by Siva Sai KumarRajana, T.S.Shrungeshwara, Chiranjeevi G.Vivek, Sampad Kumar Panda and Sridevi Jade
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 () 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 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.