A. L. Morozova, T. Barata, T. Barlyaeva, R. Gafeira
Abstract
A regression-based model was previously developed to forecast total electron content (TEC) at middle latitudes. We present a more sophisticated model using neural networks (NN) instead of linear regression. This regional model prototype simulates and forecasts TEC variations in relation to space weather conditions. The development of a prototype consisted of the selection of the best set of predictors, NN architecture, and the length of the input series. Tests made using the data from December 2014 to June 2018 show that the PCA-NN model based on a simple feed-forward NN with a very limited number (up to six) of space weather predictors performs better than the PCA-MRM model that uses up to 27 space weather predictors. The prototype is developed on a TEC series obtained from a GNSS receiver at Lisbon airport and tested on TEC series from three other locations at middle latitudes of the Eastern North Atlantic. Conclusions on the dependence of the forecast quality on longitude and latitude are made.
Keywords
ionosphere; total electron content prediction; middle latitudes; neural networks; space weather
Atmosphere
Volume 14, Number 7
2023 July
