Enlarging the Severe Hail Database in Finland by Using a Radar-Based Hail Detection Algorithm and Email Surveys to Limit Underreporting and Population Biases

Abstract

Abstract Collecting hail reports to build a climatology is challenging in a sparsely populated country such as Finland. To expand an existing database, a new approach involving daily verification of a radar- and numerical weather prediction–based hail detection algorithm was trialed during late May–August for the 10-yr period, 2008–17. If the algorithm suggested a high likelihood of hail from each identified convective cell in specified locations, then an email survey was sent to people and businesses in these locations. Telephone calls were also used occasionally. Starting from 2010, the experiment was expanded to include trained storm spotters performing the surveys (project called TATSI). All the received hail reports were documented (severe or ≥2 cm, and nonsevere, excluding graupel), giving a more complete depiction of hail occurrence in Finland. In combination with reports from the general public, news, and social media, our hail survey resulted in a 292% increase in recorded severe hail days and a 414% increase in observed severe hail cases compared to a climatological study (1930–2006). More than 2200 email surveys were sent, and responses to these surveys accounted for 53% of Finland’s severe hail cases during 2008–17. Most of the 2200 emails were sent into rural locations with low population density. These additional hail reports allowed problems with the initial radar-based hail detection algorithm to be identified, leading to the introduction of a new hail index in 2009 with improved detection and nowcasting of severe hail. This study shows a way to collect hail reports in a sparsely populated country to mitigate underreporting and population biases.