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We investigated the ionosphere response to the two severe geomagnetic storms in the ascending phase of solar cycle 25 which occurred during the 23-24 March 2023 (SYM-Hmin = −169 nT) and 23-24 April 2023 (SYM-Hmin = −233 nT) using a latitudinally aligned dense network of Global Navigation Satellite System (GNSS) receivers, magnetometers, and digisonde along the Indian longitude sector. The significant variations in TEC during the storm's main and recovery phases are mainly linked to the influence of westward Disturbance Dynamo Electric Fields (DDEFs). During the initial phase of the March 23-24 geomagnetic storm, no changes in daytime TEC were observed, despite the storm occurring at noon time with a southward IMF Bz due to the influence of electron density in the top side ionosphere. Furthermore, both pre-reversal enhancement (PRE) and ionosphere irregularities are suppressed within two hours of their onset during the main phase of the March 23-24 storm, owing to the westward transition of zonal electric fields from an eastward direction. During the recovery phase of both storms, a daytime positive storm effect is observed over the dip equatorial region, while the beyond equatorial ionization anomaly (EIA) and mid-latitude regions perceived a negative ionospheric storm effect. This phenomenon is attributed to the influence of the dominant westward DDEFs during the period. Moreover, these DDEFs effectively inhibited the equinoctial manifestation of PRE effects and post-sunset ionospheric irregularities during the recovery phase of both storms. These findings are further confirmed with supporting information from TEC recorded by Swarm satellites, model-derived ionospheric electric fields, and thermospheric O/N2. The results from this study may advance the understanding of ionospheric response to severe geomagnetic storms under the prevailing westward DDEFs during the dayside recovery phase, complementing the global efforts for more reliable space weather modeling and prediction services. [ABSTRACT FROM AUTHOR]

We present multi-scale variability of GPS-derived column integrated precipitable water vapour (PWV) estimated over five continuous GPS sites of northeast India from 2004 to 2012. PWV is estimated from GPS-derived zenith total delay using observed surface pressure and temperature from collocated meteorological sensors as well as obtained by interpolating European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis project (ERA-Interim) global reanalysis dataset. PWV estimated using ERA-Interim-derived parameters compare well with the PWV estimated using observed meteorological parameters with bias of less than ± 0.1 mm and highest root mean square error of 0.56 mm. The average PWV for the study period is about 17 mm at Bomdila in the Eastern Himalayas, about 20 mm at Shillong in Shillong plateau, about 31 mm at Lumami in Arokan-Yoma Hill ranges, and about 43 mm at Guwahati and Tezpur in Assam valley. The high altitude sites show low annual PWV variability (around 49%) than the low altitude sites (around 63-67%). Seasonal PWV value coincides with the monsoon with maximum in summer and minimum in the winter. However, percentage seasonal PWV variability is found to be almost same (around 68%) for all the five sites. The Assam valley sites do not show a distinct diurnal cycle whereas the high altitude sites indicate a distinct diurnal cycle coinciding with the daily solar cycle. Insights in to GPS PWV variability and rainfall are presented for the study period. [ABSTRACT FROM AUTHOR]

• Performance of IRI-2016 model is probed through geodetic GPS TEC during 2002–2019. • Large discrepancy is observed in high solar activity phases in both solar cycles. • IRI model fails to manifest the strength of ionization around EIA crest regions. • Differences are observed more during day time hours in all the phases of solar activity. • Winter anomaly is not reflected in IRI outputs during high solar activity years. We evaluate the performance of the latest version of International Reference Ionosphere (IRI-2016) by comparing the estimated Total Electron Content (TEC) with the observed values from four geodetic Global Positioning System (GPS) receivers, latitudinally aligned from the equator to low latitudes (−5° to 20° Geomagnetic) in the Indian longitudes (75–95°) from 2002 to 2019. It is observed that IRI-2016 model underestimates and overestimates the GPS TEC depending on the season, solar activity, and geographic location of observation. On a decadal scale, the monthly mean of IRI TEC and GPS TEC show distinct seasonal variation trends for all years with seasonal asymmetry. The bias and RMSE values are low in the ascending and descending phases of solar cycles 23 and 24 compared to high values with significant fluctuations observed during the peak solar activity phase. For the declining phase of solar cycle 24, IRI TEC underestimates the GPS TEC values at EIA crest regions. On an annual scale, IRI TEC agrees better with GPS TEC during the low solar activity years (2010 and 2019) than the high solar activity years (2002 and 2014). Moreover, IRI model is unable to manifest the winter anomaly characteristics during the high solar activity years. On a diurnal scale, the performance of IRI model is poor with high RMSE during daytime hours and it underestimates the GPS TEC at equatorial and low latitude regions during the high solar activity phases. On a seasonal scale, high bias and RMSE are observed during the spring equinox compared to other seasons under the high solar activity phases. On a spatial scale, the bias and RMSE are high with low yearly coefficient of determination ( R 2) in the EIA crest regions as compared to the equatorial and low latitude regions. High underestimation of IRI model was also observed in November 2011 due to high solar indices. The proportionality relationship between RMSE and solar indices is observed in all the phases of solar activity. Additionally, the mean annual RMSE and R 2 values indicate solar activity predominantly affecting the performance of IRI model. This study suggests that the influence of solar and magnetic indices inputs in the present IRI model could be revisited for reflecting the solar activity effects distinctly in the model outputs. In addition, IRI-2016 model requires further improvements for equatorial, low latitude, and EIA crest regions of the Indian sub-continent, specifically during the high solar activity phases. [ABSTRACT FROM AUTHOR]