Search Publications
Sympathetic Solar Eruption on 2024 February 9
Feng, Li; Li, Shu-Yue; Zhang, Qing-Min +4 more
In this paper, we perform a follow-up investigation of the solar eruption originating from active region 13575 on 2024 February 9. The primary eruption of a hot channel generates an X3.4 class flare, a full-halo coronal mass ejection (CME), and an extreme-ultraviolet (EUV) wave. Interaction between the wave and a quiescent prominence (QP) leads to…
A Statistical Study of Magnetic Flux Emergence in Solar Active Regions Prior to Strongest Flares
Abramenko, Valentina I.; Kutsenko, Alexander S.; Plotnikov, Andrei A.
Using the data on magnetic field maps and continuum intensity for Solar Cycles 23 and 24, we explored 100 active regions (ARs) that produced M5.0 or stronger flares. We focus on the presence/absence of the emergence of magnetic flux in these ARs 2–3 days before the strong flare onset. We found that 29 ARs in the sample emerged monotonically amidst…
Resolution Enhancement of SOHO/MDI Magnetograms
Qin, Ying; Yu, Xiao-Guang; Ji, Kai-Fan +1 more
Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency. The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar m…
A Revised Graduated Cylindrical Shell Model and its Application to a Prominence Eruption
Zhang, Qing-Min; Hou, Zhen-Yong; Bai, Xian-Yong
In this paper, the well-known graduated cylindrical shell (GCS) model is slightly revised by introducing longitudinal and latitudinal deflections of prominences originating from active regions (ARs). Subsequently, it is applied to the three-dimensional (3D) reconstruction of an eruptive prominence in AR 13110, which produced an M1.7 class flare an…
Super-resolution of Solar Magnetograms Using Deep Learning
Xu, Long; Zhao, Dong; Dou, Fengping +2 more
Currently, data-driven models of solar activity forecast are investigated extensively by using machine learning. For model training, it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal resolutions. In this paper, we employ deep learning models for super…
Analyzing Dominant 13.5 and 27 day Periods of Solar Terrestrial Interaction: A New Insight into Solar Cycle Activities
Saikia, Eeshankur; Syiemlieh, Rissnalin; Adhikary, Manashee +1 more
Our analysis presents an explanation of the Sun-Earth coupling mechanism during declining phase of a solar cycle, and how the dominant 13.5 and 27 day periods play roles in the coupling mechanism which led to intense terrestrial magnetic storms during this declining phase compared to the rising phase of a solar cycle. Moreover, it is observed that…
Ensemble Numerical Simulations of Realistic SEP Events and the Inspiration for Space Weather Awareness
Li, Gang; Wang, Xin; Luo, Bingxian +5 more
The solar energetic particle (SEP) event is a kind of hazardous space weather phenomena, so its quantitative forecast is of great importance from the aspect of space environmental situation awareness. We present here a set of SEP forecast tools, which consists of three components : (1) a simple polytropic solar wind model to estimate the backgroun…
How much has the Sun influenced Northern Hemisphere temperature trends? An ongoing debate
Henry, Gregory W.; Solheim, Jan-Erik; Scafetta, Nicola +20 more
In order to evaluate how much Total Solar Irradiance (TSI) has influenced Northern Hemisphere surface air temperature trends, it is important to have reliable estimates of both quantities. Sixteen different estimates of the changes in TSI since at least the 19th century were compiled from the literature. Half of these estimates are "low…
Class imbalance problem in short-term solar flare prediction
Wan, Jie; Fu, Jun-Feng; Liu, Jin-Fu +3 more
Using data-driven algorithms to accurately forecast solar flares requires reliable data sets. The solar flare dataset is composed of many non-flaring samples with a small percentage of flaring samples. This is called the class imbalance problem in data mining tasks. The prediction model is sensitive to most classes of the original data set during …
Predicting the CME arrival time based on the recommendation algorithm
Luo, Bingxian; Liu, Zhu; Li, Ming +9 more
CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect. It is of special significance to predict whether CME will reach the Earth and when it will arrive. In this paper, we firstly built a new multiple association list for 215 different events with 18 character…