Tropospheric Ducting Research Articles

Prediction of Tropospheric Ducting for UHF Signals In Plateau State Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

A study on the occurrence of tropospheric ducting for UHF signal in Plateau State using ANFIS had been investigated using the variation of the meteorological data; temperature, pressure, and relative humidity collected from NIMET for Jos, Plateau State Nigeria. The refractivity gradient was calculated using MATLAB as computational software. The conditions of occurrence of path clearance and tropospheric ducting were utilized to checkmate the occurrence of ducting over the period. The data gotten is used to train an ANFIS model, taking 70% of the data for model training, 20% for testing and 10% for validation. The integrity of the ANFIS model was tested using root mean square error (RMSE) and maximum absolute percentage error (MAPE) which confirmed that the model is accurate to 99% considered fit for forecasting. The developed model was able to mimic the tropospheric ducting trend up to a period of 7 years (2019- 2025) after successful simulation with an assertion that the rainy months are more prevailed to chances of tropospheric ducting occurrence as higher humidity prevails during this time which has more significance on refractivity gradient. An investigation was carried out on the effect of variation of tropospheric parameters; temperature, pressure and humidity on refractivity gradient using linear regression and Spearman correlation to ascertained the degree of dependence of each parameter and the result showed that refractivity gradient is strongly influenced by relative humidity with a correlation coefficient of -0.9772 as against temperature and pressure having correlation coefficients of -0.2587 and 0.2866 respectively.

Finite-frequency modeling of regional tropospheric infrasound using realistic atmospheres and terrain.

Infrasonic waves have been observed to propagate to regional (greater than 15 km) distances through the troposphere. Infrasound propagation in the geometric acoustics approximation has shown that realistic terrain can scatter acoustic energy from tropospheric ducts; however, ray methods cannot intrinsically capture finite-frequency behavior such as diffraction. A two-dimensional finite-difference time-domain (FDTD) method has been developed to solve linearized equations for infrasound propagation with realistic terrain. Acoustic wave propagation over 100 km with both flat terrain and a Gaussian hill was first simulated in order to compare finite-frequency propagation with ray predictions. The effects of realistic terrain and atmospheres on infrasound signals from a 2012 surface explosion at the Utah Testing and Training Range are then investigated. Propagation through the troposphere is suggested by array processing results, but eigenrays are not predicted due to weak to nonexistent ducting conditions. FDTD modeling suggests that the inclusion of terrain and finite frequency effects helps explain much of the observed signal in a realistic scenario. These results suggest that geometric acoustics may underestimate propagation through the troposphere, and that recorded waveforms at regional distances may be noticeably affected by terrain.

Prediction of Tropospheric Ducting for UHF Signals In Plateau State Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

Prediction of Tropospheric Ducting for UHF Signals In Plateau State Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

The Influence of PE Initial Field Construction Method on Radio Wave Propagation Loss and Tropospheric Duct Inversion

Parabolic equations (PE) are commonly employed for calculating the spatial propagation loss of wireless signals. The initial field is a crucial factor. To investigate the impact of construction accuracy on the calculation of radio wave propagation loss, we selected the half-wave dipole antenna and its Gaussian approximation to examine the influence of wide-angle PE modeling. We analyzed the disparities between the actual antenna pattern and the Gaussian beam approximation, as well as the discrepancies in the corresponding initial field and the calculation of radio wave propagation loss in PE modeling. The simulation results indicate that the error of the Gaussian approximation increases as the angle of departure from the antenna main beam increases, with a relative error of approximately 30% in the initial field. A comparison between the experimental test of the broadcast signal and the simulation calculation reveals that the model based on the actual antenna aligns more closely with the measured value on a flat underlying surface. However, in mountainous areas with significant fluctuations, the simulation results are consistent with each other and higher than the measured value. The inversion results obtained through the particle swarm optimization algorithm demonstrate that the model based on the actual antenna exhibits superior inversion accuracy for the tropospheric atmospheric duct structure.

Revisiting Enhanced AIS Detection Range under Anomalous Propagation Conditions

Two clear-air over-the-horizon propagation mechanisms affecting the Automatic Identification System (AIS) detection range are considered. Comparison results are presented between the path loss due to tropospheric ducting and path loss due to tropospheric scattering (troposcatter) for the AIS frequencies. The calculations are based on the well-known parabolic equation approximation to the wave equation, in which a simple troposcatter formula is incorporated. In most studied cases, the ducting ensures a significantly greater reduction in path loss than troposcatter even when the AIS frequencies are not well trapped in the duct. Emphasis is placed on the elevated trapping layers, and some features that may make ducting propagation less favorable in terms of increasing the AIS detection range are discussed.

Tropospheric Duct Presence and their Effects on Communications Signals in Abidjan, Douala and Libreville

Tropospheric Duct Presence and their Effects on Communications Signals in Abidjan, Douala and Libreville

Multiscale Decomposition Prediction of Propagation Loss for EM Waves in Marine Evaporation Duct Using Deep Learning

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods.

Atmospheric Ducting Interference on DAB, DAB+ Radio in Eastern Mediterranean

A serious problem affecting the local radio industry in Cyprus is radio interference from the Middle East. This problem is especially acute on the highway along the coast during the summer months because interference degrades the sound quality in vehicle receivers due to chirping noise, hissing, fading and distortion. During the last decade the issue was explored by monitoring the field strength intensity of unwanted signals with respect to atmospheric conditions that excacerbate interference. According to the research outcomes presented in this paper, severe interference occurs due to “Tropospheric Ducting”, i.e., radio energy becomes trapped between two boundaries in the lower layers of Earth’s atmosphere. Thereby, this phenomenon acts as a waveguide that favors radio waves to propagate beyond the horizon with very strong field strength intensity where under certain atmospheric conditions exceeding the expected free space theoretical value. Because the commercial Band FM is already oversaturated for years, it was considered important to expand this research to explore the impact of interference on the new digital DAB, DAB+ radio, that will soon be launched in Cyprus, as it is discussed below.

Tropospheric ducting maps

Tropospheric ducting maps

An Accurate Maritime Radio Propagation Loss Prediction Approach Employing Neural Networks

The radio propagation loss prediction model is essential for maritime communication. The oceanic tropospheric duct is much more complicated than the atmospheric structure on land due to the rough sea surface influence, and it leads to difficulties in loss prediction. Classical radio wave propagation loss prediction models are either based on complicated electromagnetic wave theories or rely on empirical data. Consequently, they suffer from low accuracy and a limited range of application. To address this issue, a novel maritime propagation loss prediction approach is proposed, which fully exploits the data for training. In this new approach, 3D sea surface contour profiles are generated based on the Pierson–Moskowitz spectrum theory and the direction expansion formula Stereo Wave Observation Program (SWOP), by sweeping the parameters. The full-wave propagation procedure of radio signals over the sea surface is simulated by commercial EM analysis software CST Studio Suite. Based on the large quantity of simulated data, the BP neural network is employed to fit the radio propagation loss and obtain the sea surface radio wave propagation prediction model. Other classical machine learning methods are also compared to validate the proposed approach. Traditional empirical model construction relies on observation data. This approach, for the first time, proposed an automatic scheme which covers the whole procedure from the data generation to prediction model training. It avoids the requirements for on-site observation, as well as significantly decreases the cost of experiments. The application scope of the prediction model such as propagation distance range and working frequency range could be adaptive through adjustments for simulated sea size and simulated working frequency. This approach is validated to save 99% of prediction time in comparison with the full-wave simulations. The prediction model trained via our proposed method could obtain the coefficient of determination R2 which is over 0.92, demonstrating the superiority of this method.

Using Multi-Source Real Landform Data to Predict and Analyze Intercity Remote Interference of 5G Communication with Ducting and Troposcatter Effects

At present, 5G base stations are densely distributed in major cities based on improving user concentrations and the large demand for services in urban hotspot areas. Moreover, 5G communication requires more accurate communication propagation loss (PL) monitoring. Low-build areas, such as suburbs and rural areas, are prone to forming relatively stable tropospheric ducts, which can bend the signal to the surface in the duct-trapping layer for multiple reflections. Due to the random flow of the atmospheric air mass, each reflection of the communication signal is re-scattered in the troposphere through the top of the duct layer, thereby expanding the propagation range of the signal and changing the expected effect of radio wave propagation. If ducting and troposcatter effects happen in the 5G base station antenna layer, co-channel interference (CCI) could occur, affecting the quality of electromagnetic propagation. Urban links in the plain area have no major terrain obstacles, but ground fluctuations and land cover scattering have a greater impact on the signal scattering at the bottom of the duct. On the basis of the forward-propagation theory, this paper adds factors, such as ducting the forecast value using real weather parameters, terrain, and land cover-type distributions to evaluate the CCIs of over-the-horizon communications on the intercity link. Based on 1300 sets of randomly generated terrains and landforms, two deep learning (DL) models were used to predict the PL of over-the-horizon communications between cities in a land-based ducting environment. The accuracy of LSTM prediction could reach 98.4%. The verification of PL prediction using DL in this paper allows for quick and efficient prediction of PL in the land-based ducting of intercity links using land cover characteristics.

Joint Inversion of Evaporation Duct Based on Radar Sea Clutter and Target Echo Using Deep Learning

Tropospheric duct is an anomalous atmospheric phenomenon over the sea surface that seriously affects the normal operation and performance evaluation of electromagnetic communication equipment at sea. Therefore, achieving precise sensing of tropospheric duct is of profound significance for the propagation of electromagnetic signals. The approach of inverting atmospheric refractivity from easily measurable radar sea clutter is also known as the refractivity from clutter (RFC) technique. However, inversion precision of the conventional RFC technique is low in the low-altitude evaporation duct environment. Due to the weak attenuation of the over-the-horizon target signal as it passes through the tropospheric duct, its strength is much stronger than that of sea clutter. Therefore, this study proposes a new method for the joint inversion of evaporation duct height (EDH) based on sea clutter and target echo by combining deep learning. By testing the inversion performance and noise immunity of the new joint inversion method, the experimental results show that the mean error RMSE and MAE of the new method proposed in this paper are reduced by 41.2% and 40.3%, respectively, compared with the conventional method in the EDH range from 0 to 40 m. In particular, the RMSE and MAE in the EDH range from 0 to 16.7 m are reduced by 54.2% and 56.4%, respectively, compared with the conventional method. It shows that the target signal is more sensitive to the lower evaporation duct, which obviously enhances the inversion precision of the lower evaporation duct and has effectively improved the weak practicality of the conventional RFC technique.

Inversion for Inhomogeneous Surface Duct without a Base Layer Based on Ocean-Scattered Low-Elevation BDS Signals

The anomalous propagation conditions, particularly the tropospheric ducts, severely impact the regular operation and performance evaluation of radio devices in the atmospheric boundary layer. Therefore, it is necessary to provide the regional distribution of tropospheric ducts for utilizing or avoiding these abnormal propagation phenomena. As significant uncooperative signal sources, the global navigation satellite systems (GNSS) have been widely applied in the remote sensing of the ocean and atmosphere due to the greater convenience and lower cost. With the completed deployment of the BeiDou Navigation Satellite System (BDS) in 2020, an additional source can be chosen in the relevant studies. Taking the BDS as an example, since the scattered signals from the ocean surface at low satellite elevation angles can be effectively trapped by tropospheric ducts, we propose a method to invert for the regional distribution of tropospheric ducts using the received power of ocean-scattered signals in this paper. Firstly, the propagation model was built to calculate the received power, and a suite of simulations was made in various atmospheric environments. The results suggested that the received power is more sensitive to the surface duct without a base layer. Then, we made a preliminary estimation of the tropospheric ducts on the ocean nearby Qingdao utilizing the Weather Research and Forecasting (WRF) model as well as the echo data measured by a Doppler weather radar. Before the inversion, the actual satellite azimuth and elevation angles should be obtained to evaluate the bistatic scattering coefficients and the received powers of the selected satellite signals. Finally, we presented an inversion example using the proposed method. In absence of the actual measurements, the received powers pre-estimated at different SNRs served as the inputs of the inversion process and the estimated duct parameters were used to verify the validity of the proposed inversion method. For both the received power and modified refractivity profile, the fitness between the values pre-estimated using the estimated duct parameters and calculated by the inverted duct parameters gets better as the elevation angle decreases and the SNR increases. The variation of the fitness between the estimated and inverted values is slightly different for each duct parameter. Moreover, the calculation of inversion errors further explained the above behaviors, including the mean absolute error (MAE) and the root mean square error (RMSE). Despite some certain errors, the inversion results maintain the overall tendencies and most characteristics of the estimated values, thus proving the validity of the inversion method.

Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts

The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index. This structure severely affects the remote sensing detection capability of electromagnetic systems designed for an environment with normal atmospheric refraction. The propagation loss of electromagnetic waves in the oceanic duct is an important concept in oceanic duct research. Owing to the long-term stability and short-term irregular changes in marine environmental parameters, the propagation loss in oceanic ducts has nonstationary and multiscale time characteristics. In this paper, we propose a multiscale decomposition prediction method for predicting the propagation loss in oceanic tropospheric ducts. The prediction performance was verified by simulating propagation loss data with noise. Using different evaluation criteria, the experimental results indicated that the proposed method outperforms six other comparison methods. Under noisy conditions, ensemble empirical mode decomposition effectively disassembles the original propagation loss into a limited number of stable sequences with different scale characteristics. Accordingly, predictive modeling was conducted based on multiscale propagation loss characteristic sequences. Finally, we reconstructed the predicted result to obtain the predicted value of the propagation loss in the oceanic duct. Additionally, a genetic algorithm was used to improve the generalization ability of the proposed method while avoiding the nonlinear predictor from falling into a local optimum.

Estimation of co-channel interference between cities caused by ducting and turbulence

With the rapid development of the fifth-generation (5G) mobile communication technology, the application of each frequency band has reached the extreme, causing mutual interference between different modules. Hence, there is a requirement for detecting filtering and preventing interference. In the troposphere, over-the-horizon propagation occurs in atmospheric ducts and turbulent media. The effects of both ducting and turbulence can increase the probability of occurrence of long-distance co-channel interference (CCI), in turn, severely affecting the key performance indicators such as system access, handover and drop. In the 5G era, to ensure communication channels and information security, CCI must be reduced. This paper introduces a scattering parabolic equation algorithm for calculating signal propagation in atmospheric ducts on irregular terrain boundaries. It combines Hitney’s radio physical optical model and Wagner’s nonuniform turbulent scattering model for calculating the tropospheric scattering in an evaporation duct or a surface-based duct. The new model proposes a tropospheric scattering parabolic equation algorithm for various tropospheric duct environments. Finally, as a specific case, the topographical boundaries between several cities in the East China Plain were considered, and the over-the-horizon propagation loss was simulated for various ducting and turbulent environments. The simulation results were used to evaluate whether CCI would occur between cities in a specific environment.

Inversion Method of Regional Range-Dependent Surface Ducts with a Base Layer by Doppler Weather Radar Echoes Based on WRF Model

Ground clutter caused by variations of atmospheric refraction environment can occur when the weather radar is observing precipitation systems, especially in the presence of a tropospheric duct. Therefore, the acquisition of duct parameters is very important for evaluating radar performance and improving data quality. Based on the measured echo data of a Doppler weather radar located at Qingdao and the numerical simulation results of modified refractivity profiles from the Weather Research and Forecasting (WRF) model, an inversion method for regional range-dependent tropospheric duct parameters over the sea area is proposed in this paper. Due to the higher antenna height of up to 169 m, the transmission environment is assumed to be a surface duct with a base layer for locating the antenna in the trapping layer. The Principal Component Analysis (PCA) and Parabolic Equation (PE) methods were used to characterize the horizontal inhomogeneity of duct parameters and the propagation of electromagnetic waves in the tropospheric duct. In the inversion model, duct parameters extracted from WRF outputs were used as the initial values. Additionally, multithread parallel processing was adopted in order to reduce the inversion time based on the characteristics of the optimization algorithm. The overall variation tendencies of the WRF simulation results in the regional distribution of duct parameters were well consistent with the inversion results, but were relatively lower in terms of specific values. Due to the influence of precipitation targets on measured echo data, the inversed echo data had different agreements with the measurements in space, and the absolute error values were less than 5 dB in about 90% of the region of interest.

Features of Propagation Characteristics of Ultrahigh Frequency Radio Waves in an Above-Water Duct as Functions of the Duct Parameters and Wavelength

A new numerical method for finding roots of the characteristic equation in problems of radio wave propagation in tropospheric ducts with a piecewise linear profile of the refractive index is proposed. For the selected model of the vertical profile of the refractive index of an surface duct, dynamics of propagation constants of modes and features of the behavior of the attenuation function of the electromagnetic field as functions of the troposphere parameters and the wavelength are studied. The contribution of the whispering gallery modes is also estimated.

Application of an updated atmospheric model to explore volcano infrasound propagation and detection in Alaska

Winds and temperature gradients greatly affect the long-range propagation of infrasound. The spatio-temporal variability of these parameters must therefore be accurately characterized to correctly interpret recorded infrasound at long distances, specifically to differentiate between source and propagation effects. Here we present the first results of an open source reanalysis model, termed Alaska Volcano Observatory Ground-to-Space (AVO-G2S), constructed to accurately characterize the atmosphere and model long-range infrasound propagation from volcanic eruptions in Alaska. We select a number of case studies to examine recent eruptions of Alaskan volcanoes whose ash emissions posed a threat to air traffic, including the two most recent eruptions of Pavlof Volcano and two typical explosions from Cleveland Volcano. Strong tropospheric ducting and low noise at the station during the 21 July 2015 explosion of Cleveland Volcano led to an automated detection of the explosion at an infrasound array 992 km away, whereas low signal-to-noise ratio for the 6 November 2014 Cleveland Volcano explosion helps explain the non-detection in real-time of a predicted strong stratospheric arrival. For the November 2014 Pavlof eruption, discrepancies between local seismic data and a distal infrasound array 460 km away cannot be solely explained by changes in atmospheric conditions, though some features of the complex propagation predictions follow the trends in long-range infrasound signals. The most recent eruption of Pavlof Volcano in March 2016 shows minimal changes in propagation conditions throughout the eruption and therefore indicates that the signals detected at long-range primarily reflect source processes. These results show how detailed examination of the acoustic propagation conditions provides insight into detection capability and eruption dynamics. Future work will implement AVO-G2S and high-resolution long-range infrasound propagation modeling in real-time for Alaskan volcanoes of interest.

Infrasound propagation in tropospheric ducts and acoustic shadow zones.

Numerical computations of the Navier-Stokes equations governing acoustic propagation are performed to investigate infrasound propagation in the troposphere and into acoustic shadow zones. An existing nonlinear finite-difference, time-domain (FDTD) solver that constrains input sound speed models to be axisymmetric is expanded to allow for advection and rigid, stair-step topography. The FDTD solver permits realistic computations along a given azimuth. It is applied to several environmental models to examine the effects of nonlinearity, topography, advection, and two-dimensional (2D) variations in wind and sound speeds on the penetration of infrasound into shadow zones. Synthesized waveforms are compared to a recording of a rocket motor fuel elimination event at the Utah Test and Training Range. Results show good agreement in the amplitude, duration, and spectra of synthesized and recorded waveforms for propagation through 2D atmospheric models whether or not topography, advection, or nonlinearity is explicitly included. However, infrasound propagation through a one-dimensional, range-averaged, atmospheric model yields waveforms with lower amplitudes and frequencies, suggesting that small-scale atmospheric variability causes significant scatter within the troposphere, leading to enhanced infrasound penetration into shadow zones. Thus, unresolved fine-scale atmospheric dynamics are not required to explain infrasound propagation into shadow zones.

PSO ALGORITHM OF RETRIEVING SURFACE DUCTS BY DOPPLER WEATHER RADAR ECHOES

Doppler weather radar is an effective tool for monitoring mesoscale and small scale weather systems, quantitatively estimating precipitation and guarding against severe convective weather. The quality of the data obtained by Doppler weather radar will be seriously affected by the anomalous propagation of electromagnetic wave in tropospheric ducts. A novel method is introduced in this paper to retrieve the surface ducts, and it is based on the Principal Component Analysis (PCA) method for modeling M profile and Parabolic Equation (PE) propagation model which is a well-established technique for efficiently solving the equations for beam propagation in an inhomogeneous atmosphere. The inversion echo powers and equivalent reflectivity factor are in accordance with the measured data, which indicates that the surface ducts can be effectively retrieved by this method.