VOCAR: An Experiment in Variability of Coastal Atmospheric Refractivity.

Paulus, R. A.

doi:10.21236/ada289202

Cited by 9 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lower frequency (0.263 GHz), the source and receiver heights, and the marine environment are consistent with the recent Variability of Coastal Atmospheric Refractivity (VOCAR) experiment (see Paulus [1994] for an overview). However, we made no attempt to simulate that experiment.…”

Section: Parabolic Equation Calculations For Em Propagation Through Rsupporting

confidence: 83%

Electromagnetic wave propagation through simulated atmospheric refractivity fields

Gilbert¹,

Xiao²,

Khanna³

et al. 1999

Radio Science

View full text Add to dashboard Cite

Abstract. Large-eddy simulation (LES) provides three-dimensional, time-dependent fields of turbulent refractivity in the atmospheric boundary layer on spatial scales down to a few tens of meters. These fields are directly applicable to the computation of electromagnetic (EM) wave propagation in the megahertz range but not in the gigahertz range. We present an approximate technique for extending LES refractivity fields to the smaller scales needed for calculating EM propagation at gigahertz frequencies. We demonstrate the technique by computing refractivity fields through 128 3 LES, extending them to smaller scales in two dimensions, and using them in a parabolic equation EM propagation model. At 0.263 GHz the very small scale structure in the extended fields has a negligible effect on the predicted EM levels. At 2 GHz, however, it increases the predicted levels by 15-25 dB. We relate these results to the refractivity structure sampled by EM waves at 0.263 and 2 GHz. We also show that at long range an EM field calculated through an LES-based refractivity field is generally less coherent and significantly weaker than one computed from a "plywood" (i.e., stratified, range-independent) model of the small-scale refractivity field. We give a physical explanation for the differences in the EM fields computed in these two ways. Finally, although the plywood model gives results that fit the EM levels observed in the recent Variability of Coastal Atmospheric Refractivity (VOCAR) experiment, it is not physically realistic. The instantaneous top of the atmospheric boundary layer is known to be sharp and horizontally varying, and we show that using such a top also yields a fit to the VOCAR data. IntroductionThe research discussed here is an interdisciplinary effort between boundary-layer meteorologists and physicists working in electromagnetic (EM) propagation. The purpose of the collaboration was to use large-eddy simulation (LES) methods and a parabolic equation ( Large-Eddy SimulationTurbulence is characterized by three-dimensional, stochastic motions (eddies) of a wide range of spatial scales. These motions generate corresponding stochastic structure in advected scalars such as refractive index. The largest eddies in the turbulent atmospheric boundary layer scale with the integral scale, which is of the order of the boundary-layer depth zi (-1 km). The smallest eddies scale with the Kolmogorov microscale, which is typically of the order of 1 mm. A direct numerical solution of the governing equations for such a turbulent flow is far beyond the present or expected capabilities of computers. 1413

show abstract

Section: Parabolic Equation Calculations For Em Propagation Through Rsupporting

confidence: 83%

Electromagnetic wave propagation through simulated atmospheric refractivity fields

Gilbert¹,

Xiao²,

Khanna³

et al. 1999

Radio Science

View full text Add to dashboard Cite

show abstract

“…Meteorological data and path geometries used in the simulations of Section IV were taken from the variability of coastal atmospheric refractivity (VOCAR) experiment [12]. The meteorology is discussed in [13].…”

Section: Vocar Experimental Datamentioning

confidence: 99%

“…Let the eigenvalues of and corresponding eigenvectors be arranged in descending order . A linear combination of orthogonal functions based on the first eigenvectors yields a minimum mean-squared error representation (12) where . 4) An optimization algorithm will vary the base height and the coefficients to obtain a refractivity profile estimate.…”

Section: Appendix Shifted Empirical Orthogonal Function (Seof) Refracmentioning

confidence: 99%

Estimation of radio refractivity structure using matched-field array processing

Gerstoft

Gingras

Rogers

et al. 2000

IEEE Trans. Antennas Propagat.

View full text Add to dashboard Cite

In coastal regions the presence of the marine boundary layer can significantly affect RF propagation. The relatively high specific humidity of the underlying "marine layer" creates elevated trapping layers in the radio refractivity structure. While direct sensing techniques provide good data, they are limited in their temporal and spatial scope. There is a need for assessing the three-dimensional (3-D) time-varying refractivity structure. Recently published results (Gingras et al.[1]) indicate that matched-field processing methods hold promise for remotely sensing the refractive profile structure between an emitter and receive array. This paper is aimed at precisely quantifying the performance one can expect with matched-field processing methods for remote sensing of the refractivity structure using signal strength measurements from a single emitter to an array of radio receivers. The performance is determined via simulation and is evaluated as a function of: 1) the aperture of the receive array; 2) the refractivity profile model; and 3) the objective function used in the optimization. Refractivity profile estimation results are provided for a surface-based duct example, an elevated duct example, and a sequence of time-varying refractivity profiles. The refractivity profiles used were based on radiosonde measurements collected off the coast of southern California.

show abstract

“…The simulated d vector was 1234 points long, with a step size of 64 m. Two refractive environments were estimated. First, a surface‐based duct from the VOCAR 1993 experiment [ Paulus , ] with homogeneous turbulence generated by and and spectrum described by . Next, an elevated duct from the VOCAR 1993 experiment with inhomogeneous turbulence simulated using .…”

Section: Inversion Resultsmentioning

confidence: 99%

Estimating refractivity from propagation loss in turbulent media

2016

View full text Add to dashboard Cite

This paper estimates lower atmospheric refractivity (M‐profile) given an electromagnetic (EM) propagation loss (PL) measurement. Specifically, height‐independent PL measurements over a range of 10–80 km are used to infer information about the existence and potential parameters of atmospheric ducts in the lowest 1 km of the atmosphere. The main improvement made on previous refractivity estimations is inclusion of range‐dependent fluctuations due to turbulence in the forward propagation model. Using this framework, the maximum likelihood (ML) estimate of atmospheric refractivity has good accuracy, and with prior information about ducting the maximum a priori (MAP) refractivity estimate can be found. Monte Carlo methods are used to estimate the mean and covariance of PL, which are fed into a Gaussian likelihood function for evaluation of estimated refractivity probability. Comparisons were made between inversions performed on propagation loss data simulated by a wide angle parabolic equation (PE) propagation model with added homogeneous and inhomogeneous turbulence. It was found that the turbulence models produce significantly different results, suggesting that accurate modeling of turbulence is key.

show abstract

VOCAR: An Experiment in Variability of Coastal Atmospheric Refractivity.

Cited by 9 publications

References 0 publications

Electromagnetic wave propagation through simulated atmospheric refractivity fields

Electromagnetic wave propagation through simulated atmospheric refractivity fields

Estimation of radio refractivity structure using matched-field array processing

Estimating refractivity from propagation loss in turbulent media

Contact Info

Product

Resources

About