Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years
Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms h...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/21/5418 |
| _version_ | 1827645625643040768 |
|---|---|
| author | Cheng Bi Peng Ren Ting Yin Zheng Xiang Yang Zhang |
| author_facet | Cheng Bi Peng Ren Ting Yin Zheng Xiang Yang Zhang |
| author_sort | Cheng Bi |
| collection | DOAJ |
| description | Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012). |
| first_indexed | 2024-03-09T18:41:49Z |
| format | Article |
| id | doaj.art-284bddd05cc6408aa427476c51e9e876 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:49Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-284bddd05cc6408aa427476c51e9e8762023-11-24T06:38:41ZengMDPI AGRemote Sensing2072-42922022-10-011421541810.3390/rs14215418Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity YearsCheng Bi0Peng Ren1Ting Yin2Zheng Xiang3Yang Zhang4School of Telecommunication Engineering, Xidian University, Xi’an 710071, ChinaSchool of Telecommunication Engineering, Xidian University, Xi’an 710071, ChinaBeijing Electronic Science & Technology Institute, Beijing 100070, ChinaSchool of Telecommunication Engineering, Xidian University, Xi’an 710071, ChinaSchool of Telecommunication Engineering, Xidian University, Xi’an 710071, ChinaPrediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012).https://www.mdpi.com/2072-4292/14/21/5418ionosphereF2 layerionosondesolar cycle-24space weatherdeep learning |
| spellingShingle | Cheng Bi Peng Ren Ting Yin Zheng Xiang Yang Zhang Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years Remote Sensing ionosphere F2 layer ionosonde solar cycle-24 space weather deep learning |
| title | Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years |
| title_full | Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years |
| title_fullStr | Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years |
| title_full_unstemmed | Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years |
| title_short | Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years |
| title_sort | modeling and forecasting ionospheric fof2 variation in the low latitude region during low and high solar activity years |
| topic | ionosphere F2 layer ionosonde solar cycle-24 space weather deep learning |
| url | https://www.mdpi.com/2072-4292/14/21/5418 |
| work_keys_str_mv | AT chengbi modelingandforecastingionosphericfof2variationinthelowlatituderegionduringlowandhighsolaractivityyears AT pengren modelingandforecastingionosphericfof2variationinthelowlatituderegionduringlowandhighsolaractivityyears AT tingyin modelingandforecastingionosphericfof2variationinthelowlatituderegionduringlowandhighsolaractivityyears AT zhengxiang modelingandforecastingionosphericfof2variationinthelowlatituderegionduringlowandhighsolaractivityyears AT yangzhang modelingandforecastingionosphericfof2variationinthelowlatituderegionduringlowandhighsolaractivityyears |