1. Solar Flare Forecasting from Magnetic Feature Properties Generated by the SMART Algorithm
D. Shaun Bloomfield 1,2, K. Domijan 3, P.A. Higgins 2, P.T. Gallagher 2
1 Northumbria University, UK
2 Trinity College Dublin, Ireland
3 NUI Maynooth, Ireland
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no
ESWW13 – 17th Nov 2016

2. Data Source Solar Monitor Active Region Tracking (SMART) algorithm
SOHO/MDI magnetic feature (MF) finder
similar in concept to SDO/HMI (S)HARP cut-outs
Extracts ~25 magnetic properties for each MF
association with GOES >C1.0
NOAA numbered regions and ephemeral/unspotted regions

3. Training and Testing Data
Direct comparison to Ahmed et al. (2013) Sol. Phys., 283, 157
Training (10.75 years): Apr 1996 – Dec ; Jan 2003 – Dec 2008
Testing (4 years): Jan 2001 – Dec ; Jan 2009 – Dec 2010
Marginal relevance scores found from training set
total length of neutral lines (Lnl)
max. horizontal gradient of vertical field across neutral line (Mx_Grad)
All-MF
NOAA-only
Training
Testing
> C1.0 within 24 hr
16,673
10,571
1,137
707
No-flare or < C1.0
313,617
177,380
5,272
2,789

4. Classification Rule Construction
Random draw of 100 flare (red) and 300 non-flare (black) MFs
Build linear logistic regression classifier – sigmoid surface

5. Classification Rule Variation
Repeat previous classifier construction 50 times
All-MF case has many zero values (no neutral lines in MF detection)
NOAA-only case boundaries much more variable

6. Forecast Application
Categorical forecasts reached by thresholding a classification rule
SMART MFs checked against GOES >C1.0 flares within 24 hr
Contingency tables drawn up to find categorical skill scores
All 50 classification rules applied to every test data point
i.e., 50 contingency tables at each classifier threshold value
Forecast
Flare
No-flare
Observed TP FN FP TN
TSS= TP FN+TP − FP FP+TN
HSS= TP+TN − E random N− E random

7. Forecast Performance All-MFs TSS Classifier Threshold Classif. Thresh.
(NOAA)
HSS
0.05
0.78
0.29
0.10
0.82
0.37
0.15
0.83
0.42
0.20
0.45
0.25
0.48
0.30
0.81
0.51
0.35
0.80
0.53
0.40
0.55
0.76
0.56
0.50
0.75
0.57
0.73
0.59
0.60
0.70
0.65
0.68
0.61
0.62
0.85
0.90
0.58
0.95
0.44
0.52
All-MFs
TSS
median
2.5th → 97.5th percentiles
Classifier Threshold

8. Forecast Performance All-MFs TSS NOAA-only TSS Classifier Threshold
HSS
0.05
0.78
0.09
0.29
0.13
0.10
0.82
0.50
0.37
0.34
0.15
0.83
0.61
0.42
0.46
0.20
0.65
0.45
0.53
0.25
0.64
0.48
0.56
0.30
0.81
0.62
0.51
0.58
0.35
0.80
0.59
0.40
0.57
0.55
0.76
0.54
0.75
0.73
0.60
0.70
0.68
0.39
0.52
0.36
0.32
0.47
0.85
0.28
0.44
0.90
0.24
0.38
0.95
0.18
0.33
All-MFs
TSS
median
2.5th → 97.5th percentiles
NOAA-only
TSS
Classifier Threshold

9. Ordinal log. regression
Conclusions
Data
Forecast method
TSS
Flare level
Reference
All-MF
Log. regression
0.83
>C1.0
This work
Neural network
0.64
Ahmed et al. (2013)
NOAA-only
0.65
Ordinal log. regression
C-class
Song et al. (2009)
NOAA/SWPC (human)
0.57
Crown (2012)
NOAA/SWPC (look-up)
0.45
McIntosh-Poisson
0.46
Bloomfield et al. (2012)
Very good / good performance compared to literature
No forecast improvement with more complicated models
>2 parameter logistic regression
linear classifiers on lower-dimensional projections (e.g., PCA, KPCA)
non-linear classifiers (e.g., SVM, GP)