Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions
Abstract We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior wor...
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003501 |
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| author | S. Dutta M. B. Cohen |
| author_facet | S. Dutta M. B. Cohen |
| author_sort | S. Dutta |
| collection | DOAJ |
| description | Abstract We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 109 to 2.5 × 1010 e−/m3, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 109 e−/m3 at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM. |
| format | Article |
| id | doaj-art-b88ae504397e48468d425a9f3e970fdd |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-b88ae504397e48468d425a9f3e970fdd2025-01-14T16:30:45ZengWileySpace Weather1542-73902023-12-012112n/an/a10.1029/2023SW003501Topside Electron Density Modeling Using Neural Network and Empirical Model PredictionsS. Dutta0M. B. Cohen1School of Electrical and Computer Engineering Georgia Institution of Technology Atlanta GA USASchool of Electrical and Computer Engineering Georgia Institution of Technology Atlanta GA USAAbstract We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid‐latitude performance with absolute errors lower than the IRI by 2.5 × 109 to 2.5 × 1010 e−/m3, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 109 e−/m3 at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E‐CHAIM.https://doi.org/10.1029/2023SW003501topside ionosphereelectron density |
| spellingShingle | S. Dutta M. B. Cohen Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions Space Weather topside ionosphere electron density |
| title | Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions |
| title_full | Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions |
| title_fullStr | Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions |
| title_full_unstemmed | Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions |
| title_short | Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions |
| title_sort | topside electron density modeling using neural network and empirical model predictions |
| topic | topside ionosphere electron density |
| url | https://doi.org/10.1029/2023SW003501 |
| work_keys_str_mv | AT sdutta topsideelectrondensitymodelingusingneuralnetworkandempiricalmodelpredictions AT mbcohen topsideelectrondensitymodelingusingneuralnetworkandempiricalmodelpredictions |