Abstract-In this work, the parabolic equation applied on radiowave and microwave tropospheric propagation, properly manipulated, and resulting in a one-dimensional form, is solved using the Finite Element Method (FEM). The necessary vertical tropospheric profile characteristics are assigned to each mesh element, while the solution advances in small and constant range segments, each excited by the solution of the previous step. This is leading to a marching algorithm, similar to the widely used SplitStep formulation. The surface boundary conditions including the wave polarization and surface conductivity properties are directly applied to the FEM system of equations. Since the FEM system returns the total solution, a technique for the separation of the transmitted and reflected waves is also presented. This method is based on the application of the Discrete Fourier Transform (DFT) in the space domain, which allows for the separation of the existing wave components. Finally, abnormal tropospheric condition propagation is being employed to assess the method, while the results are compared to those obtained using the Advance Refractive Prediction System (AREPS v.3.03) software package.

Abstract. In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure, relative humidity and temperature. A feed forward backpropagation ANN is implemented, which is trained, validated and tested using atmospheric radiosonde data from the Helliniko airport, for the period from 1991 to 2004. The network's quality and generality is assessed using the Area Under the Receiver Operating Characteristics (ROC) Curves (AUC), which resulted to a mean value of about 0.86 to 0.90, depending on the observation time. In order to validate the ANN results and to evaluate any further improvement options of the proposed method, the problem was additionally treated using Least Squares Support Vector Machine (LS-SVM) classifiers, trained and tested with identical data sets for direct performance comparison with the ANN. Furthermore, time series prediction and the effect of surface wind to the presence of tropospheric ducts appearance are discussed. The results show that the ANN model presented here performs efficiently and gives successful tropospheric ducts predictions.

Abstract-In this work, the ray trajectory for oblique incidence is determined by solving the wave equation using the Finite Element Method (FEM). In case of ionospheric reflection the Discrete Fourier Transform (DFT), applied here in the space domain, is used to discriminate the incident and reflected waves.Ray tracing equations are combined with the Poynting vector components in order to calculate the wave trajectory taking into account the Earth's curvature. The results are illustrated for different frequencies and angles of incidence using electron density profiles obtained from the Rome digisonde station, topside profiles from the IRI-95 model and atmospheric data from the MSIS-E-90 model of NSSDC as an inputs.

SUMMARYThe variation of the refractivity profiles of the troposphere and especially of the ducting effect, affects the radio wave propagation causing various phenomena such as refraction, fading and interference between radio-stations. In this work, the tropospheric ducting phenomena over the Hellenic region are studied using data from Helleniko and Thessaloniki Airports for the time period from 1991 to 1999. The data are analysed, corrected and enhanced using interpolation techniques and after a final statistical process the ducting conditions over the Hellenic region are summarized.

The refractivity variations of the troposphere are responsible for various effects on radio wave propagation, such as refraction, bending, radio-station interference, etc. In this work, the refractivity variations of the Hellenic troposphere are studied using data from Helliniko airport of Athens/Greece. The data were analyzed using various interpolation procedures, i.e. in a day-by-day manner for temperature and relative humidity, to transform the data according to a reference height common for the whole dataset and finally for the refractivity N with respect to time, using piece-wise hermite interpolation polynomials for the low and medium altitudes and linear interpolation factors for the high altitudes. Since refractivity varies with time and height, two height independent basic parameters were computed and analyzed: the refractivity at station height N0 and scale height Hs. These parameters can be used to calculate the refractivity profile. Using statistical tools as the moving average, the β0 parameter and the monthly mean values, together with the corresponding standard deviations, useful results were obtained for the variations of the refractivity with respect to observation hour, height, season, month, day and level.