In order to improve the accuracy of the International Reference Ionosphere (IRI)-2016 model for application in the Antarctic region, total electron content (TEC) data from the Global Navigation Satellite Systems (GNSS) observation data in 2018 are assimilated into the IRI-2016 model by updating the effective ionospheric parameter, IG12 index on a daily basis. The functional relationship between the IG12 index and the longitude, latitude, and the day of year (DOY) is fitted by using the spherical crown harmonic function and the polynomial, and finally establish an updated experiential model of IG12 indices over the Antarctic region. Conclusions that were reached were: 1) the updated IG12 index varies greatly over different geographical locations and 2) it is also apparent that the accuracy of the IRI-2016 model is worse in the perpetual night than that in the perpetual day. In order to verify our method, the TEC calculated by the IRI-2016 model driven by the updated IG12 index and that calculated by the original IRI-2016 model are compared with the GNSS-TEC, and the results show that the updated IRI-2016 model has improved the accuracy of the BIAS and root mean square (RMS) of the TEC calculation by 97% and 87%, respectively, on the fitting moments, while 75% and 54% on the predicting moments. In addition, compared with the original IRI-2016 model, it is found that the updated IRI-2016 model improves the accuracy of the NmF2 calculation by approximately 23% on average for the fitting time and 8% for the predicting time.

中文翻译：

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通过IRI2016与GNSS TEC同化更新南极地区IG钪佲倐指数实验模型


为了提高国际参考电离层（IRI）-2016模型在南极地区应用的准确性，将2018年全球导航卫星系统（GNSS）观测数据的总电子含量（TEC）数据同化到IRI-2016模型中。 2016年模型通过每天更新有效电离层参数IG12指数。利用球冠调和函数和多项式拟合IG12指数与经度、纬度和年份（DOY）之间的函数关系，最终建立南极地区IG12指数更新的经验模型。得出的结论是：1）更新后的 IG12 指数在不同地理位置上差异很大，2）IRI-2016 模型在永久夜晚的准确度显然比永久白天的要差。为了验证我们的方法，将更新后的IG12指数驱动的IRI-2016模型计算出的TEC与原IRI-2016模型计算出的TEC与GNSS-TEC进行了比较，结果表明更新后的IRI-2016模型计算出的TEC该模型将拟合矩的 TEC 计算的 BIAS 和均方根 (RMS) 精度分别提高了 97% 和 87%，而在预测矩上分别提高了 75% 和 54%。此外，与原始IRI-2016模型相比，更新后的IRI-2016模型的NmF2计算精度在拟合时间上平均提高了约23%，在预测时间上平均提高了8%。