Development of algorithms and software for forecasting, nowcasting and variability of TEC

Tulunay, E
Senalp, ET
Cander, LR
Tulunay, YK
Bilge, AH
Mizrahi, E
Kouris, SS
Jakowski, N
Total Electron Content (TEC) is an important characteristic of the ionosphere relevant to communications. Unpredictable variability of the ionospheric parameters due to various disturbances limits the efficiencies of communications, radar and navigation systems. Therefore forecasting and nowcasting of TEC are important in the planning and operation of Earth-space and satellite-to-satellite communication systems. Near-Earth space processes are complex being highly nonlinear and time varying with random variations in parameters where mathematical modeling is extremely difficult if not impossible. Therefore data driven models such as Neural Network (NN) based models are considered and found promising in modeling such processes. In this paper the NN based METU-NN model is introduced to forecast TEC values for the intervals ranging from 1 to 24 h in advance. Forecast and nowcast of TEC values are also considered based on TEC database. Day-to-day and hour-to-hour variability of TEC are also estimated using statistical methods. Another statistical approach based on the clustering technique is developed and a preprocessing approach is demonstrated for the forecast of ionospheric critical frequency foF2.


Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance
Tulunay, E; Ozkaptan, C; Tulunay, Yurdanur (2000-01-01)
Radio waves of a wide range of frequencies from very low frequency (VLF) to high frequency (HF), (broadly 3 to 30 MHz) can be propagated to great distances via the ionosphere.
Measurement of the Isolated Prompt Photon Production Cross Section in pp Collisions at root s=7 TeV
Khachatryan, V.; et. al. (2011-02-01)
The differential cross section for the inclusive production of isolated prompt photons has been measured as a function of the photon transverse energy E-T(Gamma) in pp collisions at root s 7 TeV using data recorded by the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 2.9 pb(-1). Photons are required to have a pseudorapidity vertical bar eta(gamma)vertical bar 21 GeV, covering the kinematic region 0.006 < x(T) < 0.086. The measured cross section is found to be in agreem...
Preparation and thermal characterization of poly(2-vinylpyridine) copolymers coordinated to Cr nanoparticles
Öztürk, Yurdagül; Kayran, Ceyhan; Hacaloğlu, Jale (2015-06-01)
In this study, polystyrene-block-poly(2-vinylpyridine), PS-b-P2VP, polyisoprene-block-poly(2-vinylpyridne), PI-b-P2VP and poly(methyl metacrylate)-block-poly(2-vinylpyridine), PMMA-b-P2VP, coordinated to Cr metal were synthesized and characterized by Fourier transform infrared, transmission electron microscopy and direct pyrolysis mass spectrometry techniques. Both thermal degradation mechanism and thermal stability of P2VP blocks were affected by the coordination of Cr nanoparticles to nitrogen of pyridine...
Development of artificial neural network based design tool for aircraft engine bolted flange connection subject to combined axial and moment load
Sanlı, Tahir Volkan; Kayran, Altan; Department of Aerospace Engineering (2018)
In this thesis, a design tool using artificial neural network (ANN) is developed for the bolted flange connections, which enables the user to analyze typical aircraft engine connections subjected to combined axial and bending moment in a fast yet very accurate way. The neural network trained for the design tool uses the database generated by numerous finite element analyses for different combinations of parametric design variables of the bolted flange connection. The defined parameters are the number of bol...
Artificial-neural-network prediction of hexagonal lattice parameters for non-stoichiometric apatites
Kockan, Umit; Ozturk, Fahrettin; Evis, Zafer (2014-01-01)
In this study, hexagonal lattice parameters (a and c) and unit-cell volumes of non-stoichiometric apatites of M-10(TO4)(6)X-2 are predicted from their ionic radii with artificial neural networks. A multilayer-perceptron network is used for training. The results indicate that the Bayesian regularization method with four neurons in the hidden layer with a tansig activation function and one neuron in the output layer with a purelin function gives the best results. It is found that the errors for the predicted ...
Citation Formats
E. Tulunay et al., “Development of algorithms and software for forecasting, nowcasting and variability of TEC,” ANNALS OF GEOPHYSICS, pp. 1201–1214, 2004, Accessed: 00, 2020. [Online]. Available: