A short-term solar eruption
events forecasting model
Xin Huang, Huaning Wang
National Astronomical Observatories,
Chinese Academy of Sciences

2015/3/17

General Session 1 @ AOSWA

1Main Contents• Background & Motivation• Present situation of solar flare forecasting• Difficulties of CMEs forecasting• Relationships between flares and CMEs
• Solar eruption events forecasting model forflares and CMEs
• Summary & Further work

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Background & Motivation• The sun is the main source of space weather, so it is important to forecast
the solar eruptions.
• There are some practical flare forecasting models• We do not have any practical forecasting model for CMEs
• Flares and CMEs are two different manifestations of the same energy
release process• We need a solar eruption events forecasting model which considers flaresand CMEs together
 Quiet Confined flare: flare without CME
 Eruptive flare: flare associated with CME• Solar eruption events forecasting model
G. P. Zhou et al., A&A 2006
 Improved flare forecasting model Simplified CMEs forecasting model
2015/3/17

General Session 1 @ AOSWA3Present situation of solar flare forecasting• Probabilistic yes/no forecasts based on theNon-potentiality of an active region• McIntosh classifications • Free magnetic energy• Magnetic field gradient • Twist parameter• Length of neutral line • Spectral index• Fractal dimension• Number of singularpoints• Flow field• Tilt angle• Magnetic shear angle• Current density
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Difficulties of CMEs forecasting• Uncertainty of frontside or backside CMEs
• Uncertainty of source region for CMEs
CMEs are inherently large-scale events L. van Driel-Gesztelyi et.
al. Ann. Geophys., 2008
CMEs start small-scale and then evolve to becomelarge-scale events• In short:
Closed magnetic field structure + Non-potentiality
Appearance of closed magnetic field structure on theSun
Magnetic source region has to build-up free energy
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Relationship between flares and CMEs
• Flare VS CMEs
 Larger solar flare is easier to associate with CME
 C 16%-25%, M 42%-55%, X 90%-92% (Yashiro et al 2005 )• Confined flare VS Eruptive flare Non-potentiality (Sun et al 2015)
 Displacement of the location of the energy release (Wang & Zhang2007 ) Background field ( Cheng et al 2011)• All parameter is only used to analysis relationships after flare has
happened, can not be used to forecast

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Solar eruption events forecasting model• Displacement parameter between center of active
region and the center of the free magnetic energy
and the overlying magnetic parameter are extracted
to distinguish confined flares from eruptive flares• Combined with the classical flare forecasting model(based on non-potential parameters), we get thesolar eruption events forecasting model

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Solar eruption events forecasting model
Activeregion

predictors

Quiet

Forecasting
model

Quiet or flaring

Quiet

Flaring
Confined
Confined or
eruptive

Confinedflare(frequency)

Eruptive
Eruptive flare(direction &speed)

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Active region data
• 12 month data ( 2011.8—2012.7)

Second NWRA Flare Forecasting Workshop hosted by Drs. Leka and Barnes

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Event data
• 12 month data ( 2011.8—2012.7)

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Predictors• Quiet or flaring Magnetic field gradient Length of neutral line Number of singular points
 Total photospheric magnetic free energy• Confined or eruptive
 Displacement parameters
 Distance between the center of longitudinal magnetogram and thecenter of photospheric magnetic free energy density (Dmf) Distance Ratio between Dmf and Dpn (Dpn is the distance between
positive magnetic field and negative magnetic field)
 Coronal parameters Decay Index at the height of 10 Mm
 Transverse magnetic flux ratio between the summation from 0Mm to10Mm and the summation from 10Mm to 20Mm
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Modeling method• Decision tree

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Performance
TP Rate FP Rate
0.783 0.122
00.613 0.089
10.605 0.136
 2=== Confusion Matrix ===ab c  classified as1139 122 193 | a = 03 19 9 | b = 1
6 11 26 | c = 2
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Statistical characteristics of events: flare frequency
750

1600
1400
1200
1000
800
600

The number of singular points

200

150

100

50002

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4-50

30

20

100400200

40024-1002

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6No flareLow frequency FlaHigh frequency fla

2.5

52

4
decayIndex

1800

Proxy for total photospheric magnetic free energy (erg/cm)

60

250

The length of neutral line (Pixels)

The maximum gradient of magnetic field (G/Pixel)

2000

x 10

3

1.5

120.5

100-102

4-0.5

04

214

Statistical characteristics of events: attacking direction
350

Measurement Position Angle (MPA)

300

250

2001501005002015/3/17050

100150200
250
Active Region Position Angle

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300

350

15

Statistical characteristics of events: attacking direction
25

Number of Events

20

15

10

50150
100
50
0-50-100-150
Difference Between Active Region Position Angle and Measurement Position Angle of CME

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Statistical characteristics of events: speed
760

1400

1200

1000

800

600

The number of singular points

140
120
10080
60
40504030
20
10204000020002

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42

decayIndex

160

Proxy for total photospheric magnetic free energy (erg/cm)

70

180

The length of neutral line (Pixels)

The maximum gradient of magnetic field (G/Pixel)

1600

x 10

32

1.5

1

1

0.5

00-1

0

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Middle Speed CME
High Speed CME-0.502417

Summary• From the flare forecasting point of view:
Extended flare forecasting model, flares andCMEs are considered as different
manifestation of the magnetic releasingprocess• From the CMEs forecasting point of view:
The associated flares reduce following
uncertainties of CMEs:
Uncertainty of frontside and backside
Uncertainty of source region
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Summary• Solar eruption events forecasting model
12-Jul-2000
C FlareM FlareX FlareCME Speed<=500
500<CME Speed<=1000
1000<CME Speed• Further work
 Study on the relationships between quiet filaments and CMEs,
 Build the forecasting model for quiet filament related CMEs
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Thanks！

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