Development and Evaluation of an Automated System for Empirical Forecasting of Flares

Transcription

1 Development and Evaluation of an Automated System for Empirical Forecasting of Flares Sung-Hong Park1 Collaborators: J. Baek2, S. Bong2, Y. Yuan3, J. Jing3 1 National Observatory of Athens (NOA) 2 Korea Astronomy and Space Science Institute (KASI) 3 New Jersey Institute of Technology (NJIT) ISSI meeting on January 26-30, 2015

2 Contents 1. Main Objectives & Overall Strategy 2. Empirical Flare Forecasting Models 3. Fully Automated System for Flare Forecasting 1) Development 2) Evaluation 4. Discussion 1) Helicity Flux Density 2) Applications: Flare Prediction for Limited FOV Observations

3 Flare Occurrence (at least C-class): Yes or No? Yes! No! 3

4 How about this AR? Yes! 4

5 1. Main Objectives Well-known Facts Most of solar flares occur in active regions (ARs) where intense magnetic fields exist in the solar atmosphere. In addition, it has been well studied that flare-productive ARs typically have complex and non-potential magnetic structures. Unknown Facts: it is not yet known exactly (1) when flares will occur, (2) how large their strength/size will be (e.g., peak/total fluxes at a specific range of wavelengths, duration, spatial size), (3) where they will occur in ARs. Hinode/SOT Ca II H image of the 2006 December 13 flare 5

6 Overall Strategy Parameter Selec>on Flare associated Reflec>ng an AR s temporal evolu>on Associated with magne>c complexity and non- poten>ality Empirical Model Prac>cal (useful for opera>onal) Simple Automated System Data collec>on/analysis Forecas>ng Evalua>on & Update 6

7 2. Empirical Flare Forecasting Models 2.1. Magnetic Helicity twists kinks inter-linkages Magnetic helicity is a measure for global complexity of a given magnetic field system in terms of twists (T), kinks (K), and inter-linkages (L) of magnetic field lines (Beger & Field 1984; Pevtsov 2008; Démoulin & Pariat 2009). H = (T i +K i ) Φ i Φ i + L ij Φ i Φ j i i j i 7

8 WHY Magnetic Helicity? Simulations: a number of numerical MHD simulations show that magnetic field configurations for the initiation of eruptions are quite far from a potential state (e.g., Chen & Shibata 2000, Fan & Gibson 2004, Archontis & Torok 2008, Kusano et al. 2012). Observations: (1) twisted or kinked structures of eruptive prominences/coronal jets/cmes/ interplanetary magnetic clouds and (2) rotating sunspots/shear flows/sheared or twisted magnetic fields/s- or J-shape coronal loops in flare/cme-productive active regions. 8

9 Magnetic Helicity Injection through a Photospheric Surface S - Berger & Field (1984): (a) Horizontal flux rope emergence (b) Vertical flux rope emergence - Démoulin & Berger (2003): Field line footpoint velocity 9 v v t B t1 v n u B t0

10 Helicity Injection before X1.4 flare, BUT Little Magnetic Flux Increase Flux Helicity Soft X-ray

11 2.2. Helicity Injection vs. Flare Productivity The feasibility of using magnetic helicity to develop a flare forecasting system was explored with a data sample of 378 ARs covering the solar cycle 23 ( ) min SOHO/MDI LOS magnetograms - 24-hr profile of helicity injection rate in the entire photospheric surface of an AR - Average helicity injection rate - The absolute value of the average helicity injection rate is compared with (1) flare index derived from the GOES soft X-ray observation for the next 24-hr time window and (2) flare-productive probability calculated for the next 3-day time window. < Flare Index > < Flare-Productive Probability > i = GOES class 11

12 (1) Correlation between Magnetic Helicity Injection Rate and Flare Index (Park et al. 2010) Flaring ARs: 153 Non-flaring ARs:

13 Flaring # 153 Non-flaring # 225 Grouping: similar < H r > Grouping: similar < Flux > Similar!! Twice!! KASI/SOS Seminar 13

14 (2) Flare-Productive Probability vs. Magnetic Helicity Injection Rate (Park et al. 2010) P i 14

15 3. Fully Automated Forecasting System 3.1. Development Input Data Data Analysis Forecas6ng Update - SDO/HMI full- disk line of sight magne>c field (B l ) data - NOAA/Solar Region Summary (SRS) data - AR detec>on - AR tracking - Calcula>on of ARs magne>c helicity - Based on empirical flare models - Forecas>ng Products: flare index and flare produc>vity for the >me windows of the next 1 st, 2 nd, and 3 rd days - Evalua>on of flare forecas>ng results - Update of the historical correla>on data between magne>c helicity and flare parameters 15

16 Input Data (1) SDO/HMI data Region of Forecas6ng Full- disk line- of- sight magne>c field (Bl) data Con>nuous observa>ons without a data gap un>l the year 2020 Temporal resolu>on: 45 sec Spa>al resolu>on: 0.5 /pixel 24 magnetograms with 1- hr cadence are used to carry out flare/CME forecas>ng for the >me windows of the 1st, 2nd, and 3rd days following the 24- hour period of the measurement of magne>c parameters. (2) NOAA/SRS data - 16 ARs ID numbers and loca>ons given by NOAA/SWPC

17 Data Analysis 1 B 2 n - AR extrac>on and tracking - Es>ma>on of the normal magne>c field component: B n B l / cosψ where ψ is the heliocentric angle AR detec>on from NOAA/SRS data 4 3 Magne>c footpoint velocity (u) is measured with the differen>al affine velocity es>mator (DAVE) method developed by Schuck (2006). Helicity flux density G θ. Posi>ve/nega>ve values of G θ corresponding to right/lee- handed helicity flux densi>es are displayed as white/black tones. Helicity Flux Density (Pariat et al. 2005) Helicity Injec6on Rate 17

18 Forecasting F idx Flare Productivity C M X 3 rd 2 nd 1 st H avg [10 40 Mx 2 hr -1 ] H avg [10 40 Mx 2 hr -1 ] 1) Empirical models based on the sta>s>cal studies of helicity injec>on in ARs associated with flares 2) Forecas>ng Products Current Service Future Service Flare Forecas6ng Flare Index: soe X- ray intensity per unit >me Flare Produc>vity: occurrence probability (%) of GOES- class (i.e., C, M, and X- class) flares Flare Dura>on (impulsive or gradual) Flare Type (compact or two ribbon / non- CME or CME) 18

19 3.2. Evaluation Period: 2013/02/ /02/19 (4 X-class, 27 M-class, 194 C-class flares) Helicity vs. 1 st day Flare Index M C V 1 V 2 1) X < V 1 : 443 No- flare: 401 (91%) >C: 35 (8%) >M: 7 (2%) 2) V 1 < X < V 2 : 422 No- flare: 299 (71%) >C: 95 (23%) >M: 21 (5%) 19 3) X > V 2 : 191 No- flare: 102 (53%) >C: 77 (40%) >M: 28 (15%)

20 Contingency Tables C- class Flare within 24 hour Yes Forecast No Observed Yes No Skill Score (X-class) based on Shuan s Definition: 0.61 (0.52) M- class Flare within 24 hour Yes Forecast No Observed Yes 9 18 No X- class Flare within 24 hour Yes Forecast No Observed Yes 2 2 No

21 Forecas6ng Results POD : TP/(TP+FN) FAR : FP/(TP+FP) ACC : (TP+TN)/(TP+FP+FN+TN) SR : TP/(TP+FP) CSI : TP/(TP+FP+FN) HSS TSS 1 st Day C M X 0.77 (0.74) 0.69 (0.67) 0.64 (0.71) 0.85 (0.69) 0.94 (0.86) 0.61 (0.83) 0.5 (0.86) 0.98 (0.97) 0.89 (0.88) (0.30) 0.38 (0.44) 0.08 (0.18) 0.46 (0.53) 0.03 (0.05) 0.39 (0.74) GS Threshold Prob. [%]

22 Comparison of Flare Forecas6ng Results (Bloomfield et al. 2012: 1996/08/ /12/31)

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24 4. Discussion How can this flare forecasting system be used for a limited fieldof-view flare observation? (1) Exclusion of less flare-productive ones among several ARs on the solar disk if there are several (2) Selection of the most likely flare-productive AR Limited field-of-view observations: ~1 1 arcmin 2 (1) DKIST Visible Broadband Imager: 2 2 arcmin 2 (optical), arcsec 2 (blue channel), arcsec 2 (red channel) Visible Spectro-polarimeter: 2 2 arcmin 2 Visible Tunable Filter: 1 1 arcmin 2 (2) EST High resolution mode: 1 1 arcmin 2 Large field-of-view mode: 2 2 arcmin 2 Typical AR size: a few arcmin 2 But some large flaring ARs >> 1 arcmin 2 (e.g. AR 12192: ~10 10 arcmin 2 ) 24

25 AR

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27 Cartoon of the large-scale magnetic field line structure in AR 9236

28 5. Summary and Future Works (1) Magnetic helicity injection through an entire AR surface A fully automated system for flare forecasting has been developed. It can be used for a reference of selecting the most likely flareproductive AR (or at least excluding non-flaring ARs). (2) Magnetic helicity flux density map in a target AR It is needed to develop a model and program to estimate the largescale coronal magnetic field structure in the AR from helicity flux density maps combined with (maybe simple potential) magnetic field line extrapolation. Then it can be a useful tool for pointing out the most likely flareproductive location and/or flare-associated field lines in the AR. 28