LAUSR

The study aims to take an advantage of the systematic combination of statistical procedures namely Principal Component analysis (PCA), Linear Model to develop a local climatological model for ionospheric Total Electron Content (TEC) irregularities. Further, Auto Regressive Moving Average (ARMA) model, Neural Network (NN) model are used to forecast the ionospheric irregularities over Bengaluru region, India. Bengaluru International GNSS Service (IGS) station dataset (geographic lat.- 13.02°N, long.77.57°E; geomagnetic latitude: 4.4°N) of an 8-year period has been used to implement the proposed algorithm. Retrieval of the main components of the times series using Principal Component Analysis, identifying and analyzing the trends and cycles in the PCA residual to model the un-modelled irregularities using Linear model and finally fitting the residual series using ARMA/NN models to forecast the irregularity es has delivered successful results. The ionospheric TEC irregularities, measured in TECU/hour, are investigated during the 24th solar cycle ascending and descending phases. The MAE and RMSE values are used for the validation of the proposed models in forecasting the ionospheric TEC irregularities during both geomagnetic quiet and storm days (12–14 October 2016). It is observed that MAE value of NN model is 0.5 TECU/hour during geomagnetic quiet period whereas it is 0.98 TECU/hour during geomagnetic disturbed period. Moreover, the RMSE value of ARMA model is 0.73 TECU/hour and 0.67 TECU/hour for NN model which reveals that NN model is comparatively good in forecasting the ionospheric TEC irregularities during geomagnetic quiet period. The proposed model can be useful to develop an ionospheric irregularity climatological tool for low latitude regions.