Potential refractivity as a similarity variable

Cook, J. W.; Burk, Stephen D.

doi:10.1007/bf00120756

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…As a point of clarification, the vertical M profile is not uniquely defined by h d in most MO models including those of Liu et al (1979) and Frederickson et al (2000). Several MO evaporation duct models exists (Cook and Burk 1992;Babin et al 1997;Frederickson et al 2000;Frederickson 2010). We choose NAVSLaM (Frederickson et al 2000;Frederickson 2010) to compute the vertical atmospheric and refractivity profiles in an evaporation duct from bulk parameters (air temperature, humidity, and pressure at a certain height above the sea surface).…”

Section: Dependence Of Refractivity Index On Atmospheric Parametersmentioning

confidence: 99%

Toward the Assimilation of the Atmospheric Surface Layer Using Numerical Weather Prediction and Radar Clutter Observations

Karimian¹,

Yardim²,

Haack³

et al. 2013

Journal of Applied Meteorology and Climatology

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Radio wave propagation on low-altitude paths over the ocean above 2 GHz is significantly affected by negative refractivity gradients in the atmospheric surface layer, which form what is often referred to as an evaporation duct (ED). Refractivity from clutter (RFC) is an inversion approach for the estimation of the refractivity profile from radar clutter, and RFC-ED refers to its implementation for the case of evaporation ducts. An approach for fusing RFC-ED output with evaporation duct characterization that is based on ensemble forecasts from a numerical weather prediction (NWP) model is examined here. Three conditions of air–sea temperature difference (ASTD) are examined. Synthetic radar clutter observations are generated using the Advanced Propagation Model. The impacts of ASTD on the evaporation duct refractivity profile, atmospheric parameter inversion, and propagation factor distributions are studied. Relative humidity at a reference height and ASTD are identified as state variables. Probability densities from NWP ensembles, RFC-ED, and joint inversions are compared. It is demonstrated that characterization of the near-surface atmosphere by combining RFC-ED and NWP reduces the estimation uncertainty of ASTD and relative humidity in an evaporation duct, with respect to using either method alone.

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Section: Dependence Of Refractivity Index On Atmospheric Parametersmentioning

confidence: 99%

Toward the Assimilation of the Atmospheric Surface Layer Using Numerical Weather Prediction and Radar Clutter Observations

Karimian¹,

Yardim²,

Haack³

et al. 2013

Journal of Applied Meteorology and Climatology

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“…For electromagnetic propagation purposes, the environment can be uniquely represented by the range and height‐dependent index of refraction, which itself is a complex function of temperature, humidity, and pressure [ Cook and Burk , 1992]. Therefore the term environmental parameters will be used exclusively for the M profile parameters henceforth.…”

Section: Model Formulationmentioning

confidence: 99%

Statistical maritime radar duct estimation using hybrid genetic algorithm–Markov chain Monte Carlo method

2007

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[1] This paper addresses the problem of estimating the lower atmospheric refractivity (M profile) under nonstandard propagation conditions frequently encountered in lowaltitude maritime radar applications. This is done by statistically estimating the duct strength (range-and height-dependent atmospheric index of refraction) from the sea surface reflected radar clutter. These environmental statistics can then be used to predict the radar performance. In previous work, genetic algorithms (GA) and Markov chain Monte Carlo (MCMC) samplers were used to calculate the atmospheric refractivity from returned radar clutter. Although GA is fast and estimates the maximum a posteriori (MAP) solution well, it poorly calculates the multidimensional integrals required to obtain the means, variances, and underlying posterior probability distribution functions of the estimated parameters. More accurate distributions and integral calculations can be obtained using MCMC samplers, such as the Metropolis-Hastings and Gibbs sampling (GS) algorithms. Their drawback is that they require a large number of samples relative to the global optimization techniques such as GA and become impractical with an increasing number of unknowns. A hybrid GA-MCMC method based on the nearest neighborhood algorithm is implemented in this paper. It is an improved GA method which improves integral calculation accuracy through hybridization with a MCMC sampler. Since the number of forward models is determined by GA, it requires fewer forward model samples than a MCMC, enabling inversion of atmospheric models with a larger number of unknowns.Citation: Yardim, C., P. Gerstoft, and W. S. Hodgkiss (2007), Statistical maritime radar duct estimation using hybrid genetic algorithm -Markov chain Monte Carlo method, Radio Sci., 42, RS3014,

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“…A large number of relevant researchers have been engaged in the research of evaporation duct model [14]. Many prediction models have been proposed, including Liu-Katsaros-Businger (LKB) model [15], Paulus-Jeske (PJ) model [16], Naval Research Laboratory (NRL) model [17], [18], Musson-Gauthier-Bruth (MGB) model [19], Babin-Young-Carton (BYC) model [20], Naval-Postgraduate-School (NPS) model [21] and CORE 3.0 algorithm model [22], as well as domestic pseudo-refractivity model [23], Universal Evaporation Duct (UED) model [24] and non-iterative flux algorithm evaporation duct prediction model [25]. Due to the complexity of the local geographic and hydrometeorological environment, the practicability of the theoretical prediction model at sea based on the Monin-Obkhov similarity theory of homogeneous and steady atmospheric boundary layer usually has some limitations and large prediction errors.…”

Section: Introductionmentioning

confidence: 99%

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

Zhao¹,

Li²,

Zhao³

et al. 2020

RADIOENGINEERING

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Evaporation duct is a specific atmospheric structure at sea, which has an important influence on the propagation path of electromagnetic waves (EW). Considering the limit of existing evaporation duct height (EDH) prediction models and aiming at proposing more accurate and stronger generalization ability of EDH models, we applied eXtreme Gradient Boosting (XGBoosting) algorithm to the field of evaporation duct for the first time. And we proposed the new EDH prediction model using XGBoost algorithm (XGB model). Simultaneously, traditional Paulus-Jeske (PJ) model and deep learning Multilayer Perceptron (MLP) model were introduced into the experiment to make a comparison. In terms of comprehensive performance, XGB model is optimal in all sub-regions and total area. Finally, cross-learning experiments were carried out to test the generalization ability of XGB model. The results show that the generalization ability of XGB model is better than that of MLP model.

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Potential refractivity as a similarity variable

Cited by 12 publications

References 11 publications

Toward the Assimilation of the Atmospheric Surface Layer Using Numerical Weather Prediction and Radar Clutter Observations

Toward the Assimilation of the Atmospheric Surface Layer Using Numerical Weather Prediction and Radar Clutter Observations

Statistical maritime radar duct estimation using hybrid genetic algorithm–Markov chain Monte Carlo method

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

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