Solar activity prediction studies and services in NAOC

He, Han; Wang, Huaning; Du, Zhanle; Li, Rong; Cui, Yanmei; Zhang, L.; He, Yulin

doi:10.1016/j.asr.2007.02.068

Cited by 13 publications

(14 citation statements)

References 15 publications

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“…Machine learning can automatically make a model from data, even in case that they are not clearly understood. Therefore machine learning technology has been employed for space weather applications in the following two aspects: space weather prediction (Al-Omari et al 2010;Chen et al 2010;Colak et al 2009;Gavrishchaka et al 2001;He et al 2008;Li et al 2007;Liu Corresponding Author : Y.-J. Moon et al 2011;Olmedo et al 2005;Qahwaji et al 2007Qahwaji et al , 2008Yuan et al 2011) and solar feature identification (Henwood et al 2010;Labrosse et al 2010;Quaalude et al 2003Quaalude et al , 2005Martens et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Choi¹,

Moon²,

Vien³

et al. 2012

Journal of The Korean Astronomical Society

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In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

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Section: Introductionmentioning

confidence: 99%

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Choi¹,

Moon²,

Vien³

et al. 2012

Journal of The Korean Astronomical Society

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“…To analyze the variations of magnetic field configuration through the flare eruptions, for each flare event in AR 10930 and AR 11158 (see Table 1), we selected one photospheric vector magnetogram before the flare and one magnetogram after the flare, and calculated the coronal magnetic field distribution for each magnetogram based on the NLFFF model. The algorithm for the NLFFF numerical modeling was developed and described in detail in our previous papers (He & Wang 2008;He et al 2011), which is an improvement from the direct boundary integral equation (DBIE) approach suggested by Yan & Li (2006). (Note that DBIE is an advancement of the original BIE method proposed by , see also Wang et al 2000Wang et al , 2001He & Wang 2006;He et al 2011.…”

Section: Coronal Magnetic Field and Nonpotentiality Of The Two Activementioning

confidence: 99%

“…(Note that DBIE is an advancement of the original BIE method proposed by , see also Wang et al 2000Wang et al , 2001He & Wang 2006;He et al 2011. ) This method has been utilized to analyze the coronal magnetic structures for a variety of solar ARs (He & Wang 2008;He et al 2011He et al , 2012He et al , 2014Liu et al 2013;Wang et al 2013;Yang et al 2014;Wang et al 2015). Figure 2 displays the employed photospheric vector magnetograms before and after the two flare events.…”

Section: Coronal Magnetic Field and Nonpotentiality Of The Two Activementioning

confidence: 99%

Chirality and magnetic configuration associated with two-ribbon solar flares: AR 10930 versus AR 11158

Wang

Yan

et al. 2020

Advances in Space Research

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The magnetic configuration of flare-bearing active regions (ARs) is one key aspect for understanding the initiation of solar flares. In this paper, we perform a comparative analysis on the chiral characteristics and the magnetic configurations of two X-class two-ribbon flares happening in AR 10930 and AR 11158, whose photospheric magnetic fields were observed by the Hinode and SDO satellites, respectively. The corresponding coronal magnetic fields were calculated based on the nonlinear forcefree field model. It is found that both the flares were initiated in local areas with extremely strong electric current density. The chirality of the magnetic field (indicated by the sign of force-free factor α) along the main polarity inversion line (PIL) is opposite for the two ARs, that is, left-handed (α < 0) for AR 10930 and right-handed (α > 0) for AR 11158. The analysis shows that, for both flare events, prominent magnetic connectivity (indicated by both high α and strong current distributions) was formed above the main PIL before the flare and was totally broken after the flare eruption. Yet the two branches of broken magnetic connectivity, combined with the isolated electric current at the magnetic connectivity breaking site, compose the opposite magnetic configurations for the two ARs owing to their opposite chiral characteristics. That is, Z-shaped configuration for AR 10930 with negative α and left-handed chirality, and inverse Z-shaped configuration for AR 11158 with positive α and right-handed chirality. We speculate that two-ribbon flares can be generally classified to these two magnetic configurations by chirality (α sign) of ARs.

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“…At SAPC, artificial intelligence techniques and physical analyses of the photospheric magnetic field and coronal magnetic field have been introduced in solar activity prediction model studies in recent years He et al, 2008). The currently available prediction models for the operational forecasting of solar activities include the solar flare short-term prediction model, the solar proton event shortterm prediction model, the solar 10.7 cm radio flux prediction model, the solar active level quantitative assessment model, the nonlinear force-free field (NLFFF) extrapolation model for the coronal magnetic field, and the solar cycle long-term prediction model (He et al, 2012).…”

Section: Rwc-china In the New Centurymentioning

confidence: 99%

“…The SMFT can obtain high-quality vector magnetograms of solar active regions and has maintained continuous observations until the present day. The vector magnetic field accumulated by SMFT in the past decades greatly improved solar activity prediction methods and modeling studies (He et al, 2008). The timely vector magnetograms of SMFT can also be helpful for the daily forecasting of solar eruptive events (Zhang et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

A brief history of Regional Warning Center China (RWC-China)

Wang

et al. 2018

Hist. Geo Space. Sci.

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Abstract. Solar-terrestrial prediction services in China began in 1969 at the Beijing Astronomical Observatory (BAO), Chinese Academy of Sciences (CAS). In 1990, BAO joined the International URSIgram and World Days Service (IUWDS) and started solar-terrestrial data and prediction interchanges with other members of IUWDS. The short-term solar activity prediction service with standard URSIgram codes began in January 1991 at BAO, and forecasts have been issued routinely every weekday from then on. The Regional Warning Center Beijing (RWC-Beijing) of IUWDS was officially approved in China in 1991 and was formally established in February 1992. In 1996, the IUWDS was changed to the current name, the International Space Environment Service (ISES). In 2000, the RWC-Beijing was renamed RWC-China according to ISES requirements. In 2001, the National Astronomical Observatories, CAS (NAOC) was established. All the solar-terrestrial data and prediction services of BAO were taken up by NAOC. The headquarters of RWC-China is located on the campus of NAOC.

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Solar activity prediction studies and services in NAOC

Cited by 13 publications

References 15 publications

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Chirality and magnetic configuration associated with two-ribbon solar flares: AR 10930 versus AR 11158

A brief history of Regional Warning Center China (RWC-China)

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