Solar flares are extremely energetic phenomena in our Solar System. Their impulsive, often drastic radiative increases, in particular 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 (Barnes et al. 2016, Paper I), 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 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, Paper III), and then we present a novel analysis method to evaluate temporal patterns of forecasting errors in (Park et al. 2019, Paper IV). With these works, this team presents a well-defined and robust methodology for evaluating solar flare forecasting methods in both research and operational frameworks, and today's performance benchmarks against which improvements and new methods may be compared.

Sunspot groups are the main source of solar flares, with the energy to power them being supplied by magnetic-field evolution (e.g. flux emergence or twisting/shearing). To date, few studies have investigated the statistical relation between sunspot-group evolution and flaring, with none considering evolution in the McIntosh classification scheme. Here we present a statistical analysis of sunspot groups from Solar Cycle 22, focusing on 24-hour changes in the three McIntosh classification components. Evolution-dependent C1.0, M1.0, and X1.0 flaring rates are calculated, leading to the following results: (i) flaring rates become increasingly higher for greater degrees of upward evolution through the McIntosh classes, with the opposite found for downward evolution; (ii) the highest flaring rates are found for upward evolution from larger, more complex, classes (e.g. Zurich D-and E-classes evolving upward to F-class produce C1.0 rates of 2.66 ± 0.28 and 2.31 ± 0.09 flares per 24 hours, respectively); (iii) increasingly complex classes give higher rates for all flare magnitudes, even when sunspot groups do not evolve over 24 hours. These results support the hypothesis that injection of magnetic energy by flux emergence (i.e. increasing in Zurich or compactness classes) leads to a higher frequency and magnitude of flaring.

to quantitatively compare the performance of today's operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019 we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human "forecaster in the loop"; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies.

Most solar flares originate in sunspot groups, where magnetic field changes lead to energy build-up and release. However, few flare-forecasting methods use information of sunspot-group evolution, instead focusing on static point-in-time observations. Here, a new forecast method is presented based upon the 24-hr evolution in McIntosh classification of sunspot groups. Evolution-dependent C1.0 and M1.0 flaring rates are found from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle 22; SC22) before converting to probabilities assuming Poisson statistics. These flaring probabilities are used to generate operational forecasts for sunspot groups over July 1996 to December 2008 (SC23), with performance studied by verification metrics. Major findings are: i) considering Brier skill score (BSS) for C1.0 flares, the evolution-dependent McIntosh-Poisson method (BSS evolution = 0.09) performs better than the static McIntosh-Poisson method (BSS static = −0.09); ii) low BSS values arise partly from both methods over-forecasting SC23 flares from the SC22 rates, symptomatic of C1.0 rates in SC23 being on average ≈ 80% of those in SC22 (with M1.0 being ≈ 50%); iii) applying a bias-correction factor to reduce the SC22 rates used in forecasting SC23 flares yields modest improvement in skill relative to climatology for both methods (BSS corr static = 0.09 and BSS corr evolution = 0.20) and improved forecast reliability diagrams.

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka et al. 2019a,b) in which we describe methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multi-day periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods, and conversely, of correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both