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Results for “probability distribution”
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Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
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Delay Probability Distributions of Acknowledged CSMA/CA With Finite Re-Transmissions Over Fading Channels
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Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)
The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.
Defining Radiation Belt Enhancement Events Based on Probability Distributions
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Hybrid computer techniques for determining probability distributions
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Rotation invariant probability distributions on the surface of a sphere, with applications to geodesy
Gravity anomalies and linear transforms over rotating spheres treated as random process with spectrum analysis and applications for geodesy
A probability distribution for the number of thunderstorm events at Cape Kennedy, Florida
Negative binomial distribution for number of thunderstorms at Cape Kennedy, Florida
A technique for assessing probable distributions of tropical precipitation echo lengths for X-band radar from Nimbus 3 HRIR data
Statistical model for simulating radar echoes from tropical precipitation for ocean surface region using Nimbus 3 satellite infrared data
Probability distributions for thunderstorm activity at Cape Kennedy, Florida
Binomial distribution models for thunderstorm activity at Cape Kennedy
Probability distribution of vertical longitudinal shear fluctuations.
This paper discusses some recent measurements of third and fourth moments of vertical differences (shears) of longitudinal velocity fluctuations obtained in unstable air at the NASA 150 m meteorological tower site at Cape Kennedy, Fla. Each set of measurements consisted of longitudinal velocity fluctuation time histories obtained at the 18, 30, 60, 90, 120 and 150 m levels, so that 15 wind-shear time histories were obtained from each set of measurements. It appears that the distribution function of the longitudinal wind fluctuations at two levels is not bivariate Gaussian. The implications of the results relative to the design and operation of aerospace vehicles are discussed.-
Turbulent plane Couette flow using probability distribution functions
A numerical scheme employing a combination of the discrete ordinate method and finite differences is developed for solving the one-dimensional form of Lundgren's (1967) model equation for turbulent plane Couette flow. The approach used requires no a priori assumption about the form of the turbulent distribution function, and the numerical solution is obtained directly from the governing differential equations. Two different types of boundary conditions (zero-gradient and Chapman-Enskog) for the distribution function are evaluated by comparing the numerical results with experimental data. It is found that: (1) the present approach gives convergent and stable results over a wide range of Reynolds numbers; (2) Lundgren's equation yields results that compare well with experimental data for mean velocity and skin friction in the case of simple Couette flow; (3) the zero-gradient boundary condition leads to a logarithmic flow profile; and (4) the Chapman-Enskog boundary condition provides very good agreement with experimental data when applied within the near-wall region.
Probable distribution of large impact basins on Venus - Comparison with Mercury and the moon
The reported study is based on the 12.5 cm wavelength data of Rumsey et al. (1974). The considered low resolution (80 km) radar image covers an area equivalent to 19% of the surface of Venus. The study had the objective to map potential large impact structures and relate their size frequency distribution to those of Mercury and the moon. The Venus radar map was analyzed using a color television film density slicer system for enhancement of subtle changes and gross patterns of contrasting radar reflectivity. Analysis by this technique permitted the recognition of 12 possible basins with diameters exceeding 600 km on about 8% of the total surface area of the planet. The preliminary basin size frequency distribution determined for Venus from these low resolution data suggests that the cloud-covered planet could be more cratered per unit area by basins than either the moon or Mercury.
A model for gust amplitude and gust length based on the bivariate gamma probability distribution function
A model of the largest gust amplitude and gust length is presented which uses the properties of the bivariate gamma distribution. The gust amplitude and length are strongly dependent on the filter function; the amplitude increases with altitude and is larger in winter than in summer.
A bivariate gamma probability distribution with application to gust modeling
A five-parameter gamma distribution (BGD) having two shape parameters, two location parameters, and a correlation parameter is investigated. This general BGD is expressed as a double series and as a single series of the modified Bessel function. It reduces to the known special case for equal shape parameters. Practical functions for computer evaluations for the general BGD and for special cases are presented. Applications to wind gust modeling for the ascent flight of the space shuttle are illustrated.
Some properties of a 5-parameter bivariate probability distribution
A five-parameter bivariate gamma distribution having two shape parameters, two location parameters and a correlation parameter was developed. This more general bivariate gamma distribution reduces to the known four-parameter distribution. The five-parameter distribution gives a better fit to the gust data. The statistical properties of this general bivariate gamma distribution and a hypothesis test were investigated. Although these developments have come too late in the Shuttle program to be used directly as design criteria for ascent wind gust loads, the new wind gust model has helped to explain the wind profile conditions which cause large dynamic loads. Other potential applications of the newly developed five-parameter bivariate gamma distribution are in the areas of reliability theory, signal noise, and vibration mechanics.
Probability distribution of wind retrieval error for the NASA scatterometer
The NASA scatterometer (NSCAT) is a spaceborne scatterometer scheduled to be deployed in the mid-1990s. An analysis of the wind retrieval error distribution for wind estimates based on backscatter measurements made by the NSCAT instrument is presented. The results are based on an end-to-end simulation of the scatterometer instrument and data processing. In general, the distribution of the wind speed error, when normalized, is independent of the true wind speed and direction. The wind speed error can be characterized by a normal distribution. The wind direction error is independent of the true wind speed, but depends on the true wind direction. Details for wind vectors with true wind speeds from 3 m/s to 33 m/s and true wind directions from 0 to 360 deg are presented.
Comparison of monthly rain rates derived from GPI and SSM/I using probability distribution functions
Three years of monthly rain rates over 5 deg x 5 deg latitude-longitude boxes have been calculated for oceanic regions 50 deg N-50 deg S from measurements taken by the Special Sensor Microwave/Imager on board the Defense Meteorological Satellite Program satellites using the technique developed by Wilheit et al. (1987, 1991). The annual and seasonal zonal-mean rain rates are larger than Jaeger's (1983) climatological estimates but are smaller than those estimated from the GOES precipitation index (GPI) for the same period. Regional comparison with the GPI showed that these rain rates are smaller in the north Indian Ocean and in the southern extratropics where the GPI is known to overestimate. The differences are also dominated by a jump at 170 deg W in the GPI rain rates across the mid-Pacific Ocean. This jump is attributed to the fusion of different satellite measurements in producing the GPI.