Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models
Abstract An assessment of the risk of extreme geomagnetic storms is critically important for modern society. However, current methods mainly focus on using stationary statistical models to analyze extreme geomagnetic events. These models ignore the non‐stationary nature of the data, caused by effect...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2025SW004404 |
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| author | Ting Wang David Fletcher Matthew Parry Craig J. Rodger Andy W. Smith Tanja Petersen |
| author_facet | Ting Wang David Fletcher Matthew Parry Craig J. Rodger Andy W. Smith Tanja Petersen |
| author_sort | Ting Wang |
| collection | DOAJ |
| description | Abstract An assessment of the risk of extreme geomagnetic storms is critically important for modern society. However, current methods mainly focus on using stationary statistical models to analyze extreme geomagnetic events. These models ignore the non‐stationary nature of the data, caused by effects of the solar cycle and the seasons, and thus could provide unreliable estimates of return levels. We propose use of hidden Markov models and generalized additive models, both involving time‐varying parameters, in order to capture these features of the data. We use these models to analyze extreme values of the magnitude of the derivative in the horizontal component R1(t) of geomagnetic observations from the Eyrewell geomagnetic observatory in New Zealand. We use residual diagnostics to check for lack‐of‐fit of the models, demonstrate that they can successfully model the effects of both the solar cycle and the seasons, and use the best‐fitting models to provide more reliable estimates of return levels. From our analysis, the 50‐year and 100‐year conditional return levels of the extreme magnitude of the derivative in the horizontal component R1(t) at the Eyrewell magnetic observatory are likely to be within the ranges 500–2,600 and 700–4,500 nT/min respectively at solar maximum. |
| format | Article |
| id | doaj-art-d1608541082d496fb635fc0b2ec3b59c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-d1608541082d496fb635fc0b2ec3b59c2025-08-20T03:34:53ZengWileySpace Weather1542-73902025-07-01237n/an/a10.1029/2025SW004404Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical ModelsTing Wang0David Fletcher1Matthew Parry2Craig J. Rodger3Andy W. Smith4Tanja Petersen5Department of Mathematics and Statistics University of Otago Dunedin New ZealandDavid Fletcher Consulting Limited Karitane New ZealandDepartment of Mathematics and Statistics University of Otago Dunedin New ZealandDepartment of Physics University of Otago Dunedin New ZealandDepartment of Mathematics, Physics and Electrical Engineering Northumbria University Newcastle upon Tyne UKGNS Science Lower Hutt New ZealandAbstract An assessment of the risk of extreme geomagnetic storms is critically important for modern society. However, current methods mainly focus on using stationary statistical models to analyze extreme geomagnetic events. These models ignore the non‐stationary nature of the data, caused by effects of the solar cycle and the seasons, and thus could provide unreliable estimates of return levels. We propose use of hidden Markov models and generalized additive models, both involving time‐varying parameters, in order to capture these features of the data. We use these models to analyze extreme values of the magnitude of the derivative in the horizontal component R1(t) of geomagnetic observations from the Eyrewell geomagnetic observatory in New Zealand. We use residual diagnostics to check for lack‐of‐fit of the models, demonstrate that they can successfully model the effects of both the solar cycle and the seasons, and use the best‐fitting models to provide more reliable estimates of return levels. From our analysis, the 50‐year and 100‐year conditional return levels of the extreme magnitude of the derivative in the horizontal component R1(t) at the Eyrewell magnetic observatory are likely to be within the ranges 500–2,600 and 700–4,500 nT/min respectively at solar maximum.https://doi.org/10.1029/2025SW004404 |
| spellingShingle | Ting Wang David Fletcher Matthew Parry Craig J. Rodger Andy W. Smith Tanja Petersen Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models Space Weather |
| title | Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models |
| title_full | Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models |
| title_fullStr | Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models |
| title_full_unstemmed | Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models |
| title_short | Better Forecasting of Extreme Geomagnetic Storms Using Non‐Stationary Statistical Models |
| title_sort | better forecasting of extreme geomagnetic storms using non stationary statistical models |
| url | https://doi.org/10.1029/2025SW004404 |
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