A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning
Abstract Widely used geomagnetic activity indices like Kp or Dst, derived from the combined data from several observatories distributed worldwide, are crucial to forecasting since solar‐driven geomagnetic activity can significantly affect technology and human activities on Earth and in near‐Earth sp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2025GL114848 |
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| author | Guram Kervalishvili Ingo Michaelis Monika Korte Jan Rauberg Jürgen Matzka |
| author_facet | Guram Kervalishvili Ingo Michaelis Monika Korte Jan Rauberg Jürgen Matzka |
| author_sort | Guram Kervalishvili |
| collection | DOAJ |
| description | Abstract Widely used geomagnetic activity indices like Kp or Dst, derived from the combined data from several observatories distributed worldwide, are crucial to forecasting since solar‐driven geomagnetic activity can significantly affect technology and human activities on Earth and in near‐Earth space. We developed a new model to forecast geomagnetic indices by incorporating predicted data from individual observatories. Unlike previous models that rely solely on an index and overlook local physical effects, our approach accounts for each observatory separately in the forecasting process, allowing for index predictions that integrate the same physical principles as in the original calculations of the index. We demonstrate the model's performance for Kp and the newer Hpo indices (Hp60 and Hp30), which measure planetary disturbances with higher resolution than Kp and without its upper limit of 9. The model demonstrates good agreement, accurately capturing trends and overall behavior, even with sparse solar wind data. |
| format | Article |
| id | doaj-art-fa427d3c952f4a1abc5d082e5333f9de |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-fa427d3c952f4a1abc5d082e5333f9de2025-08-20T02:56:35ZengWileyGeophysical Research Letters0094-82761944-80072025-04-01528n/an/a10.1029/2025GL114848A Novel Model for Forecasting Geomagnetic Indices Using Machine LearningGuram Kervalishvili0Ingo Michaelis1Monika Korte2Jan Rauberg3Jürgen Matzka4GFZ Helmholtz Centre for Geosciences Potsdam GermanyGFZ Helmholtz Centre for Geosciences Potsdam GermanyGFZ Helmholtz Centre for Geosciences Potsdam GermanyGFZ Helmholtz Centre for Geosciences Potsdam GermanyGFZ Helmholtz Centre for Geosciences Potsdam GermanyAbstract Widely used geomagnetic activity indices like Kp or Dst, derived from the combined data from several observatories distributed worldwide, are crucial to forecasting since solar‐driven geomagnetic activity can significantly affect technology and human activities on Earth and in near‐Earth space. We developed a new model to forecast geomagnetic indices by incorporating predicted data from individual observatories. Unlike previous models that rely solely on an index and overlook local physical effects, our approach accounts for each observatory separately in the forecasting process, allowing for index predictions that integrate the same physical principles as in the original calculations of the index. We demonstrate the model's performance for Kp and the newer Hpo indices (Hp60 and Hp30), which measure planetary disturbances with higher resolution than Kp and without its upper limit of 9. The model demonstrates good agreement, accurately capturing trends and overall behavior, even with sparse solar wind data.https://doi.org/10.1029/2025GL114848Kp indexHpo indicesHp30 indexsolar windforecastgeomagnetic activity |
| spellingShingle | Guram Kervalishvili Ingo Michaelis Monika Korte Jan Rauberg Jürgen Matzka A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning Geophysical Research Letters Kp index Hpo indices Hp30 index solar wind forecast geomagnetic activity |
| title | A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning |
| title_full | A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning |
| title_fullStr | A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning |
| title_full_unstemmed | A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning |
| title_short | A Novel Model for Forecasting Geomagnetic Indices Using Machine Learning |
| title_sort | novel model for forecasting geomagnetic indices using machine learning |
| topic | Kp index Hpo indices Hp30 index solar wind forecast geomagnetic activity |
| url | https://doi.org/10.1029/2025GL114848 |
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