Graham Steward 1, Vasily Lobzin 1, Mike Terkildsen 1, Matt Francis 1, Iver Cairns 2 (1) Bureau of Meteorology, Space Weather Services, Sydney, Australia; (2) School of Physics, University of Sydney, Australia Flarecast 2011 (based on 2003 GONG magnetogram images) Analyzes Line-of-sight magnetograms supplied by the National Solar Observatory’s (NSO) Global Oscillation Network Group (GONG). Automatically finds: max/min B field; radial distance and latitude/longitude; Strong-gradient Polarity Inversion Lines (SPIL, B >= 0.01 G/km); and draws contour plot for region of interest. Thresholding technique a PdPd One Parameter C (complexity)76.8 L (length)81.1 XG (X gradient)73.2 YG (Y gradient)83.7 SG (summed gradients)80.5 Best of two parameters C | YG86.8 Best of three parameters (C | YG) & L88.4 Flare Potential - Neyman-Pearson criterion Probability (Pd) to predict C – class flares or greater Length of the SPIL (L) A proxy for SPIL complexity (C) Maximum north-south gradient along the SPIL (YG) Maximum east-west gradient along the SPIL (XG) Sum of gradients along the SPIL (SG) Flarecast 2015 (based on SDO magnetogram images) Parameter(s) P d for fp = 20%, Instant P d for fp = 20%, Averaged P d for fp = 10%, Instant P d for fp = 10%, Averaged 1 par, M + X par, M5 + X par, X par, M + X par, M5 + X par, X par, M + X par, M5 + X par, X ParameterP d for fp = 20%, Instant P d for fp = 20%, Averaged P d for fp = 10%, Instant P d for fp = 10%, Averaged C L XG YG SG ParameterP d for fp = 20%, Instant P d for fp = 20%, Averaged P d for fp = 10%, Instant P d for fp = 10%, Averaged C L XG YG SG Performance for M- and X-class flares ( 4847 quiet days, 214 days with flares) Performance for X-class flares ( 5038 quiet days, 47 days with flares ) Length of the SPIL (L) A proxy for SPIL complexity (C) Maximum east-west SPIL gradient (YG) Maximum north-south SPIL gradient (XG) Sum of gradients along the SPIL (SG) Maximum flux values (90th percentile) Total integrated flux Distance between opposite polarities Maximum projection of the gradient (90th percentile) Dipole direction Complexity Length of SPIL Next set of parameters to be analyzed for each active region on solar disc (not all are expected to be significant) Maximum SPIL gradient (90th percentile) Sum of gradient along SPIL Maximum N-S SPIL gradient (90th percentile) Sum of N-S gradient along SPIL Maximum E-W SPIL gradient (90th percentile) Sum of E-W gradient along SPIL Australian Space Weather Service (SWS) X-ray flare predictions are uploaded every six hours to the Global Information System Center (GISC) Melbourne, part of the World Meteorological Organisation (WMO) Information System (WIS), where it is made freely available for viewing or for subscription with automated delivery by or FTP after registering as a user (see wis.bom.gov.au, search for "solar flare"). Flare Scoreboard sponsored by the Community Coordinated Modelling Center currently uses WIS by FTP to get the SWS flare forecasts ( ccmc.gsfc.nasa.gov/challenges/flare.php ). Dissemination of flare probability forecasts Flarecast vs Culgoora Solar Observatory Theophrastus Flarecast automatically examines characteristics of physical features observed on the line-of-sight magnetogram images such as gradient along the SPIL. Furthermore, the features analysed by Flarecast are not subject to an observer's interpretation as are sunspot drawing classifications. The flare prediction system Theophrastus is a US expert system which uses the McIntosh sunspot classification system, historical flare rates, spot growth and spot activity (i.e. rotation, magnetic shear) to estimate flare probabilities (Ref. McIntosh, P.S. 1990, Sol Phys., 125,251). Culgoora forecast is based on manual sunspot analysis by the IPS solar observer which is only updated when the observatory is staffed. Auto Solar Flare Forecast Probability Model Larger & more complex sunspot groups larger and more numerous flares [Waldmeier 1938 & Giovanelli 1939] Flares occur along magnetic neutral lines separating opposite polarities of the line-of- sight magnetic field [Severny 1958] Active regions with large polarity inversion and steep field gradients produce 10 times more flares as the average group [Feldman, Hoory, Vorpahl & Zirin 1974] A region is ~2.7 times more likely to produce an M-class flare (or greater) if it produced at least one on the previous day (flares occurring prior to this have little impact) [Terkildsen 2015]. A region is ~5.4 times more likely to produce an X-class flare if it has produced an X- class flare some time over the last 3 days (flare occurrence sometime over the last three days is a better predictor of X-class flares than flare occurrence over one previous day) [Terkildsen 2015]. This is an example of a logistic regression model used to generate probabilistic forecasts for flaring activity over the next 24 hours. The model is currently being driven by active region data (magnetic class, Zurich class, sunspot number and area) as well as recent flaring history, and generates forecasts for the occurrence of flares ≥M1 and flares ≥X1 in magnitude for both discrete active regions and for the full disc. The model-based forecast is updated on the occurrence of an "event" (e.g. flare or update of classification). At 00,06,12,18UT an independent job comes along and generates the XML format for WMO Information System (WIS)/CCMC and transfers the data to WIS GISC-Melbourne where it is freely available (subscribe at wis.bom.gov.au). The next step is to incorporate new Flarecast parameters into the statistical model. Preliminary analysis has shown significant improvement in model performance when incorporating Flarecast parameters. Model improvement will be assessed on the basis of relative skill scores, reliability graphs, ROC plots and AUC. Culgoora Theophrastus predictions compared to the logistic regression model using Flarecast outputs over the same period ( ). Note high AUC is good and low log loss is good. Statistical studies The allowed rate of false positives Pf ≤ 10% per active region analyzed (Pd − Percent flares predicted correctly). Automatic Flare Forecast Modelling LR AUC.8910 Theophrastus AUC LR log loss Theophrastus log loss The performance of different thresholding techniques. The allowed rate of false positives Pf ≤ 10% and Pf ≤ 20% per active region analyzed (Pd − Percent flares predicted correctly). Flare Potential - Neyman-Pearson criterion Summary of the best results using a combination of 1, 2 and 3 parameters. wis.bom.gov.au