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.

It is important to understand the dynamical processes that cause heat waves at regional scales. This study examined the physical mechanism that was responsible for a heat wave in South Korea in August 2016. Unlike previous August heat waves over the Korean Peninsula, the intensity of the geopotential height over the Kamchatka Peninsula in August 2016 was the strongest since 1979, which acted as an atmospheric blocking in the downstream region of the Korean Peninsula. Therefore, the anomalous high geopotential height in Mongolia, where the surface temperature was quite high, was observed persistently in August 2016. This anomalous high in Mongolia induced northerly winds with warm temperatures onto the Korean Peninsula, which contributed to a heat wave in August 2016. We further showed that the anomalous high geopotential height over the Kamchatka Peninsula in August 2016 was triggered by strong convection in the western-to-central subtropical Pacific through atmospheric teleconnections, which was quite different from a typical heat wave over the Korean Peninsula, in which convective forcing around the South China Sea is strong. This implies that convective forcing in the subtropical Pacific should also be monitored to predict heat wave events in East Asia, including South Korea. On the other hand, the zonal wave train associated with the circumglobal teleconnection pattern is also associated with the anomalous high geopotential height around Mongolia and the Kamchatka Peninsula, which may have contributed to the heat wave in August 2016.

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.