Modelling radio refractive index in the atmospheric surface layer

Salamon, Steve J.; Hansen, Hedley J.; Abbott, Derek

doi:10.1049/el.2015.0195

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…A common feature of these studies is the assumption that a linear positive refractivity gradient is the likely worst-case of a subrefractive atmosphere. Boundary layer similarity theory [17] indicates that surface refractivity gradients are likely to be stronger near the surface than at greater heights, and a subrefractive profile of this non-linear form has been shown to be capable of greater attenuation on a terrestrial path than a linear gradient with the same increase in refractivity over the lowest 100 m of the atmosphere [18].…”

Section: B Median Depression Fadingmentioning

confidence: 99%

Universal Kriging Prediction of Line-of-Sight Microwave Fading

2020

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Prediction of the severity of multipath fading is fundamental to the design of point-to-point terrestrial fixed microwave links at frequencies below 10 GHz, but error in this prediction may be significant in countries such as Australia, not represented in the dataset used to generate existing empirical models. We take advantage of recently collected worst-month fading data from Australia, and find new parameters particularly useful in predicting the severe fading experienced in Northern Australia. These parameters are from very irregularly spaced weather stations, so we investigate various interpolation techniques for this situation, including a new version of natural neighbour interpolation. Conventional multipath prediction models are based on ordinary least squares (OLS) regression, but we refine this, taking spatial correlation into account with generalised least squares (GLS) regression. We then demonstrate further improvement in regions well populated by measured data, by employing universal kriging.

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Section: B Median Depression Fadingmentioning

confidence: 99%

Universal Kriging Prediction of Line-of-Sight Microwave Fading

2020

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“…The dominant clear-air characteristics of the atmosphere affecting radio propagation are vertical moisture gradients and temperature gradients, as they produce vertical refractive index gradients, which cause the predominantly horizontally propagating radio signals to curve up or down. It is important to not only know the mean gradient over the surface layer; gradient variations within the surface layer may have a significant effect [22].…”

Section: Examples Of Gls With Regular and Irregularly Sampled Datamentioning

confidence: 99%

How real are observed trends in small correlated datasets?

2019

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The eye may perceive a significant trend in plotted time-series data, but if the model errors of nearby data points are correlated, the trend may be an illusion. We examine generalized least-squares (GLS) estimation, finding that error correlation may be underestimated in highly correlated small datasets by conventional techniques. This risks indicating a significant trend when there is none. A new correlation estimate based on the Durbin–Watson statistic is developed, leading to an improved estimate of autoregression with highly correlated data, thus reducing this risk. These techniques are generalized to randomly located data points in space, through the new concept of the nearest new neighbour path. We describe tests on the validity of the GLS schemes, allowing verification of the models employed. Examples illustrating our method include a 40-year record of atmospheric carbon dioxide, and Antarctic ice core data. While more conservative than existing techniques, our new GLS estimate finds a statistically significant increase in background carbon dioxide concentration, with an accelerating trend. We conclude with an example of a worldwide empirical climate model for radio propagation studies, to illustrate dealing with spatial correlation in unevenly distributed data points over the surface of the Earth. The method is generally applicable, not only to climate-related data, but to many other kinds of problems (e.g. biological, medical and geological data), where there are unequally (or randomly) spaced observations in temporally or spatially distributed datasets.

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“…Simplified models based on a mass of historical detecting data have been studied extensively, for example, linear model, exponential model, double exponential model, Hopfield model, and subsection model [1][2][3]. A retrieval algorithm using ground-based global positioning system (GPS) is developed to establish the atmospheric refractivity profile model [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

Ma¹,

Liu²,

Zhang³

2019

PIER M

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A method based on the radial basis function neural network (RBFNN) is developed to fast establish the tropospheric refractivity profile model. Parameters of the RBFNN include SPREAD, and the number of training samples is optimized. The actual measured data of meteorological station at Qingdao city in China are used as test data to evaluate the performance of RBFNN. The simulation results show that the root mean squared error (RMSE) has a minimum of 0.81 when SPREAD is 8.1. The simulated values agree well with the test data which is observed by using the sounding balloon method. Finally, the tropospheric refractivity profile model of a selected area is established by using two different simulation methods. This paper attempts to propose a method to fast establish the tropospheric refractivity profile model which provides an available method to correct the atmospheric refraction error in radar applications.

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Modelling radio refractive index in the atmospheric surface layer

Cited by 8 publications

References 7 publications

Universal Kriging Prediction of Line-of-Sight Microwave Fading

Universal Kriging Prediction of Line-of-Sight Microwave Fading

How real are observed trends in small correlated datasets?

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

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