Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed

Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning...
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Autor*in:	Qirong Jiao [verfasserIn] Wenlong Liu [verfasserIn] Dianjun Zhang [verfasserIn] Jinbin Cao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Space weather

Übergeordnetes Werk:	In: The Astrophysical Journal - IOP Publishing, 2022, 958(2023), 1, p 70
Übergeordnetes Werk:	volume:958 ; year:2023 ; number:1, p 70

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4357/acfc21

Katalog-ID:	DOAJ091074010

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source	In The Astrophysical Journal 958(2023), 1, p 70 volume:958 year:2023 number:1, p 70
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author	Qirong Jiao
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topic_title	QB460-466 Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed Space weather
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title	Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
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title_full	Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
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title_sort	relation between latitude-dependent sunspot data and near-earth solar wind speed
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title_auth	Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
abstract	Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning prediction approach. The results show that the low-latitude sunspot number has a larger effect on the solar wind speed. The time delay between the annual average solar wind speed and sunspot number decreases as the latitude range decreases. A machine-learning model is developed for the prediction of solar wind speed considering latitude and time effects. It is found that the model performs differently with latitude-dependent sunspot data. It is revealed that the timescale of the solar wind speed is more strongly influenced by low-latitude sunspots and that sunspot data have a greater impact on the 30 day average solar wind speed than on a daily basis. With the addition of sunspot data below 7.°2 latitude, the prediction of the daily and 30 day averages is improved by 0.23% and 12%, respectively. The best correlation coefficient is 0.787 for the daily solar wind prediction model.
abstractGer	Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning prediction approach. The results show that the low-latitude sunspot number has a larger effect on the solar wind speed. The time delay between the annual average solar wind speed and sunspot number decreases as the latitude range decreases. A machine-learning model is developed for the prediction of solar wind speed considering latitude and time effects. It is found that the model performs differently with latitude-dependent sunspot data. It is revealed that the timescale of the solar wind speed is more strongly influenced by low-latitude sunspots and that sunspot data have a greater impact on the 30 day average solar wind speed than on a daily basis. With the addition of sunspot data below 7.°2 latitude, the prediction of the daily and 30 day averages is improved by 0.23% and 12%, respectively. The best correlation coefficient is 0.787 for the daily solar wind prediction model.
abstract_unstemmed	Solar wind is important for the space environment between the Sun and the Earth and varies with the sunspot cycle, which is influenced by solar internal dynamics. We study the impact of latitude-dependent sunspot data on solar wind speed using the Granger causality test method and a machine-learning prediction approach. The results show that the low-latitude sunspot number has a larger effect on the solar wind speed. The time delay between the annual average solar wind speed and sunspot number decreases as the latitude range decreases. A machine-learning model is developed for the prediction of solar wind speed considering latitude and time effects. It is found that the model performs differently with latitude-dependent sunspot data. It is revealed that the timescale of the solar wind speed is more strongly influenced by low-latitude sunspots and that sunspot data have a greater impact on the 30 day average solar wind speed than on a daily basis. With the addition of sunspot data below 7.°2 latitude, the prediction of the daily and 30 day averages is improved by 0.23% and 12%, respectively. The best correlation coefficient is 0.787 for the daily solar wind prediction model.
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title_short	Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed
url	https://doi.org/10.3847/1538-4357/acfc21 https://doaj.org/article/46170204a8fa4c8cb0069bb2db5d9ef1 https://doaj.org/toc/1538-4357
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Relation between Latitude-dependent Sunspot Data and Near-Earth Solar Wind Speed

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Zugang & Verfügbarkeit

Vorhandene Bände

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