This paper studies a damaging hail storm that occurred on 6 June 2015 in the complex topography of Switzerland. The storm persisted for several hours and produced large hail resulting in significant damage.Storms of comparable severity occur on average only three times per year within the entire Swiss radar domain, but are rare events at this exact location, according to a set of over 400,000 automatically identified storms. A multitude of datasets, partly novel for central Europe, is now available to study the storm in great detail capturing its impacts, severity and development. The data we use include radar-based hail products, crowd-sourced hail reports, and insurance loss data. These independent datasets permitted a verification of both hail occurrence and hail size estimations by radar. The crowd-sourced reports agree well with radar-based hail observations and insurance data. Model data (ERA-Interim reanalysis, regional COSMO-2 analysis and WRF simulations) and radio-sounding data showed, that conditions were favourable for thunderstorm development due to an unstable and moist atmosphere over Switzerland, brought about by an interplay of large-scale pattern and local processes. Advection ahead of a cold front west of Switzerland and local evapotranspiration lead to high lower-tropospheric moisture. The large-scale flow and topographically induced Alpine pumping resulted in strong directional wind shear, and contributed to the longevity and severity of this storm. The cold front was not relevant for the vertical lifting. Using model simulations with very high resolution, we identified mountain wind systems and cold-air outflow as possible triggering and propagation mechanisms of this hail storm.

Existing heat–health warning systems focus on warning vulnerable groups in order to reduce mortality. However, human health and performance are affected at much lower environmental heat strain levels than those directly associated with higher mortality. Moreover, workers are at elevated health risks when exposed to prolonged heat. This study describes the multilingual “HEAT-SHIELD occupational warning system” platform (https://heatshield.zonalab.it/) operating for Europe and developed within the framework of the HEAT-SHIELD project. This system is based on probabilistic medium-range forecasts calibrated on approximately 1800 meteorological stations in Europe and provides the ensemble forecast of the daily maximum heat stress. The platform provides a non-customized output represented by a map showing the weekly maximum probability of exceeding a specific heat stress condition, for each of the four upcoming weeks. Customized output allows the forecast of the personalized local heat-stress-risk based on workers’ physical, clothing and behavioral characteristics and the work environment (outdoors in the sun or shade), also taking into account heat acclimatization. Personal daily heat stress risk levels and behavioral suggestions (hydration and work breaks recommended) to be taken into consideration in the short term (5 days) are provided together with long-term heat risk forecasts (up to 46 days), all which are useful for planning work activities. The HEAT-SHIELD platform provides adaptation strategies for “managing” the impact of global warming.

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.