Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity pr...Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.展开更多
A study is made of the differences in the polarization distribution and other characteristics of microwave emission for several active regionswith high flare productivity. Conclusions are drawn about the magnetic fiel...A study is made of the differences in the polarization distribution and other characteristics of microwave emission for several active regionswith high flare productivity. Conclusions are drawn about the magnetic field structure of these regions at coronal heights.展开更多
Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-fil...Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-filtergrams and characteristics of active regions received from other sources, they form the initial database. The first stage includes superimposing the images, identifying microwave sources with active regions, assigning NOAA numbers to the sources, and determining for each active region the heliolatitude, extent, and inclination angle of the group's axis to the equator. These data are used to calculate the boundaries of longitude zones for each active region. A next stage involves determining the brightness temperatures of microwave sources less than the polarization distribution, the degree of polarization, and microwave emission flux, as well as calculating the parameters of microwave sources. Each parameter is assigned its own value of the weight factor, and the sum of values is used to draw the conclusion about the flare occurrence probability in each active region and on the Sun in general.展开更多
基金Science and Technology Facilities Council(STFC,Grant No.ST/M000826/1)National Research Development and Innovation Office(OTKA,Grant No.K142987)Hungary for enabling this research+4 种基金ST/S000518/1,PIA.CE.RI.2020-2022 Linea 2,CESAR 2020-35-HH.0,and UNKP-224-II-ELTE-186 grantsthe support from ISSI-Beijing for their project“Step forward in solar flare and coronal mass ejection(CME)forecasting”supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0560000)the National Key R&D Program of China(Grant No.2021YFA1600504)the National Natural Science Foundation of China(Grant No.11873060)。
文摘Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.
基金Supported by the Ministry of Industry and Science (No.477.2003,2)Russian Federal Program "Astronomy", and the China- Russia Joint Research Center on Space WeatheChinese Academy of Sciences
文摘A study is made of the differences in the polarization distribution and other characteristics of microwave emission for several active regionswith high flare productivity. Conclusions are drawn about the magnetic field structure of these regions at coronal heights.
基金Supported by the Education and Science Ministry of Russian Federation (477.2003.2)Russian Federal Program "Astronomy"the China-Russia Joint Research Center on Space Weather, Chinese Academy of Sciences
文摘Input data of the system are two-dimensional images and one-dimensional distributions of total and polarized solar emission at 5.2 cm wavelength obtained with SSRT. Together with photoheliograms, magnetograms, Hα-filtergrams and characteristics of active regions received from other sources, they form the initial database. The first stage includes superimposing the images, identifying microwave sources with active regions, assigning NOAA numbers to the sources, and determining for each active region the heliolatitude, extent, and inclination angle of the group's axis to the equator. These data are used to calculate the boundaries of longitude zones for each active region. A next stage involves determining the brightness temperatures of microwave sources less than the polarization distribution, the degree of polarization, and microwave emission flux, as well as calculating the parameters of microwave sources. Each parameter is assigned its own value of the weight factor, and the sum of values is used to draw the conclusion about the flare occurrence probability in each active region and on the Sun in general.
