Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets
Abstract One of the main goals when studying Space Weather is to characterize extreme events occurrences and related characteristics. To do so, dedicated statistical methods from the so‐called extreme value analysis (EVA) field have been developed. In this study we used Ca index, derived from aa, in...
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Wiley
2020-06-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002450 |
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| author | G. Bernoux V. Maget |
| author_facet | G. Bernoux V. Maget |
| author_sort | G. Bernoux |
| collection | DOAJ |
| description | Abstract One of the main goals when studying Space Weather is to characterize extreme events occurrences and related characteristics. To do so, dedicated statistical methods from the so‐called extreme value analysis (EVA) field have been developed. In this study we used Ca index, derived from aa, in order to characterize geoeffectiveness from the radiation belts point of view with a 150‐year‐long data set. The analysis performed in this study thus focuses on this newsworthy index to provide clues on the reliability of EVA methods. The first main result we present here is that the 1‐in‐10‐, 1‐in‐50‐, and 1‐in‐100‐year events, respectively, match Ca values of 100.39, 131.39, and 142.84 nT. Consequently, the only 1‐in‐100 event observed during the Space Era would be the “Halloween Storm” in 2003 that reached a Ca value of 147.6 nT. The second main result highlighted in this work is that performing the same analysis with shorter subsets (20 years long) can give significantly different results for two reasons. The first reason is that some short time periods do not display the same distribution of events as the full period. The second reason is that the choice of the correct threshold (when using a Peaks Over Threshold approach) is made difficult with a short data set and leads to inaccurate results. This is a strong result as for accurate estimation of the induced effects of extreme events in radiation belts, we may only rely on short flux data sets from one or another mission (mostly shorter than 20 years). |
| format | Article |
| id | doaj-art-7caca1b7e2764f4dbb6188569b625a5a |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-7caca1b7e2764f4dbb6188569b625a5a2025-01-14T16:30:43ZengWileySpace Weather1542-73902020-06-01186n/an/a10.1029/2020SW002450Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data SetsG. Bernoux0V. Maget1ONERA/DPHY Université de Toulouse Toulouse FranceONERA/DPHY Université de Toulouse Toulouse FranceAbstract One of the main goals when studying Space Weather is to characterize extreme events occurrences and related characteristics. To do so, dedicated statistical methods from the so‐called extreme value analysis (EVA) field have been developed. In this study we used Ca index, derived from aa, in order to characterize geoeffectiveness from the radiation belts point of view with a 150‐year‐long data set. The analysis performed in this study thus focuses on this newsworthy index to provide clues on the reliability of EVA methods. The first main result we present here is that the 1‐in‐10‐, 1‐in‐50‐, and 1‐in‐100‐year events, respectively, match Ca values of 100.39, 131.39, and 142.84 nT. Consequently, the only 1‐in‐100 event observed during the Space Era would be the “Halloween Storm” in 2003 that reached a Ca value of 147.6 nT. The second main result highlighted in this work is that performing the same analysis with shorter subsets (20 years long) can give significantly different results for two reasons. The first reason is that some short time periods do not display the same distribution of events as the full period. The second reason is that the choice of the correct threshold (when using a Peaks Over Threshold approach) is made difficult with a short data set and leads to inaccurate results. This is a strong result as for accurate estimation of the induced effects of extreme events in radiation belts, we may only rely on short flux data sets from one or another mission (mostly shorter than 20 years).https://doi.org/10.1029/2020SW002450Space Weatherextreme value analysisradiation beltsCa indexextreme events |
| spellingShingle | G. Bernoux V. Maget Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets Space Weather Space Weather extreme value analysis radiation belts Ca index extreme events |
| title | Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets |
| title_full | Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets |
| title_fullStr | Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets |
| title_full_unstemmed | Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets |
| title_short | Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets |
| title_sort | characterizing extreme geomagnetic storms using extreme value analysis a discussion on the representativeness of short data sets |
| topic | Space Weather extreme value analysis radiation belts Ca index extreme events |
| url | https://doi.org/10.1029/2020SW002450 |
| work_keys_str_mv | AT gbernoux characterizingextremegeomagneticstormsusingextremevalueanalysisadiscussionontherepresentativenessofshortdatasets AT vmaget characterizingextremegeomagneticstormsusingextremevalueanalysisadiscussionontherepresentativenessofshortdatasets |