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1. Introduction
Thunderstorms, particularly severe events accompanied by large hail, damaging wind gusts, tornadoes, or flash floods, pose a considerable risk to society (Brooks 2013; Papagiannaki et al. 2013; Terti et al. 2017; Papagiannaki et al. 2017). Therefore, knowledge of their local climatology is not only important for weather forecasting purposes, but also for risk assessment by emergency managers or the (re)insurance industry. Another pressing question is whether such phenomena are becoming more frequent as a result of changing climate (e.g., Trapp et al. 2007; Kapsch et al. 2012; Allen et al. 2014; Seeley and Romps 2015; Gensini and Mote 2015; Allen 2018). To answer this question, a reliable record of observations over a period of many years is necessary, which is challenging, particularly where direct observations over long periods are sparse. A number of approaches have been taken in the past to tackle this issue in different regions.
A straightforward way to build a thunderstorm climatology is to use direct observations from manned weather stations, some of which offer decades of observations (even up to 100-yr periods; Changnon and Changnon 2001; Bielec-Bąkowska 2003). Important disadvantages are that human contributions to these observations introduce errors, such as inhomogeneities (Czernecki et al. 2016), and that periods for which observations are available may be intermittent. Furthermore, the spatial coverage of observing stations may be too dispersed to capture the scale of most thunderstorms. Stations also seldom offer observations of severe weather, such as tornadoes or large hail. An exception to this pattern is a Chinese dataset of hail size observations and reasonably high spatial density that has allowed for analyses of long-term trends (Xie et al. 2008; Li et al. 2016). To capture severe events occurring between manned stations, their records can be supplemented with reports from the general public. Such observations may include severe weather reports from trained spotters or from untrained individuals. Recent advances in technology have allowed the development of crowd-sourcing techniques to collect such reports (e.g., Dotzek et al. 2009; Elmore et al. 2014; Seimon et al. 2016; Holzer et al. 2017; Groenemeijer et al. 2017). While these techniques increase our ability to detect severe thunderstorms, considerable temporal and spatial inhomogeneity exists for historical severe thunderstorm observations developed in the United States, Europe,...