Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-05-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2019SW002411 |
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| author | Eun‐Young Ji Yong‐Jae Moon Eunsu Park |
| author_facet | Eun‐Young Ji Yong‐Jae Moon Eunsu Park |
| author_sort | Eun‐Young Ji |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7f5117eb6f2d4d469575467701df78a3 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-7f5117eb6f2d4d469575467701df78a32025-01-14T16:27:35ZengWileySpace Weather1542-73902020-05-01185n/an/a10.1029/2019SW002411Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial NetworksEun‐Young Ji0Yong‐Jae Moon1Eunsu Park2Department of Astronomy and Space Science, College of Applied Science Kyung Hee University Yongin South KoreaDepartment of Astronomy and Space Science, College of Applied Science Kyung Hee University Yongin South KoreaSchool of Space Research Kyung Hee University Yongin South KoreaAbstract 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.https://doi.org/10.1029/2019SW002411 |
| spellingShingle | Eun‐Young Ji Yong‐Jae Moon Eunsu Park Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks Space Weather |
| title | Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks |
| title_full | Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks |
| title_fullStr | Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks |
| title_full_unstemmed | Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks |
| title_short | Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks |
| title_sort | improvement of iri global tec maps by deep learning based on conditional generative adversarial networks |
| url | https://doi.org/10.1029/2019SW002411 |
| work_keys_str_mv | AT eunyoungji improvementofiriglobaltecmapsbydeeplearningbasedonconditionalgenerativeadversarialnetworks AT yongjaemoon improvementofiriglobaltecmapsbydeeplearningbasedonconditionalgenerativeadversarialnetworks AT eunsupark improvementofiriglobaltecmapsbydeeplearningbasedonconditionalgenerativeadversarialnetworks |