Ensemble forecasting of major solar flares: First results. Issue 10 (7th October 2015)

• Record Type: Journal Article
• Authors: Guerra, J. A.; Pulkkinen, A.; Uritsky, V. M.
• Is Part Of: Space weather. Volume 13:Issue 10(2015:Oct.)
• Page Start: 626
• Page End: 642
• Publication Date: 2015-10-07
• Subjects: Flare Forecasting -- Ensemble method -- Prototype; Space environment -- Periodicals; 551.509992
• DOI: 10.1002/2015SW001195
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: We present the results from the first ensemble prediction model for major solar flares (M and X classes). The primary aim of this investigation is to explore the construction of an ensemble for an initial prototyping of this new concept. Using the probabilistic forecasts from three models hosted at the Community Coordinated Modeling Center (NASA‐GSFC) and the NOAA forecasts, we developed an ensemble forecast by linearly combining the flaring probabilities from all four methods. Performance‐based combination weights were calculated using a Monte Carlo‐type algorithm that applies a decision threshold P t h to the combined probabilities and maximizing the Heidke Skill Score (HSS). Using the data for 13 recent solar active regions between years 2012 and 2014, we found that linear combination methods can improve the overall probabilistic prediction and improve the categorical prediction for certain values of decision thresholds. Combination weights vary with the applied threshold and none of the tested individual forecasting models seem to provide more accurate predictions than the others for all values of P t h . According to the maximum values of HSS, a performance‐based weights calculated by averaging over the sample, performed similarly to a equally weighted model. The values P t h for which the ensemble forecast performs the best are 25% for M‐class flares and 15% for X‐class flares. When the human‐adjusted probabilities from NOAA are excluded from the ensemble, the ensemble performance in terms of the Heidke score is reduced. Key Point: An ensemble forecasting method improves probabilistic and categorical forecasts