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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 19 records

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗

Multi-Group Maximum Entropy Model for Translational Non-Equilibrium

The aim of the current work is to describe a new model for flows in translational non- equilibrium. Starting from the statistical description of a gas proposed by Boltzmann, the model relies on a domain decomposition technique in velocity space. Using the maximum entropy principle, the logarithm of the distribution function in each velocity sub-domain (group) is expressed with a power series in molecular velocity. New governing equations are obtained using the method of weighted residuals by taking the velocity moments of the Boltzmann equation. The model is applied to a spatially homogeneous Boltzmann equation with a Bhatnagar-Gross-Krook1(BGK) model collision operator and the relaxation of an initial non-equilibrium distribution to a Maxwellian is studied using the model. In addition, numerical results obtained using the model for a 1D shock tube problem are also reported.

Entropy↗

On the maximum-entropy/autoregressive modeling of time series

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Chao, B. F.↗

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R 2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m −3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Ozone↗

Bounding Species Distribution Models

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for "clamping" model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used [Current Zoology 57 (5): 642-647, 2011].

Stohlgren, Thomas J.↗

Far-infrared emission, gas, and ultraluminous HII regions in M101

We have made deconvolved maps of M101 in each of the 4 IRAS bands using a new maximum-entropy based model for reconstructing distributions with correlated structure on multiple scales. This new deconvolution procedure, developed by Weir (1992, J.Opt.Soc.Am., submitted), is superior to other maximum entropy-based techniques for several reasons. For our purposes, an important advantage is that it has less artifacts and greatly reduced systematic biases compared with the 'HiRes' maps of M101 produced by Aumann, Fowler, and Melnyk (1990, AJ, 99, 1674), thus it is accurate enough to perform aperture photometry. The deconvolved maps have a maximum resolution of approximately 30 sec, sufficient to resolve the brightest HII complexes and much of the spiral structure. The new maps and our comparison of the far-infrared, gas, and optical distributions are being written up in Kenney, Weir & Scoville (1992, in preparation). The quality of the reconstructed far-infrared maps is good enough for us to carry out most of the analyses outlined in the original proposal. From a ratio of the 60 micron and 100 micron maps, we have found that the ultraluminous HII complexes in the outer galaxy have the hottest dust temperatures with T approximately 50-60 K, which is twice as hot as most of the disk. Their extraordinary luminosity in the far-infrared is due to a relatively small amount of dust being heated to high temperatures, rather than a large concentration of dust and gas. A map of the dust opacity at 60 microns shows good overall agreement with a map of cold gas (HI+H2), indicating that throughout most of the galaxy only approximately 20 percent of the dust is warm enough to be detected by IRAS. The ultraluminous HII complexes have a high luminosity-to-gas mass ratio, independent of whether the far-infrared or the H-alpha emission line is used to measure the luminosity, which implies that gas is being converted into high mass stars more rapidly in these complexes compared to other locations in the disk.

Kenney, J. D. P.↗

Structural modelling and control design under incomplete parameter information: The maximum-entropy approach

A stochastic structural control model is described. In contrast to the customary deterministic model, the stochastic minimum data/maximum entropy model directly incorporates the least possible a priori parameter information. The approach is to adopt this model as the basic design model, thus incorporating the effects of parameter uncertainty at a fundamental level, and design mean-square optimal controls (that is, choose the control law to minimize the average of a quadratic performance index over the parameter ensemble).

Hyland, D. C.↗

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

environmental modeling↗

On the velocity fields of elliptical galaxies

A family of self-consistent maximum entropy dynamical models is presented for the triaxial 'perfect ellipsoids'. These models are projected in different viewing geometries to explore the possible morphologies of the 2D radial-velocity fields of elliptical galaxies. It is found that, typically, about half the time an 'ordinary' velocity field is seen with a mixture of major- and minor-axis rotation. For other lines of sight, the velocity fields are more complicated, showing multiple peaks, steep gradients, strongly twisted contours, or kinematically distinct or counter-rotating cores. The origin of each of these velocity field morphologies is explained in terms of competing contributions from the major families of tube orbits.

Statler, Thomas S.↗

Size and Structure of the Sequence Space of Repeat Proteins

The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family— the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occurring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of *30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of frustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family and suggests new strategies for protein design.

Jacopo Marchi↗

Echo mapping of broad H-beta emission in NGC 5548

A one-dimensional echo map of the broad H-beta emission-line region in the Seyfert 1 galaxy NGC 5548 is reconstructed by using a maximum entropy technique to model the variable optical continuum and integrated H-beta emission-line fluxes observed during the 1989-1990 monitoring campaign as reported by Peterson et al. (1991). The echo map has a strong peak at a time delay of 20 lt-days which has an unresolved full width of 20 days at half-maximum. The H-beta response at time delay zero is less than one-third of that at 20 days, implying a deficit of H-beta-emitting gas near the line of sight to the continuum source. This rules out spherically symmetric and edge-on disk geometries for isotropically emitting H-beta clouds.

Horne, Keith↗

Radio-source structure in astrometric and geodetic very long baseline interferometry

An algorithm to calculate source structure corrections for bandwidth synthesis delays and delay rates is used to refer the position of extragalacitc radio sources to a specific feature of each source's morphology. The delay and delay rate are obtained from VBLI observations. The algorithm and its theoretical basis are described, and simulations for a simple two-component source are discussed. VLBI data for the complex radio source NRAO 140 are analyzed. For this source, structure corrections are calculated with maps from three different VLBI imaging techniques: the hybrid-CLEAN algorithm, the maximum entropy method, and model fitting. The calculated structure corrections are compared with brightness distributions to the actual data. The results are used to map NRAO 140. It is found that the CLEAN map produces the most accurate structure corrections.

Charlot, P.↗

Bryce Canyon Water Resources: Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and fens. These species contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and the services it provides to the park. Unfortunately, many of these GDEs are too small to identify with traditional Earth observation platforms and are difficult to physically reach for monitoring purposes. This project partnered with the National Park Service to identify springs and seeps as a proxy for GDEs within Bryce Canyon from 2013–2022. Furthermore, this project tested the feasibility of various methods to detect and monitor springs and seeps and therefore facilitate the partner’s efforts to conserve these ecologically valuable GDEs in Bryce Canyon. The team mapped groundwater discharge with high resolution National Agriculture Imagery Program (NAIP) and assessed park vegetation trends with Landsat 8 Operational Land Imager (OLI) and PlanetScope imagery. In-situ precipitation data and the Western Land Data Assimilation System (WLDAS) were used to produce time series of climatic variables. Seeps and spring locations were predicted using random forest classification and maximum entropy machine learning models.

Groundwater dependent ecosystems↗

Modulation of Gravity Waves by Tides as Seen in CRISTA Temperatures

During shuttle missions STS-66 (November, 1994) and STS-85 (August, 1997) the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere (CRISTA) acquired temperature data with very high spatial resolution. These are analyzed for gravity waves (GW). The altitude range spans the whole middle atmosphere from the tropopause up to the mesopause. In the upper mesosphere tidal amplitudes exceed values of 10 K. Modulation of GW activity by the tides is observed and analyzed using CRISTA temperatures and tidal predictions of the Global Scale Wave Model (GSWM). The modulation process is identified as a tidally-induced change of the background buoyancy frequency. The findings agree well with the expectations for saturated GW and are the first global scale observations of this process.

SHUTTLE MISSIONS↗