Predicting Equatorial Ionospheric Convective Instability Using Machine Learning
Abstract The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F (ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irreg...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
|
| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023SW003505 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536439791124480 |
|---|---|
| author | D. Garcia E. L. Rojas D. L. Hysell |
| author_facet | D. Garcia E. L. Rojas D. L. Hysell |
| author_sort | D. Garcia |
| collection | DOAJ |
| description | Abstract The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F (ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%. |
| format | Article |
| id | doaj-art-cee66584a35940569474526833f143ae |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-cee66584a35940569474526833f143ae2025-01-14T16:30:45ZengWileySpace Weather1542-73902023-12-012112n/an/a10.1029/2023SW003505Predicting Equatorial Ionospheric Convective Instability Using Machine LearningD. Garcia0E. L. Rojas1D. L. Hysell2Electrical and Computer Engineering Cornell University Ithaca NY USAEarth and Atmospheric Sciences Cornell University Ithaca NY USAEarth and Atmospheric Sciences Cornell University Ithaca NY USAAbstract The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread‐F (ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first‐principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%.https://doi.org/10.1029/2023SW003505machine learningequatorial spread Fforecastingneural networksrandom forestsionospheric irregularities |
| spellingShingle | D. Garcia E. L. Rojas D. L. Hysell Predicting Equatorial Ionospheric Convective Instability Using Machine Learning Space Weather machine learning equatorial spread F forecasting neural networks random forests ionospheric irregularities |
| title | Predicting Equatorial Ionospheric Convective Instability Using Machine Learning |
| title_full | Predicting Equatorial Ionospheric Convective Instability Using Machine Learning |
| title_fullStr | Predicting Equatorial Ionospheric Convective Instability Using Machine Learning |
| title_full_unstemmed | Predicting Equatorial Ionospheric Convective Instability Using Machine Learning |
| title_short | Predicting Equatorial Ionospheric Convective Instability Using Machine Learning |
| title_sort | predicting equatorial ionospheric convective instability using machine learning |
| topic | machine learning equatorial spread F forecasting neural networks random forests ionospheric irregularities |
| url | https://doi.org/10.1029/2023SW003505 |
| work_keys_str_mv | AT dgarcia predictingequatorialionosphericconvectiveinstabilityusingmachinelearning AT elrojas predictingequatorialionosphericconvectiveinstabilityusingmachinelearning AT dlhysell predictingequatorialionosphericconvectiveinstabilityusingmachinelearning |