The current study aims at investigating and identifying the ionospheric effects of the geomagnetic storm that occurred during 17–19 March 2015. Incidentally, with SYM‐H hitting a minimum of −232 nT, this was the strongest storm of the current solar cycle 24. The study investigates how the storm has affected the equatorial, low‐latitude, and midlatitude ionosphere in the American and the European sectors using available ground‐based ionosonde and GPS TEC (total electron content) data. The possible effects of prompt electric field penetration is observed in both sectors during the main phase of the storm. In the American sector, the coexistence of both positive and negative ionospheric storm phases are observed at low latitudes and midlatitudes to high latitudes, respectively. The positive storm phase is mainly due to the prompt penetration electric fields. The negative storm phase in the midlatitude region is a combined effect of disturbance dynamo electric fields, the equatorward shift of the midlatitude density trough, and the equatorward compression of the plasmapause in combination with chemical compositional changes. Strong negative ionospheric storm phase is observed in both ionosonde and TEC observations during the recovery phase which also shows a strong hemispherical asymmetry. Additionally, the variation of equatorial ionization anomaly as seen through the SWARM constellation plasma measurements across different longitudes has been discussed. We, also, take a look at the performance of the IRI Real‐Time Assimilative Mapping during this storm as an ionospheric space weather tool.

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The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived Vertical Total Electron Content (VTEC) from International GNSS Service (IGS), magnetic field data, geomagnetic indices, Global Ionospheric Maps (GIMs), Thermospheric Mass Density (TMD), and [O/N2] ratio measurement. Strong ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration, during the storm recovery phase and following after. First, daytime VTEC (9:00-20:00 UT) presented variations of > 15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00-24:00 and 00:00-05:00 UT) VTEC presented low values (58 TECU) in the recovery phase. Second, the ionospheric values during the storm main phase and following after, at low-and mid-latitudes, caused the Equatorial Ionization Anomaly (EIA) to expand due to Prompt Penetration Electric Field (PPEF). Furthermore, VTEC enhancements are likely to occur few hours after the SSC of 25 August 2018, while enhancements of Thermospheric Mass Density (TMD) and [O/N2] ratio started to appear later on 26 and 27 of August 2018.

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The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.

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