Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network

• Published in: Space Weather 2015-08, Vol.13 (8), p.446-457
• Main Authors: de Lima, G. R. T., Stephany, S., de Paula, E. R., Batista, I. S., Abdu, M. A.
• Format: Article
• Language: English
• Subjects: Architecture; artificial neural network; Artificial neural networks; Bubbles; Correlation analysis; Electron density; Equator; Equatorial ionization anomaly; Heuristic; Instability; Ionization; Ionosphere; Ionospheric plasma; Ionospheric plasma bubbles; ionospheric scintillation; Ionospherics; Latitude; Lead time; Magnetic equator; Magnetohydrodynamic stability; Neural networks; Plasma bubbles; Plasma instabilities; Plasmas (physics); prediction of occurrence of scintillation; Preprocessing; Radio signals; Resampling; Scintillation; Stations; Training
• ISSN: 1542-7390; 1539-4964; 1542-7390
• DOI: 10.1002/2015SW001182

Electron density irregularity structures, often associated with ionospheric plasma bubbles, drive amplitude and phase fluctuations in radio signals that, in turn, create a phenomenon known as ionospheric scintillation. The phenomenon occurs frequently around the magnetic equator where plasma instability mechanisms generate postsunset plasma bubbles and density depletions. A previous correlation study suggested that scintillation at the magnetic equator may provide a forecast of subsequent scintillation at the equatorial ionization anomaly southern peak. In this work, it is proposed to predict the level of scintillation over São Luís (2.52°S, 44.3°W; dip latitude: ~2.5°S) near the magnetic equator with lead time of hours but without specifying the moment at which the scintillation starts or ends. A collection of extended databases relating scintillation to ionospheric variables for São Luís is employed to perform the training of an artificial neural network with a new architecture. Two classes are considered, not strong (null/weak/moderate) and strong scintillation. An innovative scheme preprocesses the data taking into account similarities of the values of the variables for the same class. A formerly proposed resampling heuristic is employed to provide a balanced number of tuples of each class in the training set. Tests were performed showing that the proposed neural network is able to predict the level of scintillation over the station on the evening ahead of the data sample considered between 17:30 and 19:00 LT. Key Points Intended to predict scintillation in the same station with antecedence of hours Employs an artificial neural network to predict the level of scintillation Introduces an innovative preprocessing for the neural network data training