Evaluation of simplified evaporation duct refractivity models for inversion problems

Saeger, J. T.; Grimes, N. G.; Rickard, H. Erin; Hackett, Erin E.

doi:10.1002/2014rs005642

Cited by 22 publications

(32 citation statements)

References 28 publications

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“…The parametric modified refractivity model used in this study is a piecewise evaporation duct model (Figure ) based on a model originally proposed by Gerstoft et al (), and modified and evaluated in Saeger et al (). The model is defined by an evaporation layer component and a mixed layer component:

M false(z false) = M_{0} + ({, \begin{matrix} array c_{0} (z - z_{normald} \ln (\frac{z + 0.00015}{0.00015})), & array z \leq z_{L} \\ array m_{1} z - M_{1}, & array z > z_{L} \end{matrix})

where z is the altitude in meters above mean sea level, M 0 is the sea surface modified refractivity (i.e., at z = 0).…”

Section: Methodsmentioning

confidence: 99%

“…The parametric modified refractivity model used in this study is a piecewise evaporation duct model (Figure 1) based on a model originally proposed by Gerstoft et al (2003), and modified and evaluated in Saeger et al (2015). The model is defined by an evaporation layer component and a mixed layer component:…”

Section: Refractivity Modelmentioning

confidence: 99%

“…This paper follows similar methods to perform inversions for evaporation duct parameters with GAs using a refractivity model that consists of three refractivity parameters similar to Gerstoft et al () and Gingras et al (). The refractivity model used in this paper differs from these prior studies (i.e., evaporative duct versus surface or elevated duct) but was examined in the study by Saeger et al (), which demonstrated its ability to match measured evaporative duct atmospheric refractivity and its resulting propagation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

Penton

Hackett

2018

Radio Science

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Radar performance is impacted by variations in atmospheric refractivity because it changes the direction of electromagnetic wave propagation. Because direct measurement and simulation of atmospheric refractivity at high resolution over large distances is challenging, inversion techniques have developed to address this knowledge gap in predicting refractivity. Here we use synthetic radar data to solve refractivity inversion problems, focusing on evaporation ducts. Unlike most prior “refractivity from clutter” studies, this study examines inversions based on point‐to‐point propagation rather than sea clutter. This approach removes complexities associated with uncertainties in surface reflection coefficients; however, the sea surface still plays a role in the forward scattering and reflection (multipath) of electromagnetic waves. Using synthetic data that permit quantitative evaluation of inverse solution accuracy and detailed knowledge regarding the sea state conditions used to produce the data, we evaluate the impact various representations of the sea surface have on the accuracy of refractivity inversions. These representations include neglect of the sea surface, use of the same phase‐resolved surface, and use of a statistically equivalent sea surface. Results show that neglect of the sea surface and use of a statistically equivalent sea surface significantly decreases the accuracy of inverse solutions, and this decrease primarily results from discrepancies in the multipath pattern that generate larger variations in the propagation than does the refractivity. We show that averaging propagation over several different phases of the sea surface aids in washing out the multipath pattern to increase sensitivity of the inversion to the features of the refractivity profile.

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M false(z false) = M_{0} + ({, \begin{matrix} array c_{0} (z - z_{normald} \ln (\frac{z + 0.00015}{0.00015})), & array z \leq z_{L} \\ array m_{1} z - M_{1}, & array z > z_{L} \end{matrix})

where z is the altitude in meters above mean sea level, M 0 is the sea surface modified refractivity (i.e., at z = 0).…”

Section: Methodsmentioning

confidence: 99%

Section: Refractivity Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

Penton

Hackett

2018

Radio Science

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“…The trials are intended to encompass a range of realistic atmospheric conditions for testing the inversion process. The trial refractivity parameters used in this study (c 0 , z d , and m 1 ; Table 3) were selected based on the Saeger et al (2015) study evaluating simplified evaporation duct models for inversion problems, as well as from evaluation of atmospheric data from the Tropical Air-Sea Propagation Study (Kulessa et al, 2017).…”

Section: Numerical Experimentsmentioning

confidence: 99%

Impact of Radar Data Sampling on the Accuracy of Atmospheric Refractivity Inversions Over Marine Surfaces

Matsko

Hackett

2019

Radio Science

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The turbulent nature of the marine atmospheric boundary layer and interactions across the air‐sea interface cause ever‐changing environmental conditions, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of EM systems requires in situ knowledge of the refractivity. Inversion approaches to estimate refractivity rely on measured EM data; however, despite its importance, few studies have examined the influence of data density on the accuracy of refractivity inversions. This study applies a bistatic radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of the sampling density of radar propagation loss data, and its source location, on accuracy of refractivity inversions. Genetic algorithms and a radar propagation model are used to perform the inversions. Numerical experiments examine various randomly distributed amounts of synthetic data from a 100‐m (altitude) by 60‐km (range) area. Three domains within this area are examined from which data were sourced. A data density of approximately 1% of the prediction domain yielded the smallest errors of refractivity parameters, and root mean square errors of refractivity and propagation loss. These error reductions are attributed to avoidance of nonunique solutions that likely impact lower data densities, supported by their classification as a misleading or difficult inverse problem. Generally, data sourced from long range result in lower refractivity and propagation loss root mean square errors compared to data sourced from other domains.

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“…Several refractivity profile models have been proposed in the literature, each with different advantages and complexity [Karimian et al, 2011]. Recently, evaluation of evaporation duct refractivity models for inverse problem applications was carried out [Saeger et al, 2015], as well as a sensitivity analysis of the parameters describing the refractivity profile in order to ascertain model parameter relative importance within PE radar wave propagation models [Lentini and Hackett, 2015].…”

Section: Introductionmentioning

confidence: 99%

Duct heights inferred from radar sea clutter using proper orthogonal bases

Fountoulakis

Earls

2016

Radio Sci.

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Maritime electromagnetic (EM)‐based communication and detection systems are strongly influenced by meteorological conditions, as they can cause anomalous electromagnetic propagation within the surface layer. To predict the performance of such systems, detailed knowledge of the refractivity profile is required. In recent years, refractivity from clutter (RFC) methods has been developed to estimate this refractivity profile by measuring radar clutter return from the rough ocean surface. The current work proposes an RFC framework that utilizes a novel surrogate model for EM propagation. The surrogate model is based on an offline created library of sparsely sampled field data of clutter returns, compressed into proper orthogonal bases, and indexed on specific surface layer refractive parameters. By exploiting the Riemannian manifold structure of the space that proper orthogonal bases occur in, we are able to interpolate among them. This, then, enables us to use the surrogate model in an inverse problem setting, whose goal is to uncover in situ maritime EM propagation conditions efficiently. We demonstrate the feasibility of our proposed surrogate model‐based RFC approach for evaporation duct by testing it with field data obtained from an experimental campaign.

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Evaluation of simplified evaporation duct refractivity models for inversion problems

Cited by 22 publications

References 28 publications

Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

Rough Ocean Surface Effects on Evaporative Duct Atmospheric Refractivity Inversions Using Genetic Algorithms

Impact of Radar Data Sampling on the Accuracy of Atmospheric Refractivity Inversions Over Marine Surfaces

Duct heights inferred from radar sea clutter using proper orthogonal bases

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