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Predicting Interplanetary Shock Occurrence for Solar Cycle 25: Opportunities and Challenges in Space Weather Research

Space Weather(2024)

Univ Maryland | Southwest Res Inst | Natl Inst Space Res | Trinity Coll Dublin | RMIT Univ | Phys Res Lab | Univ Aquila | NASA | Johns Hopkins Univ | South African Natl Space Agcy | Space Sci Inst | Nagoya Univ | Finnish Meteorol Inst | Univ Bergen | Natl Ctr Atmospher Res | Univ Michigan | Indian Inst Sci Educ & Res Kolkata | Indian Inst Geomagnetism | Univ Otago | Northumbria Univ | Univ Colorado | Vikram Sarabhai Space Ctr VSSC | Univ Calif Los Angeles

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Abstract
Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate well with solar activity, being more numerous during times of high sunspot numbers. Earth‐bound IP shocks cause many space weather effects that are promptly observed in geospace and on the ground. Such effects can pose considerable threats to human assets in space and on the ground, including satellites in the upper atmosphere and power infrastructure. Thus, it is of great interest to the space weather community to (a) keep an accurate catalog of shocks observed near Earth, and (b) be able to forecast shock occurrence as a function of the solar cycle (SC). In this work, we use a supervised machine learning regression model to predict the number of shocks expected in SC25 using three previously published sunspot predictions for the same cycle. We predict shock counts to be around 275 ± 10, which is ∼47% higher than the shock occurrence in SC24 (187 ± 8), but still smaller than the shock occurrence in SC23 (343 ± 12). With the perspective of having more IP shocks on the horizon for SC25, we briefly discuss many opportunities in space weather research for the remainder years of SC25. The next decade or so will bring unprecedented opportunities for research and forecasting effects in the solar wind, magnetosphere, ionosphere, and on the ground. As a result, we predict SC25 will offer excellent opportunities for shock occurrences and data availability for conducting space weather research and forecasting.
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space weather,interplanetary shocks,machine learning,solar cycle 25,sunspot numbers
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要点】:本文利用监督机器学习回归模型预测了太阳周期25(SC25)期间星际冲击的发生次数,并探讨了未来在空间天气研究中的机会与挑战。

方法】:研究采用监督机器学习回归模型,基于三个已发表的同一周期太阳黑子预测数据来预测SC25期间冲击次数。

实验】:使用三个不同的太阳黑子预测数据集进行模型训练和预测,得到SC25期间冲击次数预计为275±10次,较SC24期间增加了约47%,但低于SC23期间的343±12次。