A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics, and Performance Results for Operational Solar Flare Forecasting Systems

作者：K. D. Leka, Sung‐Hong Park, K. Kusano, Jesse Andries, G. Barnes, Suzy Bingham, D. Shaun Bloomfield, Aoife E. McCloskey, Véronique Delouille, D. A. Falconer, P. T. Gallagher, Manolis K. Georgoulis, Yûki Kubo, Kangjin Lee, Sang‐Woo Lee, V. V. Lobzin, JunChul Mun, Sophie A. Murray, Tarek A. M. Hamad Nageem, Rami Qahwaji, Michael Sharpe, R. A. Steenburgh, Graham Steward, Michael Terkildsen · 发表于：The Astrophysical Journal Supplement Series · 年份：2019 · DOI：10.3847/1538-4365/ab2e12 · 被引用次数：120 · 研究领域：Solar and Space Plasma Dynamics、Solar Radiation and Photovoltaics、Oil, Gas, and Environmental Issues

Abstract Solar flares are extremely energetic phenomena in our solar system. Their impulsive and often drastic radiative increases, particularly at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand solar flares to the point of being able to forecast them. As data and algorithms improve dramatically, questions must be asked concerning how well the forecasting performs; crucially, we must ask how to rigorously measure performance in order to critically gauge any improvements. Building upon earlier-developed methodology of Paper I (Barnes et al. 2016), international representatives of regional warning centers and research facilities assembled in 2017 at the Institute for Space-Earth Environmental Research, Nagoya University, Japan to, for the first time, directly compare the performance of operational solar flare forecasting methods. Multiple quantitative evaluation metrics are employed, with the focus and discussion on evaluation methodologies given the restrictions of operational forecasting. Numerous methods performed consistently above the “no-skill” level, although which method scored top marks is decisively a function of flare event definition and the metric used; there was no single winner. Following in this paper series, we ask why the performances differ by examining implementation details (Leka et al. 2019), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in Pa...