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Articles by L.A. McKinnell
Total Records ( 2 ) for L.A. McKinnell
  L.A. McKinnell and E.O. Oyeyemi
  A new neural network (NN) based global empirical model for the foF2 parameter, which represents the peak ionospheric electron density, has been developed using extended temporal and spatial geophysical relevant inputs. It has been proposed that this new model be considered as a suitable replacement for the International Union of Radio Science (URSI) and International Radio Consultative Committee (CCIR) model options currently used within the International Reference Ionosphere (IRI) model for the purpose of F2 peak electron density predictions. The most recent version of the model has incorporated data from 135 global ionospheric stations including a number of equatorial stations. This paper concentrates on the ability of this new model to predict foF2 for the equatorial sector, an area that has been identified as problematic within the current IRI peak prediction setup. The improvement in the predictions of the foF2 parameter by the new model as compared to the URSI and CCIR model options of the IRI is demonstrated and the requirement for additional foF2 data from the equatorial zone for the purpose of global modeling of foF2 is highlighted in this paper.
  L.A. McKinnell , M.W. Paradza , P.J. Cilliers , M.A. Abdu and J.R. de Souza
  The probability of occurrence of spread-F can be modeled and predicted using neural networks (NNs). This paper presents a feasibility study into the development of a NN based model for the prediction of the probability of occurrence of spread-F over selected equatorial stations within the Brazilian sector. The input space included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position. Twelve years of spread-F data from Brazil (covering the period 1978–1989) measured at the equatorial site Fortaleza (3.9°S, 38.45°W ) and low latitude site Cachoeira Paulista (22.6°S, 45.0°W ) are used in the development of an input space and NN architecture for the model. Spread-F data that is believed to be related to plasma bubble developments (range spread-F) was used in the development of the model. The model results show the probability of spread-F occurrence as a function of local time, season and latitude. Results from the Brazilian Sector NN (BSNN) based model are presented in this paper, as well as a comparative analysis with a Brazilian model developed for the same purpose.
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