J. R. K. Kumar Dabbakuti

TL;DR: In this article, a multivariate ionospheric TEC forecasting model based on linear time series model in combination with Autoregressive and Moving Average (ARMA) is proposed and implemented using Bengaluru International GNSS Service (IGS) station data (geographic lat. −13.02°N, long. 77.57°E; geomagnetic latitude: 4.4°N) during the period of 8 years (2009-2016) in the 24th solar cycle.

TL;DR: In this article, an empirical orthogonal function (EOF) model is constructed by using ground-based Global Navigational Satellite System (GNSS) TEC observation data at the Bangalore International GNSS Service (IGS) station (geographic 13.02° N, 77.57° E; geomagnetic latitude 4.4° N) during an extended period (2009-2016) in the 24th solar cycle.

TL;DR: In this article, Singular Spectrum Analysis (SSA) was used to extract information from the noisy time-series data with the first four SSA modes in the decomposition, almost converging to total variance of about 99%.

TL;DR: Preliminary results reveal that the refined Klobuchar model parameters impart enhanced ionospheric delay corrections to regional navigation satellite systems with single-frequency GPS receivers, such as the Indian Regional Navigation Satellite System.

TL;DR: In this article, the temporal ionospheric time delay variations were studied based on the empirical orthogonal function (EOF) analysis and wavelet transforms (WT) and the results reveal that the first few EOFs represented the majority of TEC variability pertaining to the physical processes of the ionosphere.