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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 in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016–2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth–Sun line. [ABSTRACT FROM AUTHOR]

A workshop was recently held at Nagoya University (2017 October 31–November 2), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today’s operational solar flare forecasting facilities. Building upon Paper I of this series, in Paper II 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, in Paper IV (Park et al. 2019) we will present a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms). Hence, most importantly, with this series of papers, we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies. [ABSTRACT FROM AUTHOR]