D spatial positioning parameters. Other classes inherit from this base class; several classes for input ICARTT instrument files, which contain the necessary flight positioning information as a basis for data processing, as well as other classes for output ICARTT files, which contain the interpolated model data. Utility classes provide functionality for routine procedures such as: comparing field names among ICARTT files, reading ICARTT entries from a data file and storing them in data structures, and returning a reduced spatial grid based on a collection of ICARTT entries. Although the GMI-IPS is compatible with GMI model data, it can be adapted with reasonable effort for any simulation that creates Hierarchical Data Format (HDF) files. The same can be said of its adaptability to ICARTT files outside of the context of the ATom mission. The GMI-IPS contains just under 30,000 lines of code, eight classes, and a dozen drivers and utility programs. It is maintained with GIT source code management and has been used to deliver processed GMI model data for the ATom campaigns that have taken place to date.

Damon, M. R.↗

Meridionally propagating interannual-to-interdecadal variability in a linear ocean-atmosphere model

Meridional oscillation modes in a global, primitive-equation coupled ocean-atmosphere model have been analyzed in order to determine whether they contain such meridionally propagating modes as surface-pressure perturbations with years-to-decades oscillation periods. A two-layer global ocean model and a two-level global atmosphere model were then formulated. For realistic parameter values and basic states, meridional modes oscillating at periods of several years to several decades are noted to be present in the coupled ocean-atmosphere model; the oscillation periods, travel times, and meridional structures of surface pressure perturbations in one of the modes are found to be comparable to the corresponding characteristics of observed sea-level pressure perturbations.

Mehta, Vikram M.↗

A new Mars radiation environment model with visualization

A new model for the radiation environment to be found on the planet Mars due to Galactic Cosmic Rays (OCR) has been developed at the NASA Langley Research Center. Solar modulated primary particles rescaled for Mars conditions are transported through the Martian atmosphere, with temporal properties modeled with variable timescales, down to the surface, with altitude and backscattering patterns taken into account. The Martian atmosphere has been modeled by using the Mars Global Reference Atmospheric Model--version 2001 (Mars-GRAM 2001). The altitude to compute the atmospheric thickness profile has been determined by using a model for the topography based on the data provided by the Mars Orbiter Laser Altimeter (MOLA) instrument on board the Mars Global Surveyor (MGS) spacecraft. The Mars surface composition has been modeled based on averages over the measurements obtained from orbiting spacecraft and at various landing sites, taking into account the possible volatile inventory (e.g., CO2 ice, H2O ice) along with its time variation throughout the Martian year. Particle transport has been performed with the HZETRN heavy ion code. The Mars Radiation Environment Model has been made available worldwide through the Space Ionizing Radiation Effects and Shielding Tools (SIREST) website, a project of NASA Langley Research Center. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Center LaRC↗

The GISS global climate-middle atmosphere model. II - Model variability due to interactions between planetary waves, the mean circulation and gravity wave drag

The variability which arises in the GISS Global Climate-Middle Atmosphere Model on two time scales is reviewed: interannual standard deviations, derived from the five-year control run, and intraseasonal variability as exemplified by statospheric warnings. The model's extratropical variability for both mean fields and eddy statistics appears reasonable when compared with observations, while the tropical wind variability near the stratopause may be excessive possibly, due to inertial oscillations. Both wave 1 and wave 2 warmings develop, with connections to tropospheric forcing. Variability on both time scales results from a complex set of interactions among planetary waves, the mean circulation, and gravity wave drag. Specific examples of these interactions are presented, which imply that variability in gravity wave forcing and drag may be an important component of the variability of the middle atmosphere.

Rind, D.↗

Modeling the terrestrial hydrology for the global atmosphere - The future role of satellite data

A global terrestrial hydrology model has been developed for the transport and storage of moisture and heat in the ground surface layer where the hydrological parameters react to diurnal and seasonal changes in the atmosphere. The spatial and temporal variability of land surface features is considered in the model by means of large scale parameterizations. The model can be either forced by the atmosphere using conventional meteorological data or coupled to an atmospheric general circulation model (GCM) for interactive studies. The global surface is divided into 4 deg longitude by 5 deg latitude cells while the ground is represented by a thin surface layer, a bulk layer (the root zone), and a deep layer (the ground water zone). Results are presented from a seven-day global experiment which was conducted utilizing the GLAS GCM (NASA Goddard Laboratory for Atmospheric Sciences). The model has demonstrated its capability to predict, over a large region, the overall soil moisture storage and major flux exchanges with the atmosphere above and the ground water below.

Lin, J. D.↗