Storm time IRI-Plas model forecast for an African equatorial station

Abstract

The limitation of ionospheric models in describing short-term ionospheric events has led to the development of data assimilative models e.g. the International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model . This paper compares the IRI-Plas derived total electron content (TEC), the peak height (hmF2) and critical frequency (foF2) of the F2-layer with those obtained from Global Positioning System (GPS) receiver's and Digisonde Precision Sounder (DPS-4) measurements over Ilorin (Geog. Lat. 8.50 o N; Long. 4.50 o E, dip: – 7.9 o ) during geomagnetic storm days. The model estimation was done by assimilation of Ionosonde foF2 and TEC derived from GPS (GPS-TEC) and Global Ionospheric Map (GIM-TEC) into the model code. In order to study the effect of data assimilation on the model's representation, the “no input” option of the model was used as reference. The result shows that with the exception of the foF2 assimilation mode, all the options generally reproduced TEC quite well for all the storm days considered. Overall, the model adjusted with GPS-TEC gives the best prediction of TEC as it reduced the prediction error of TEC by a multiple of up to three compared to using the GIM-TEC. Also, all the options failed to reproduce the storm induced prominent features in the storm-time features of foF2 and hmF2. In other word, assimilation with the TEC does not generally improve the storm-time predictions of foF2 and hmF2 at the station. Consequently, for storm-time estimation of the F2-layer peak parameters, the ‘no input’ representation of the model is more valid at this station.

Performance evaluation of IRI-Plas 2017 model with ionosonde data measurements of ionospheric parameters

“…The model has a high percentage deviation value of electron density measurements, mostly in between altitudes from 200 km to 450 km and during early nighttime and before midnight hours, with values of −21% and 8% for overestimation and underestimation, respectively. The investigations by Alçay et al [ 78 ] and Adebiyi et al [ 79 ] show a similar overestimation of the IRI Plas model using ionosonde data.…”

“…The ionospheric models primarily recreate ongoing ionospheric responses, and their reliance on real observations presents a difficulty in areas with a sparse and uneven distribution of ionosonde stations. The ionospheric models, on the other hand, provide averaged electron density measurements in both time and space, which may differ from the real values found using ionosondes [ 58 , 76 , 78 , 79 , 85 , 86 ]. The IRI-Plas 2017 model overestimates totally on June 17, 2014 at 14 UT ( Fig.…”

The Ionospheric Dynamics of the African Sector Responding to a Severe Geomagnetic Storm; the Storm of 3–5 November 2021

“…The output is two plots of uncalibrated and calibrated TEC against Universal time (UT) and *CMN files with parameters of modified Julian date, time, elevation and azimuth angles, geographical positions, derived vertical TEC, slant TEC, and scintillation index for each GNSS measurement. The VTEC estimates from the software have consistently yielded accurate and reliable results for studies of ionospheric variability in the low-latitude regions involving modeling and comparative analysis with ionospheric models (Adebiyi et al, 2019;Akala et al, 2013;Okoh et al, 2020). It should be noted that the parameter VTEC from this methodology was simply defined as TEC for this paper.…”

“…This is intended to minimize induced errors in positioning, navigation, and timing applications of GNSS. However, previous studies have shown that the models' performance tends to degrade mostly during geomagnetic storm periods in the region (Adebiyi et al, 2019 Timoçin et al, 2018;Vuković & Kos, 2017), and this presents additional challenges to global ionospheric modeling and forecasting. Therefore, this study explains spatial and temporal varying factors involved in determining how the African ionosphere responds to a storm.…”