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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Total ozone and aerosol optical depths inferred from radiometric measurements in the Chappuis absorption band

A second-derivative smoothing technique, commonly used in inversion work, is applied to the problem of inferring total columnar ozone amounts and aerosol optical depths. The application is unique in that the unknowns may be solved for directly without employing standard inversion methods. It is shown, however, that by employing inversion constraints, better solutions are normally obtained. The method is quite versatile and able to deal with varying total ozone and various aerosol size distributions. The technique is applied first in simulation, then to 119 days of measurements taken in Tucson, Arizona, that are compared to TOMS values for the same dates. The technique is also applied to two measurements taken at Mauna Loa, Hawaii, for which Dobson ozone values are available in addition to the TOMS values, and the results agree to within 15 percent. It is also shown through simulations that additional information can be obtained from measurements outside the Chappuis band. This approach reduces the bias and spread of the estimated total ozone and is unique in that it uses measurements from both the Chappuis and Huggins absorption bands.

Flittner, D. E.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1983

New observational constraints on the temperature inversions of Uranus and Neptune

The presence of a temperature inversion in the lower stratospheres of both Uranus and Neptune is confirmed by the 20-micron photometric data presented. It is found that the brightness temperature difference between 17.8 and 19.6 microns is 0.8 + or - 0.5 K for Uranus and 1.8 + or - 0.6 K for Neptune, implying that the temperature inversions of both planets are weaker than previously thought. Comparisons with model atmospheres suggested by Appleby (1980) imply that these temperature inversions may be understood as a consequence of heating through CH4 and aerosol absorption of sunlight. The stratospheric CH4 mixing ratio of Neptune must, however, be higher than that at the temperature minimum.

Tokunaga, A. T.↗
NASA NTRS1986

Hierarchial implicit dynamic least-square solution algorithm

This paper develops an implicit type transient solution strategy which possesses hierarchial levels of application. In particular, due to the manner of formulation, stiffness updating, assembly inversion, solution constraint, as well as iteration are all performed at a localized level. The level of iterative calculations depends on the type of hierarchial partitioning employed, namely degree of freedom, nodal, elemental, material/nonlinear group, substructural, and so on. Since the iterative solution process and application of constraints are applied at a local level, the resulting so-called hierarchial implicit solution algorithm possesses very stable and efficient numerical properties and is highly storage efficient. To demonstrate the scheme, the results of several benchmark examples are presented. These enable comparisons with the Newton-Raphson solved implicit transient solution method. Overall the comparisons illustrate the superior stability and efficiency of the hierarchial scheme.

Padovan, J.↗
NASA NTRS1991

Ocean circulation modeling by use of radar altimeter data

The project will investigate the use of radar altimetry (RA) data in the determination of the ocean circulation models. RA data will be used to verify prognostic experiments of the steady state and seasonal cycle of large-scale circulation models and the statistical steady state of eddy-resolving models. The data will serve as initial and update conditions in data assimilation experiments and as constraints in inverse calculations. The aim of the project is a better understanding of ocean physics, the determination and mapping of ocean currents, and a contribution to the establishment of ocean circulation models for climate studies. The goal of the project is to use satellite radar altimetry data for improving our knowledge of ocean circulation both in a descriptive sense and through the physics that govern the circulation state. The basic tool is a series of ocean circulation models. Depending on the model, different techniques will be applied to incorporate the RA data.

Olbers, Dirk↗
NASA NTRS1996

The Lunar Crust: Global Structure and Signature of Major Basins

New lunar gravity and topography data from the Clementine Mission provide a global Bouguer anomaly map corrected for the gravitational attraction of mare fill in mascon basins. Most of the gravity signal remaining after corrections for the attraction of topography and mare fill can be attributed to variations in depth to the lunar Moho and therefore crustal thickness. The large range of global crustal thickness (approx. 20-120 km) is indicative of major spatial variations in melting of the lunar exterior and/or significant impact-related redistribution. The 6l-km average crustal thickness, constrained by a depth-to-Moho measured during the Apollo 12 and 14 missions, is preferentially distributed toward the farside, accounting for much of the offset in center-of-figure from the center-of-mass. While the average farside thickness is 12 km greater than the nearside, the distribution is nonuniform, with dramatic thinning beneath the farside, South Pole-Aitken basin. With the global crustal thickness map as a constraint, regional inversions of gravity and topography resolve the crustal structure of major mascon basins to half wavelengths of 150 km. In order to yield crustal thickness maps with the maximum horizontal resolution permitted by the data, the downward continuation of the Bouguer gravity is stabilized by a three- dimensional, minimum-slope and curvature algorithm. Both mare and non-mare basins are characterized by a central upwarped moho that is surrounded by rings of thickened crust lying mainly within the basin rims. The inferred relief at this density interface suggests a deep structural component to the surficial features of multiring lunar impact basins. For large (greater than 300 km diameter) basins, moho relief appears uncorrelated with diameter, but is negatively correlated with basin age. In several cases, it appears that the multiring structures were out of isostatic equilibrium prior to mare emplacement, suggesting that the lithosphere was strong enough to maintain their state of stress to the present.

Neumann, Gregory A.↗
NASA NTRS2007

Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Kawa, S. R.↗
NASA NTRS2010

Comparing Global Atmospheric CO2 Flux and Transport Models with Remote Sensing (and Other) Observations

We report recent progress derived from comparison of global CO2 flux and transport models with new remote sensing and other sources of CO2 data including those from satellite. The overall objective of this activity is to improve the process models that represent our understanding of the workings of the atmospheric carbon cycle. Model estimates of CO2 surface flux and atmospheric transport processes are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, to provide the basic framework for carbon data assimilation, and ultimately for future projections of carbon-climate interactions. Models can also be used to test consistency within and between CO2 data sets under varying geophysical states. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 2000 through 2009. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at 1x1 degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-3), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to remote sensing observations from TCCON, GOSAT, and AIRS as well as relevant in situ observations. Examples of the influence of key process representations are shown from both forward and inverse model comparisons. We find that the model can resolve much of the synoptic, seasonal, and interannual variability in the observations, although reasons for persistent discrepancies in northern hemisphere vegetation uptake are examined. At this time, we do not find any serious shortcomings in the model transport representation, but this is still the subject of close scrutiny. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in CO2 fluxes and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Kawa, S. R.↗
NASA NTRS2022

Constraining Venus and Mars Internal Structures from the Gravity Response to Atmospheric Loading

The gravity fields of celestial bodies that possess an atmosphere are periodically perturbed by the redistribution of fluid mass associated with the atmospheric dynamics. A component of this perturbation is due to the gravitational response of the body to the deformation of its surface induced by the atmospheric pressure loading. The magnitude of this effect depends on the relation between the loading and the response in terms of geopotential variations measured by the load Love numbers. In this work, we simulate and analyze the gravity field generated by the atmospheres of Venus and Mars by accounting for different models of their internal structure. By precisely characterizing the phenomena that drive the mass transportation in the atmosphere through general circulation models, we determine the effect of the interior structure on the response to the atmospheric loading. An accurate estimation of the time-varying gravity field, which measures the atmospheric contribution, may provide significant constraints on the interior structure through the measurement of the load Love numbers. A combined determination of tidal and load Love numbers would enhance our knowledge of the interior of planetary bodies, providing further geophysical constraints in the inversion of internal structure models.The gravity fields of celestial bodies that possess an atmosphere are periodically perturbed by the redistribution of fluid mass associated with the atmospheric dynamics. A component of this perturbation is due to the gravitational response of the body to the deformation of its surface induced by the atmospheric pressure loading. The magnitude of this effect depends on the relation between the loading and the response in terms of geopotential variations measured by the load Love numbers. In this work, we simulate and analyze the gravity field generated by the atmospheres of Venus and Mars by accounting for different models of their internal structure. By precisely characterizing the phenomena that drive the mass transportation in the atmosphere through general circulation models, we determine the effect of the interior structure on the response to the atmospheric loading. An accurate estimation of the time-varying gravity field, which measures the atmospheric contribution, may provide significant constraints on the interior structure through the measurement of the load Love numbers. A combined determination of tidal and load Love numbers would enhance our knowledge of the interior of planetary bodies, providing further geophysical constraints in the inversion of internal structure models.

Mars↗
NASA NTRS1982

On the inversion of block tridiagonals without storage constraints

A strategy was developed to permit trade-offs between the number of floating point operations required and the storage requirements for the solution of certain difference problems, such as block tridiagonal systems of equations. This is done by recomputing some intermediate results instead of storing them. Reducing the storage to the square root of the current requirement roughly doubles the number of computations. Reducing the storage more than this tends to make the number of computations prohibitively large. In theory, if m is the order of each sub-matrix in the block tridiagonal matrix, one can solve any linear system with only 5m(2) + 1 temporary storage cells. In many cases m is a constant and quite small. For example, in solving a factored form of the three-dimensional Navier-Stokes equations, the size m of the block tridiagonals is 5. This method lends itself to efficient use on computers with parallel processing or vector processing architectures. On these computers the larger number of floating point operations is more than offset by the decrease in I/O and the increased percentage of vector operations made possible by this algorithm.

Merriam, M. L.↗
NASA NTRS2013

Effects of Systematic and Random Errors on the Retrieval of Particle Microphysical Properties from Multiwavelength Lidar Measurements Using Inversion with Regularization

In this work we study the effects of systematic and random errors on the inversion of multiwavelength (MW) lidar data using the well-known regularization technique to obtain vertically resolved aerosol microphysical properties. The software implementation used here was developed at the Physics Instrumentation Center (PIC) in Troitsk (Russia) in conjunction with the NASA/Goddard Space Flight Center. Its applicability to Raman lidar systems based on backscattering measurements at three wavelengths (355, 532 and 1064 nm) and extinction measurements at two wavelengths (355 and 532 nm) has been demonstrated widely. The systematic error sensitivity is quantified by first determining the retrieved parameters for a given set of optical input data consistent with three different sets of aerosol physical parameters. Then each optical input is perturbed by varying amounts and the inversion is repeated. Using bimodal aerosol size distributions, we find a generally linear dependence of the retrieved errors in the microphysical properties on the induced systematic errors in the optical data. For the retrievals of effective radius, number/surface/volume concentrations and fine-mode radius and volume, we find that these results are not significantly affected by the range of the constraints used in inversions. But significant sensitivity was found to the allowed range of the imaginary part of the particle refractive index. Our results also indicate that there exists an additive property for the deviations induced by the biases present in the individual optical data. This property permits the results here to be used to predict deviations in retrieved parameters when multiple input optical data are biased simultaneously as well as to study the influence of random errors on the retrievals. The above results are applied to questions regarding lidar design, in particular for the spaceborne multiwavelength lidar under consideration for the upcoming ACE mission.

vertical profiles↗
NASA NTRS1992

Inversion of parameters for semiarid regions by a neural network

Microwave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.

Zurk, Lisa M.↗
NASA NTRS1985

Speed of sound in the solar interior

The sound speed of the solar interior is directly determinable on the basis of the frequencies of solar 5-min oscillations, irrespective of solar model, and relying only on a simple asymptotic description of the oscillations in terms of trapped acoustic waves. It is plausible that, by using this asymptotic determination as an initial trial in a more accurate inversion, and imposing constraints of smoothness on the solution resulting from the iteration, a good model representing the large scale structure of the sun which satisfies the observed frequencies may be determined.

Christensen-Dalsgaard, J.↗
NASA NTRS1988

Atmospheric profiling of water vapour with a 20.5-23.5 GHz autocorrelation radiometer

A tropospheric water vapor profiling system is presented. The hardware consists of an upward looking radiometer (CORRAD) deployed at ground level. The CORRAD measures the autocorrelation of the downwelling thermal emission from the atmosphere over a passband of 20.5 to 23.5 GHz out to a maximum time delay of 6.1 ns. This produces 100 MHz resolution imaging of the complete emission spectrum about the 22.235 GHz water vapor resonance line. The 31 equivalent frequency channels produced by Fourier transformation of the data provide additional constraints on the inversion process required to estimate the water vapor profile, as compared to standard 2 to 5 frequency channel profiling systems. The CORRAD hardware is described, and radiometer and radiosonde profiles are compared.

Ruff, C. S.↗
NASA NTRS2007

Paleomagnetic Analysis Using SQUID Microscopy

Superconducting quantum interference device (SQUID) microscopes are a new generation of instruments that map magnetic fields with unprecedented spatial resolution and moment sensitivity. Unlike standard rock magnetometers, SQUID microscopes map magnetic fields rather than measuring magnetic moments such that the sample magnetization pattern must be retrieved from source model fits to the measured field data. In this paper, we presented the first direct comparison between paleomagnetic analyses on natural samples using joint measurements from SQUID microscopy and moment magnetometry. We demonstrated that in combination with apriori geologic and petrographic data, SQUID microscopy can accurately characterize the magnetization of lunar glass spherules and Hawaiian basalt. The bulk moment magnitude and direction of these samples inferred from inversions of SQUID microscopy data match direct measurements on the same samples using moment magnetometry. In addition, these inversions provide unique constraints on the magnetization distribution within the sample. These measurements are among the most sensitive and highest resolution quantitative paleomagnetic studies of natural remanent magnetization to date. We expect that this technique will be able to extend many other standard paleomagnetic techniques to previously inaccessible microscale samples.

Weiss, Benjamin P.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-21838-DND

Correlation relationships between particle microphysical parameters and optical data are investigated. The results show that surface-area concentrations and extinction coefficients are linearly correlated with a correlation coefficient above 0.99 for arbitrary particle size distribution. The correlation relationships that we obtained can be used as constraints in our inversion of optical lidar data. Simulation studies demonstrate a significant stabilization of aerosol microphysical data products if we apply the gradient correlation method (GCM) in our traditional regularization technique.

Alexei Kolgotin↗