Validating the IRI-2020 model for ionospheric storms over the North-East Asian sector induced by extreme geomagnetic storms

We validated and analyzed the effectiveness of the IRI-2020 model with MSIS2.1 code in depicting the ionosphereic responses to extreme geomagnetic storms in the Northeast Asian region. To conduct this analysis, we selected two significant ionospheric storm events that occurred after the commencement of the 25th solar cycle. For the positive ionospheric storm case, we focused on the G3-level geomagnetic storm event that took place on November 3–4, 2021. During this event, a significant positive ionospheric storm occurred over the low and middle latitudes of the Asian sector due to the simultaneous impact of strong positive storms induced by intense prompt penetration electric fields (PPEF). Regarding negative ionospheric storm cases, we chose the G2-level event occurring on April 14–15, 2022, during which strong negative ionospheric storms were observed not only over the Korean peninsula but also across Japan. To verify these ionospheric storm responses, we analyzed data from ionosonde and GNSS receivers installed in Korea and Japan, focusing on foF2, hmF2, and vertical total electron content (VTEC) values. We established the International 5 Quietest Days (IQDs) as a reference baseline. Our research indicates that although the IRI-2020 model did not precisely calculate the absolute changes in foF2 during positive storms in this region, it consistently estimated higher electron density compared to quiet days. Similarly, during negative storms, it consistently predicted lower electron density compared to quiet days, demonstrating alignment of the IRI-2020 model with the observed trend of evolution for contrasting ionospheric storms. Additionally, we examined the computational capabilities of the four topside profilers integrated into the IRI-2020 model and found that the IRI-cor2 option's profiler exhibited the most accurate performance in analyzing the two storm cases. Our study thoroughly elucidates these analytical processes and findings, delineating the strengths and weaknesses of the IRI-2020 model. Based on our findings, we propose potential avenues for improving the IRI model in the future.