Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks. Issue 5 (27th April 2020)

• Record Type: Journal Article
• Authors: Ji, Eun‐Young; Moon, Yong‐Jae; Park, Eunsu
• Is Part Of: Space weather. Volume 18:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-27
• Subjects: Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2019SW002411
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: 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. Plain Language Summary: The International Reference Ionosphere (IRI) is one of the most widely used standard models of the ionosphere. The IRI total electron content (TEC) is very important to monitor ionospheric disturbances. This paper makes a model to improve IRI TEC maps by deep learning based on conditional Generative Adversarial Networks, which is a general purpose solution to resolve image‐to‐image translation problems. We consider many pairs of IRI TEC maps and IGS TEC maps for training the model. Our model greatly improves the global IRI TEC maps, which are very consistent with the observations. Key Points: We make a model to improve IRI TEC maps by deep learning based on conditional Generative Adversarial Networks (cGANs) The global TEC maps that our model generated are much more consistent with the IGS TEC maps than the IRI TEC maps Our approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay