Hail hazard estimation over Italy with a combination of high-resolution reanalysis, overshooting top detections and lightning data

Hail is one of the most hazardous perils for agriculture, infrastructures, and economy among severe weather events related to deep moist convection. The frequency and severity of severe hailstorms is increasing throughout all Europe with the highest potential to worsen expected over Italy. Hence it is becoming increasingly important to enhance our knowledge of hailstorms to limit and prevent major damages in the most affected areas. However, due to the intrinsic difficulties in systematically observing and simulating hail events, their scientific understanding is still very limited. While direct hail observations are strongly heterogeneous, temporally limited and scarce, numerical simulations lack a sufficient level of detail to properly represent strongly-localized and rapidly-evolving high-impact weather events. Furthermore, the very complex dynamics governing hailstorms prevent the simulation of direct model estimates for hail. Hail probability can be indirectly assessed considering a set of meteorological parameters describing dynamical and thermodynamical characteristics of convective environments prone to hail development. For these reasons the new high-resolution reanalysis dataset SPHERA (High rEsolution ReAnalysis over Italy), developed at ARPAE-SIMC, is considered for investigating hail-favouring environments over Italy. Produced as a dynamical downscaling of the global reanalysis ERA5 (ECMWF), SPHERA is based on the model COSMO at the convection-permitting horizontal resolution of 2.2 km, and provides hourly meteorological products. The high level of detail of SPHERA is expected to enhance the representation of the key ingredients describing hailstorm potential compared to coarser and convection-parametrized datasets that have been employed up to now. Anyhow, these convective parameters alone can not be sufficient for reliably retrieving hail probability, but they must be combined with the available hail information coming from observed data. A major source of information are remote sensing observations, especially Overshooting cloud Top (OT) satellite detections, that recently have proven to be of great potential in discerning hail occurrence, as well as lightning strikes data presenting an intensification in the flash rates (i.e. Lightning Jump (LJ)). In this study OT detections from the geostationary Meteosat Second Generation SEVIRI infrared images are considered, while the LJ index data are obtained from the LAMPINET lightning detection dataset over Italy. Based on the numerical proxies describing hailstorm environments, a filter is built and tuned to retain OT and LJ occurrences related to hail presence on the ground. These are subsequently validated by considering a set of direct hail observations coming from ESWD (European Severe Weather Database) and claims to national insurance companies for hail damage. This method has already provided useful new insights in spatial hail climatologies over Europe, Australia and South Africa, and is expected to benefit even more when considering new advances in numerical modeling and automatic OT detection algorithms, and including additional lightning strikes data.