Severe convective events, accompanied by strong wind gusts, high intensity precipitation and hail often cause large impacts on society. Particularly in large metropolitan areas these events can pose enormous challenges to civil protection agencies such as fire brigades. In order to maintain an effective management of fire brigade operations in advance as well as during such events it would certainly be of great value to be able to predict possible impacts on high spatial resolution, such as urban district scale or higher. However predictability of local impacts is strongly limited due to a lack of predictive skill for severe convective events as well as the dependence on non-meteorological factors such as the local exposure to specific hazards and the vulnerability of exposed assets. Based on weather related operation records of the Berlin fire brigade we investigated in how far data from state of the art nowcasting systems could potentially be used to predict local weather impacts on very short lead times. Convective cell footprints, comprising spatial information on the maximum observed radar reflectivity have been constructed. Local radar reflectivity as a predictor for the cell intensity is linked to the occurrence of fire brigade operations. A strong dependence of operation occurrences, particularly for those operations tagged with the keyword water damage, at locations affected by a thunderstorm cell is found. Within a 1x1km area and in a time interval of 6 hours after a thunderstorm event, the likelihood of a water damage to occur after a convective event has been detected is found to be roughly 4%, compared to 0.5% if the area has not been affected. The likelihood increases with exposure, based on indicators of building density derived from open street map data.
In a second study we related severe weather warnings to the search of the public for information about severe weather on the internet. Frequency of access to the warning web site of the german weather service and to articles on severe weather topics within the german Wikipedia were used as indicators of information search. After filtering for long term trends, work and school week days, models of the exponential increase of internet traffic with the amount of warning activity could be derived. Generally, information about convective weather is more frequently sought than on large scale storms. Highest interest is on the day of severe events itself, but the traffic also increases slowly on the days before, but drops of rapidly afterwards, thus nicely showing the importance of weather forecasts.