Experiences with >50,000 Crowdsourced Hail Reports in Switzerland

摘要

Crowdsourcing is an observational method that has gained increasing popularity in recent years. In hail research, crowdsourced reports bridge the gap between heuristically defined radar hail algorithms, which are automatic and spatially and temporally widespread, and hail sensors, which provide precise hail measurements at fewer locations. We report on experiences with and first results from a hail size reporting function in the app of the Swiss National Weather Service. App users can report the presence and size of hail by choosing a predefined size category. Since May 2015, the app has gathered >50,000 hail reports from the Swiss population. This is an unprecedented wealth of data on the presence and approximate size of hail on the ground. The reports are filtered automatically for plausibility. The filters require a minimum radar reflectivity value in a neighborhood of a report, remove duplicate reports and obviously artificial patterns, and limit the time difference between the event and the report submission time. Except for the largest size category, the filters seem to be successful. After filtering, 48% of all reports remain, which we compare against two operationally used radar hail detection and size estimation algorithms, probability of hail (POH) and maximum expected severe hail size (MESHS). The comparison suggests that POH and MESHS are defined too restrictively and that some hail events are missed by the algorithms. Although there is significant variability between size categories, we found a positive correlation between the reported hail size and the radar-based size estimates.