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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…