The capability of IRI-2016 in reproducing the hemispheric asymmetry, the winter and semiannual anomalies has been assessed over the equatorial ionization anomaly (EIA) during quiet periods of years 2013–2014. The EIA reconstructed using Total Electron Content (TEC) derived from Global Navigation Satellite System was compared with that computed using IRI-2016 along longitude 25° − 40^oE. These were analyzed along with hemispheric changes in the neutral wind derived from the horizontal wind model and the TIMED GUVI columnar O/N₂ data. IRI-2016 clearly captured the hemispheric asymmetry of the anomaly during all seasons albeit with some discrepancies in the magnitude and location of the crests. The winter anomaly in TEC which corresponded with greater O/N₂ in the winter hemisphere was also predicted by IRI-2016 during December solstice. The model also captured the semiannual anomaly with stronger crests in the northern hemisphere. Furthermore, it reproduced the variation trend of the asymmetry index (A) in December solstice and equinox during noon. However, in June solstice the model failed to capture the winter anomaly and misrepresented the variation of A. This was linked with its inability to accurately predict the pattern of the neutral wind, the maximum height of the F2 layer and the changes in O/N₂ in both hemispheres. The difference between the variations of EUV and F10.7 fluxes was also a potential source of errors in IRI-2016. The results highlight the significance of the inclusion of wind data in IRI-2016 in order to enhance its performance over East Africa.

IRI-2016 再现半球不对称、冬季和半年异常的能力已通过赤道电离异常 (EIA) 在 2013-2014 年的平静时期进行评估。使用来自全球导航卫星系统的总电子含量 (TEC) 重建的 EIA 与使用 IRI-2016 沿经度 25° - 40 ^o E计算的 EIA 进行了比较。 这些与来自水平风的中性风的半球变化一起进行了分析模型和 TIMED GUVI 柱状 O/N ₂数据。IRI-2016 清楚地捕捉到了所有季节异常的半球不对称性，尽管波峰的大小和位置存在一些差异。TEC的冬季异常对应于较大的O/N ₂IRI-2016 在 12 月至日也预测了冬季半球。该模型还捕获了北半球具有更强波峰的半年异常。此外，它再现了十二月至日和正午分时不对称指数（A）的变化趋势。然而，在六月至日，该模型未能捕捉到冬季异常，并错误地反映了 A 的变化。 这与其无法准确预测中性风的模式、F2 层的最大高度和 O/N 的变化有关。₂在两个半球。EUV 和 F10.7 通量变化之间的差异也是 IRI-2016 中潜在的错误来源。结果强调了在 IRI-2016 中包含风数据的重要性，以提高其在东非的表现。