Abstract
In the framework of the Space Situational Awareness program of the European Space Agency (ESA/SSA), an automatic flare detection system was developed at Kanzelhöhe Observatory (KSO). The system has been in operation since mid-2013. The event detection algorithm was upgraded in September 2017. All data back to 2014 was reprocessed using the new algorithm. In order to evaluate both algorithms, we apply verification measures that are commonly used for forecast validation. In order to overcome the problem of rare events, which biases the verification measures, we introduce a new event-based method. We divide the timeline of the Hupalpha observations into positive events (flaring period) and negative events (quiet period), independent of the length of each event. In total, 329 positive and negative events were detected between 2014 and 2016. The hit rate for the new algorithm reached 96% (just five events were missed) and a false-alarm ratio of 17%. This is a significant improvement of the algorithm, as the original system had a hit rate of 85% and a false-alarm ratio of 33%. The true skill score and the Heidke skill score both reach values of 0.8 for the new algorithm; originally, they were at 0.5. The mean flare positions are accurate within {pm},1 heliographic degree for both algorithms, and the peak times improve from a mean difference of 1.7pm 2.9~mbox{minutes} to 1.3pm 2.3~mbox{minutes}. The flare start times that had been systematically late by about 3 minutes as determined by the original algorithm, now match the visual inspection within -0.47pm 4.10~mbox{minutes}.
Highlights
Solar flares are sudden enhancements of radiation in a wide range of wavelengths within regions of strong magnetic fields on the Sun, the so-called active regions, which have a complex magnetic configuration (e.g. Sammis, Tang, and Zirin, 2000)
In this article we present an enhanced version of the automatic flare detection system described in Pötzi et al (2015), and we validate the detection algorithm, taking into account the fact that flares are rare events and that a normal verification scheme would be biased by this strong imbalance between positive and negative events
We expect that the results of the automatic detections are on average closer to the visual Kanzelhöhe Observatory (KSO) flare reports than the NOAA reports, as they are based on the data from the same observatory
Summary
Solar flares are sudden enhancements of radiation in a wide range of wavelengths within regions of strong magnetic fields on the Sun, the so-called active regions, which have a complex magnetic configuration (e.g. Sammis, Tang, and Zirin, 2000). The flare energy is converted into the acceleration of high-energy particles, mass motions, and heating of the solar plasma (e.g. reviews by Priest and Forbes, 2002; Benz, 2017) They are well observable optically from ground-based observatories (e.g. review by Veronig and Pötzi, 2016). The detection methods range from comparatively simple image recognition methods based on intensity variations derived from running-difference images (Piazzesi et al, 2012), region-growing and edge-based techniques (Veronig et al, 2000), to more complex algorithms using machine learning (Fernandez Borda et al, 2002; Ahmed et al, 2013), or support vector machine classifiers (Qu et al, 2003). These methods have been applied to space-borne image sequences in the extreme ultraviolet (EUV) and soft X-ray range (e.g. Qahwaji, Ahmed, and Colak, 2010; Bonte et al, 2013), and to ground-based Hα filtergrams (e.g. Veronig et al, 2000; Kirk et al, 2013; Pötzi et al, 2015)
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