Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases
Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst
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| creator | Reyes, Paula I. Pinto, Victor A. Moya, Pablo S. |
| description | Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst |
| doi_str_mv | 10.1029/2021SW002766 |
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Plain Language Summary
Geomagnetic storms are a common occurrence on Earth, and they can have significant impact on our lives. The occurrence of geomagnetic storms depends on the strength of the 11 yr solar cycle (SC), and the different phases in it. Since we have been recording sunspot numbers (which roughly indicate the activity of the sun) for centuries, and the storm index Dst (a measurement of geomagnetic activity on Earth) for decades, we study in this manuscript the connection between the two dataset, this is, how sunspot number (and therefore SC) relates to the occurrence of geomagnetic storms. We found that the latest SC behaved in a way, that is, more characteristic of the phases of low activity (minimum phase) of the previous cycles. We also found that in general, the declining phase of a cycle tends to be connected to the maximum phase of the next cycle, which indicates that a prediction of the next cycle can be attempted. In that regard, our results suggest that the SC that just started should be stronger than the current cycle, but no the strongest of the past five cycles.
Key Points
Geomagnetic storms are characterized by solar cycle (SC) and phases using a log‐normal distribution fitted using maximum likelihood method
SC24 behaved similarly to the minimum phase of the past five SCs in terms of the intensity of the storms that occurred
Descending phase characteristic lognormal coefficient correlates with the following maximum phase and may predict strength of SC25</description><identifier>ISSN: 1542-7390</identifier><identifier>ISSN: 1539-4964</identifier><identifier>EISSN: 1542-7390</identifier><identifier>DOI: 10.1029/2021SW002766</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Analysis ; Datasets ; Distribution (Probability theory) ; DST Index ; extreme value analysis ; Forecasts and trends ; Geomagnetic activity ; Geomagnetic storms ; Geomagnetism ; Ionospheric research ; Magnetic storms ; Maximum likelihood method ; Normal distribution ; Phases ; Probability distribution ; Solar cycle ; solar cycle 25 ; Statistical analysis ; Stochastic processes ; Storm index ; Storms ; Sunspot numbers ; Sunspots</subject><ispartof>Space Weather, 2021-09, Vol.19 (9), p.n/a</ispartof><rights>2021. The Authors.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3837-cb135f0258f2e3f27d1da0696087758905f61feb644e62fb1ce0b239b7a7c4b13</citedby><cites>FETCH-LOGICAL-c3837-cb135f0258f2e3f27d1da0696087758905f61feb644e62fb1ce0b239b7a7c4b13</cites><orcidid>0000-0002-9161-0888 ; 0000-0001-6006-6448 ; 0000-0003-1210-167X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2021SW002766$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2021SW002766$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,11562,27924,27925,45574,45575,46052,46476</link.rule.ids></links><search><creatorcontrib>Reyes, Paula I.</creatorcontrib><creatorcontrib>Pinto, Victor A.</creatorcontrib><creatorcontrib>Moya, Pablo S.</creatorcontrib><title>Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases</title><title>Space Weather</title><description>Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst<−50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log‐normal probability distribution over their minimum Dst index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with Dst< −100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the σ value—the fit log‐normal distribution “width” parameter—characteristic of the occurrence rate of storms, we have found that the σdes (the sigma value in the descending phase of one cycle) shows the highest correlation (r=−0.76) with σmax (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended.
Plain Language Summary
Geomagnetic storms are a common occurrence on Earth, and they can have significant impact on our lives. The occurrence of geomagnetic storms depends on the strength of the 11 yr solar cycle (SC), and the different phases in it. Since we have been recording sunspot numbers (which roughly indicate the activity of the sun) for centuries, and the storm index Dst (a measurement of geomagnetic activity on Earth) for decades, we study in this manuscript the connection between the two dataset, this is, how sunspot number (and therefore SC) relates to the occurrence of geomagnetic storms. We found that the latest SC behaved in a way, that is, more characteristic of the phases of low activity (minimum phase) of the previous cycles. We also found that in general, the declining phase of a cycle tends to be connected to the maximum phase of the next cycle, which indicates that a prediction of the next cycle can be attempted. In that regard, our results suggest that the SC that just started should be stronger than the current cycle, but no the strongest of the past five cycles.
Key Points
Geomagnetic storms are characterized by solar cycle (SC) and phases using a log‐normal distribution fitted using maximum likelihood method
SC24 behaved similarly to the minimum phase of the past five SCs in terms of the intensity of the storms that occurred
Descending phase characteristic lognormal coefficient correlates with the following maximum phase and may predict strength of SC25</description><subject>Analysis</subject><subject>Datasets</subject><subject>Distribution (Probability theory)</subject><subject>DST Index</subject><subject>extreme value analysis</subject><subject>Forecasts and trends</subject><subject>Geomagnetic activity</subject><subject>Geomagnetic storms</subject><subject>Geomagnetism</subject><subject>Ionospheric research</subject><subject>Magnetic storms</subject><subject>Maximum likelihood method</subject><subject>Normal distribution</subject><subject>Phases</subject><subject>Probability distribution</subject><subject>Solar cycle</subject><subject>solar cycle 25</subject><subject>Statistical analysis</subject><subject>Stochastic processes</subject><subject>Storm index</subject><subject>Storms</subject><subject>Sunspot numbers</subject><subject>Sunspots</subject><issn>1542-7390</issn><issn>1539-4964</issn><issn>1542-7390</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kM9LwzAUx4MoOKc3_4CAVzuTtEna4xhzCsLETnYMafqyZXTNTDtk_72RethJ3uE9Hp_v9_1A6J6SCSWseGKE0XJNCJNCXKAR5RlLZFqQy7P6Gt103S4yGWfZCC0W4Pd600LvDC57H_Z4acwxBGgNYN3WeLUFF_AHNLp3vsVr129x6Rsd8OxkGsDvW91Bd4uurG46uPvLY_T5PF_NXpK35eJ1Nn1LTJqnMjEVTbkljOeWQWqZrGmtiSgEyaXkeUG4FdRCJbIMBLMVNUAqlhaV1NJkUTxGD4PvIfivI3S92vljaONIxXg8W3DKeaQmA7XRDSjXWt8HbWLUsHfGt2Bd7E-loHETVmRR8DgITPBdF8CqQ3B7HU6KEvX7W3X-24izAf-OPqd_WVWu54zSQqY_2E14Yg</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Reyes, Paula I.</creator><creator>Pinto, Victor A.</creator><creator>Moya, Pablo S.</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-9161-0888</orcidid><orcidid>https://orcid.org/0000-0001-6006-6448</orcidid><orcidid>https://orcid.org/0000-0003-1210-167X</orcidid></search><sort><creationdate>202109</creationdate><title>Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases</title><author>Reyes, Paula I. ; Pinto, Victor A. ; Moya, Pablo S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3837-cb135f0258f2e3f27d1da0696087758905f61feb644e62fb1ce0b239b7a7c4b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analysis</topic><topic>Datasets</topic><topic>Distribution (Probability theory)</topic><topic>DST Index</topic><topic>extreme value analysis</topic><topic>Forecasts and trends</topic><topic>Geomagnetic activity</topic><topic>Geomagnetic storms</topic><topic>Geomagnetism</topic><topic>Ionospheric research</topic><topic>Magnetic storms</topic><topic>Maximum likelihood method</topic><topic>Normal distribution</topic><topic>Phases</topic><topic>Probability distribution</topic><topic>Solar cycle</topic><topic>solar cycle 25</topic><topic>Statistical analysis</topic><topic>Stochastic processes</topic><topic>Storm index</topic><topic>Storms</topic><topic>Sunspot numbers</topic><topic>Sunspots</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reyes, Paula I.</creatorcontrib><creatorcontrib>Pinto, Victor A.</creatorcontrib><creatorcontrib>Moya, Pablo S.</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Space Weather</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reyes, Paula I.</au><au>Pinto, Victor A.</au><au>Moya, Pablo S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases</atitle><jtitle>Space Weather</jtitle><date>2021-09</date><risdate>2021</risdate><volume>19</volume><issue>9</issue><epage>n/a</epage><issn>1542-7390</issn><issn>1539-4964</issn><eissn>1542-7390</eissn><abstract>Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst<−50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log‐normal probability distribution over their minimum Dst index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with Dst< −100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the σ value—the fit log‐normal distribution “width” parameter—characteristic of the occurrence rate of storms, we have found that the σdes (the sigma value in the descending phase of one cycle) shows the highest correlation (r=−0.76) with σmax (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended.
Plain Language Summary
Geomagnetic storms are a common occurrence on Earth, and they can have significant impact on our lives. The occurrence of geomagnetic storms depends on the strength of the 11 yr solar cycle (SC), and the different phases in it. Since we have been recording sunspot numbers (which roughly indicate the activity of the sun) for centuries, and the storm index Dst (a measurement of geomagnetic activity on Earth) for decades, we study in this manuscript the connection between the two dataset, this is, how sunspot number (and therefore SC) relates to the occurrence of geomagnetic storms. We found that the latest SC behaved in a way, that is, more characteristic of the phases of low activity (minimum phase) of the previous cycles. We also found that in general, the declining phase of a cycle tends to be connected to the maximum phase of the next cycle, which indicates that a prediction of the next cycle can be attempted. In that regard, our results suggest that the SC that just started should be stronger than the current cycle, but no the strongest of the past five cycles.
Key Points
Geomagnetic storms are characterized by solar cycle (SC) and phases using a log‐normal distribution fitted using maximum likelihood method
SC24 behaved similarly to the minimum phase of the past five SCs in terms of the intensity of the storms that occurred
Descending phase characteristic lognormal coefficient correlates with the following maximum phase and may predict strength of SC25</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2021SW002766</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-9161-0888</orcidid><orcidid>https://orcid.org/0000-0001-6006-6448</orcidid><orcidid>https://orcid.org/0000-0003-1210-167X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Datasets Distribution (Probability theory) DST Index extreme value analysis Forecasts and trends Geomagnetic activity Geomagnetic storms Geomagnetism Ionospheric research Magnetic storms Maximum likelihood method Normal distribution Phases Probability distribution Solar cycle solar cycle 25 Statistical analysis Stochastic processes Storm index Storms Sunspot numbers Sunspots |
| title | Geomagnetic Storm Occurrence and Their Relation With Solar Cycle Phases |
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