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Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks
Space Weather ( IF 4.288 ) Pub Date : 2020-04-27 , DOI: 10.1029/2019sw002411
Eun‐Young Ji 1 , Yong‐Jae Moon 1, 2 , Eunsu Park 2
Affiliation  

In this study, we make a model, which is called DeepIRI, to generate improved International Reference Ionosphere (IRI) total electron content (TEC) maps by deep learning based on conditional Generative Adversarial Networks. For this we consider 48,901 pairs of IRI TEC maps and International Global Navigation Satellite Systems (GNSS) Service (IGS) TEC maps from 2001 to 2011 for training the model. We evaluate the model by comparing IGS TEC maps and DeepIRI TEC ones from 2013 to 2017. The DeepIRI TEC maps that our model generated are much more consistent with the corresponding IGS TEC maps than the IRI TEC ones. Especially, ionospheric peak structures are successfully generated in DeepIRI TEC maps while they are not in IRI‐2016 ones. From the average differences between IRI and IGS TEC maps, our model greatly improved the IRI TEC at low‐latitude region around the equatorial anomaly. These results show that our model can improve the global TEC prediction ability of the IRI‐2016. Our study suggests a sufficient possibility to generate DeepIRI global TEC maps in near real time if IRI is generated in time. Our approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay.

中文翻译:

基于条件生成对抗网络的深度学习对IRI Global TEC地图的改进

在这项研究中,我们建立了一个名为DeepIRI的模型,通过基于条件生成对抗网络的深度学习来生成改进的国际参考电离层(IRI)总电子含量(TEC)图。为此,我们考虑从2001年到2011年对48,901对IRI TEC地图和国际全球导航卫星系统(GNSS)服务(IGS)TEC地图进行训练。我们通过比较2013年至2017年的IGS TEC贴图和DeepIRI TEC贴图来评估模型。我们的模型生成的DeepIRI TEC贴图比IRI TEC贴图与相应的IGS TEC贴图更加一致。特别是,在DeepIRI TEC映射中成功生成了电离层峰结构,而在IRI-2016中则没有。根据IRI和IGS TEC地图之间的平均差异,我们的模型极大地改善了赤道异常周围低纬度地区的IRI TEC。这些结果表明,我们的模型可以提高IRI-2016的全球TEC预测能力。我们的研究表明,如果及时生成IRI,则有足够的可能性近乎实时地生成DeepIRI全球TEC图。如果可以得到更现实的观察结果并且有时间延迟,我们的方法可以用于改进经验模型的版本。
更新日期:2020-04-27
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