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Response of data‐driven artificial neural network‐based TEC models to neutral wind for different locations, seasons, and solar activity levels from the Indian longitude sector
Author(s) -
Sur D.,
Haldar S.,
Ray S.,
Paul A.
Publication year - 2017
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1002/2016ja023678
Subject(s) - tec , total electron content , longitude , geographic coordinate system , latitude , international reference ionosphere , ionosphere , satellite , artificial neural network , global positioning system , environmental science , meteorology , geodesy , remote sensing , computer science , geography , geology , geophysics , artificial intelligence , physics , telecommunications , astronomy
The perturbations imposed on transionospheric signals by the ionosphere are a major concern for navigation. The dynamic nature of the ionosphere in the low‐latitude equatorial region and the Indian longitude sector has some specific characteristics such as sharp temporal and latitudinal variation of total electron content (TEC). TEC in the Indian longitude sector also undergoes seasonal variations. The large magnitude and sharp variation of TEC cause large and variable range errors for satellite‐based navigation system such as Global Positioning System (GPS) throughout the day. For accurate navigation using satellite‐based augmentation systems, proper prediction of TEC under certain geophysical conditions is necessary in the equatorial region. It has been reported in the literature that prediction accuracy of TEC has been improved using measured data‐driven artificial neural network (ANN)‐based vertical TEC (VTEC) models, compared to standard ionospheric models. A set of observations carried out in the Indian longitude sector have been reported in this paper in order to find the amount of improvement in performance accuracy of an ANN‐based VTEC model after incorporation of neutral wind as model input. The variations of this improvement in prediction accuracy with respect to latitude, longitude, season, and solar activity have also been reported in this paper.