Signal power distributions for simulated outdoor sound propagation in varying refractive conditions.

Probability distributions of acoustic signals propagating through the near-ground atmosphere are simulated by the parabolic equation method. The simulations involve propagation at four angles relative to the mean wind, with frequencies of 100, 200, 400, and 800 Hz. The environmental representation includes realistic atmospheric refractive profiles, turbulence, and ground interactions; cases are considered with and without parametric uncertainties in the wind velocity and surface heat flux.

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol.

Introduction: The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions.

Methods And Analysis: We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications.

Machine-learning of long-range sound propagation through simulated atmospheric turbulence.

Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions.

Distribution of the two-point product of complex amplitudes in the fully saturated scattering regime.

This Letter considers probability density functions (pdfs) involving products of the complex amplitudes observed at two points (which may, in general, involve separations in space, time, or frequency) in conditions of fully saturated scattering. First, the pdf is derived for the product of the complex amplitude at one point with the conjugate of the complex amplitude at another point. It is shown that the real and imaginary parts of this product each have a variance gamma pdf.

Multilevel modeling and regression of community annoyance to transportation noise.

A multilevel (hierarchical) model is devised that separates noise tolerance into variations occurring at the levels of individual listeners and communities. This approach successfully describes the characteristics of real community transportation noise surveys, with the individual- and community-level variations producing distinct statistical signatures, both of which are evident in the surveys. Predictions are provided for quantities such as the probability of annoyance based on the observed noise level and statistical parameters characterizing the community tolerance.

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations.

Description and quantification of uncertainty in outdoor sound propagation calculations.

The accuracy of outdoor sound propagation predictions is often limited by imperfect knowledge of the atmospheric and ground properties, and random environmental variations such as turbulence. This article describes the impact of such uncertainties, and how they can be efficiently addressed and quantified with stochastic sampling techniques such as Monte Carlo and Latin hypercube sampling (LHS). Extensions to these techniques, such as importance sampling based on simpler, more efficient propagation models, and adaptive importance sampling, are described.

Information-criterion based selection of models for community noise annoyance.

Statistical evidence for various models relating day-night sound level (DNL) to community noise annoyance is assessed with the Akaike information criterion. In particular, community-specific adjustments such as the community tolerance level (CTL, the DNL at which 50% of survey respondents are highly annoyed) and community tolerance spread (CTS, the difference between the DNL at which 90% and 10% are highly annoyed) are considered. The results strongly support models characterizing annoyance on a community-by-community basis, rather than with complete pooling and analysis of all available surveys.

Proper orthogonal decomposition and cluster weighted modeling for sensitivity analysis of sound propagation in the atmospheric surface layer.

Outdoor sound propagation predictions are compromised by uncertainty and error in the atmosphere and terrain representations, and sometimes also by simplified or incorrect physics. A model's predictive power, i.e.

Characterization of uncertainty in outdoor sound propagation predictions.

Predictive skill for outdoor sound propagation is assessed using high-resolution atmospheric fields from large-eddy simulations (LES). Propagation calculations through the full LES fields are compared to calculations through subsets of the LES fields that have been processed in typical ways, such as mean vertical profiles and instantaneous vertical profiles synchronized to the sound propagation. It is found that mean sound pressure levels can be predicted with low errors from the mean profiles, except in refractive shadow regions.