A zonally averaged chemical-dynamical model of the lower thermosphere
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A vertically integrated formulation (VIF) model for sea ice/snow and land snow is discussed which can simulate the nonlinear effects of heat storage and transfer through the layers of snow and ice. The VIF demonstates the accuracy of the multilayer formulation, while benefitting from the computational flexibility of linear formulations. In the second part, the model is implemented in a seasonal dynamic zonally averaged climate model. It is found that, in response to a change between extreme high and low summer insolation orbits, the winter orbital change dominates over the opposite summer change for sea ice. For snow over land the shorter but more pronounced summer orbital change is shown to dominate.
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A computer model for microenvironment atomic oxygen exposure has been developed to extend atomic oxygen modeling capability to include shadowing and reflections. The model uses average exposure conditions established by the direct exposure model and extends the application of these conditions to treat surfaces of arbitrary shape and orientation.
Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.
Time series models successfully reproduce or predict geomagnetic activity indices from solar wind parameters. A method is presented that converts a type of nonlinear filter, the nonlinear Autoregressive Moving Average (ARMA) model to the nonlinear damped oscillator physical model. The oscillator parameters, the growth and decay, the oscillation frequencies and the coupling strength to the input are derived from the filter coefficients. Mathematical methods are derived to obtain unique and consistent filter coefficients while keeping the prediction error low. These methods are applied to an oscillator model for the Dst geomagnetic index driven by the solar wind input. A data set is examined in two ways: the model parameters are calculated as averages over short time intervals, and a nonlinear ARMA model is calculated and the model parameters are derived as a function of the phase space.
The nonlinear modeling and analysis of dc-dc converters in the continuous mode and discontinuous mode was done by averaging and discrete sampling techniques. A model was developed by combining these two techniques. This model, the discrete average model, accurately predicts the envelope of the output voltage and is easy to implement in circuit and state variable forms. The proposed model is shown to be dependent on the type of duty cycle control. The proper selection of the power stage model, between average and discrete average, is largely a function of the error processor in the feedback loop. The accuracy of the measurement data taken by a conventional technique is affected by the conditions at which the data is collected.
The average Earth's magnetic field is solved with complex mathematical models based on mean square integral. Depending on the selection of the Earth magnetic model, the average Earth's magnetic field can have different solutions. This paper presents a simple technique that takes advantage of the damping effects of the b-dot controller and is not dependent of the Earth magnetic model; but it is dependent on the magnetic torquers of the satellite which is not taken into consideration in the known mathematical models. Also the solution of this new technique can be implemented so easily that the flight software can be updated during flight, and the control system can have current gains for the magnetic torquers. Finally, this technique is verified and validated using flight data from a satellite that it has been in orbit for three years.
Numerical and analytical micromechanical models are presented to predict the thermoelastic behavior of a textile composite. In the numerical model, the unit-cell is discretized with finite elements, and periodic boundary conditions are imposed between opposite faces of the unit-cell. For a thin textile composite, stress gradients effects through the thickness are demonstrated. The consequent difference in the stiffness and strength behavior of thick and thin composites are discussed. The numerical model is implemented to predict 3-D thermo-elastic constants for a thick textile composite, and the plate thermo-mechanical properties for a thin textile composite. The numerical model is extended to compute the thermal residual microstresses due to processing to predict the composite failure envelopes. An analytical model - Selective Averaging Method (SAM) - is proposed, which is based on a judicious combination of stiffness and compliance averaging to estimate the 3-D elastic constants. Both the models are tested and verified for several examples by comparing the stiffness properties with elasticity solutions and available results.
A flow model has been developed for analyzing multistage turbomachinery flows. This model, referred to as the average passage flow model, describes the time-averaged flow field with a typical passage of a blade row embedded within a multistage configuration. Computer resource requirements, supporting empirical modeling, formulation code development, and multitasking and storage are discussed. Illustrations from simulations of the space shuttle main engine (SSME) fuel turbine performed to date are given.
A mathematical model is described which predicts the service life of nickel-cadmium batteries designed for geosynchronous orbit satellites. Regression analysis technique is used to analyze orbital data on second generation trickle charged batteries. The model gives average cell voltage as a function of design parameters, operating parameters and time. The voltage model has the properties of providing a good fit to the data, good predictive capability, and agreement with known battery performance characteristics. Average cell voltage can be predicted to within 0.02 volts for up to 8 years. This modeling shows that these batteries will operate reliably for 10 years. Third-generation batteries are expected to operate even longer.
In this paper we describe fundamental properties of an 'off-line' three-dimensional transport model, that is, a model which uses prescribed rather than predicted winds. The model is currently used primarily for studying problems of the middle atmosphere because we have not (yet) incorporated a formulation for the convective transport of trace species, a prerequisite for many tropospheric problems. The off-line model is simpler and less expensive than a model which predicts the wind and mass evolution (an 'on-line' model), but it is more complex than the two-dimensional (2-D) zonally averaged transport models often used in the study of chemistry and transport in the middle atmosphere. It thus serves as a model of intermediate complexity and can fill a useful niche for the study of transport and chemistry. We compare simulations of four tracers, released in the lower stratosphere, in both the on- and off-line models to document the difference resulting from differences in modeling the same problem with this intermediate model. These differences identify the price to be paid in going to a cheaper and simpler calculation. The off-line model transports a tracer in three dimensions. For this reason, it requires fewer approximations than 2-D transport model, which must parameterize the effects of mixing by transient and zonally asymmetric wind features. We compare simulations of the off-line model with simulations of a 2-D model for two problems. First, we compare 2-D and three-dimensional (3-D) models by simulating the emission of an NO(x)-like tracer by a fleet of high-speed aircraft. The off-line model is then used to simulate the transport of C-14 and to contrast its simulation properties to that of the host of 2-D models which participated in an identical simulation in a recent NASA model intercomparison. The off-line model is shown to be somewhat sensitive to the sampling strategy for off-line winds. Simulations with daily averaged winds are in very good qualitative agreement but are less diffusive than when driven with instantaneous winds sampled at half-hour intervals. Simulations with the off-line and 2-D models are quite similar in the middle and upper stratosphere but behave quite differently in the lower stratosphere, where the 3-D model has a substantially more vigorous circulation. The off-line model is quite realistic in its simulation of C-14. While there are still systematic differences between the 3-D calculation and the observations, the differences seem to be substantially reduced when compared with the body of 2-D simulations documented in the above mentioned NASA intercomparison, particularly at 31 deg N.
Values of eddy mixing coefficients, especially that for vertical mixing, are of particular importance for various photochemical models of the Mars atmosphere, which are either globally or zonally averaged. These models represent atmospheric transport processes in terms of eddy diffusion. While this is not appropriate for the advective transport by the mean meridional circulation (in general, a part of the eddy diffusion tensor actually does represent advection--a 'correction' to the Eulerian-mean circulation), it can be applied to simulate both the small- and larger-scale eddy transports of an atmospheric constituent. An effort is underway to estimate values of the eddy mixing coefficients for the Mars atmosphere using circulation data generated with the NASA Ames Mars GCM. This model simulates the three-dimensional winds in the atmosphere, and these are then used as inputs for a tracer transport model. The latter model has previously been used in interactive dust transport experiments with the Ames GCM. Idealized tracer transport experiments, with a conservative or nearly conservative hypothetical tracer, are performed to generate data from which the eddy diffusion coefficients can be estimated. Carrying out matched pairs of tracer experiments, using two very different initial states, permits all four components of the diffusion tensor to be determined. A large number of GCM experiments have been conducted, spanning a range of seasons and atmospheric dust loading, allowing the eddy mixing coefficients to be estimated for a variety of atmospheric conditions.
Frechet kernels for the RSL data in Hunson Bay are computed to determine the detailed depth-dependent sensitivity of the data to variations in a viscosity profile which is consistent with the past inferences. The RSL data provide a robust constraint on the average viscosity in the top half of the lower mantle only (the average must be near 10 exp 21 Pa s). The data admit models whose average viscosity in the deep mantle (below 1800 km depth) and in the upper mantle can differ significantly from the value (near 10 exp 21 Pa s) estimated for the top half of the lower mantle. The total variation in the viscosity from the surface to the CMB can exceed an order of magnitude or more and still satisfy the constraint provided by the Hudson Bay RSL data set. The necessity of invoking an isoviscous mantle model in previous studies is a consequence of the limited class of viscosity model solutions employed in those studies.
Voyager four-color imaging data of Triton are analyzed to calculate the bolometric hemispheric albedo as a function of latitude and longitude. Zonal averages of these data have been incorporated into a thermal balance model involving insolation, reradiation, and latent heat of sublimation of N2 ice for the surface. The current average bolometric albedo of Triton's polar caps is 0.8, implying an effective temperature of 34.2 K and a surface pressure of N2 of 1.6 microbar for unit emissivity. This pressure is an order of magnitude lower than the surface pressure of 18 microbar inferred from Voyager data (Broadfoot et al., 1989; Conrath et al., 1989), a discrepancy that can be reconciled if the emissivity of the N2 on Triton's surface is 0.66. The model predicts that Triton's surface north of 15 deg N latitude is experiencing deposition of N2 frosts, as are the bright portions of the south polar cap near the equator. This result explains why the south cap covers nearly the entire southern hemisphere of Triton.
Application of the digital signal processing technique of autoregressive-moving-average (ARMA) modeling to the estimation of power spectra and shock spectra from Space Shuttle lift-off flight accelerograms is described in this paper. The background for application to ARMA of lift-off accelerograms which are non-stationary in nature is exemplified through a step-by-step discussion of actual numerical results. Included is a discussion of pertinent mathematical background for the ARMA approximations. Potential areas for application of ARMA modeling in payload integration activities are suggested.
Lunar rocks may be classified into three major groups: (1) coarse-grained igneous rocks, (2) fine-grained igneous rocks, and (3) breccias. Group 1 is interpreted as primitive lunar crustal rocks that display various degrees of crushing and/or annealing. Group 2 is interpreted as volcanic rocks. Group 3 is interpreted as resulting from impacts on the lunar surface and is subdivided on the basis of matrix textures into fragmental breccias, crystalline breccias that have been annealed, and crystalline breccias with igneous matrices. A synthesis of the data concerning lunar highlands polymict breccias compels the prediction that the breccias should have homogeneous matrices from rock to rock within regions of the highlands of limited size where impact mixing has been efficient and extensive. But the returned breccias, even from one landing site, display a wide range in composition. This incompatibility between prediction and observation is a paradox that may be resolved by a process that acts after impact mixing to cause a differentiation of the breccia compositions. Partial melting of the local average crustal composition (as modeled by the average soil composition for each site) and separation of melt and residue in ejecta and/or fall-back blankets are compatible with the reviewed data and may resolve the paradox.
Fabry-Perot determinations of thermospheric temperatures from 630.0 nm nightglow line width measurements were carried out for the period April to August, 1983. The nightly variation of the thermospheric temperature measured on 53 nights is compared with MSIS model predictions and found to agree occasionally with the model but, on the average, to exceed model predictions by approximately 180 K. The largest differences, 400 to 500 K occur during strongly increasing geomagnetic activity. Significant differences occur both during high geomagnetic/low solar activity and during low geomagnetic/high solar activity.