Characterisation and predictability of a strong and a weak forcing severe convective event – a multi-data approach

Abstract

Two severe summer-time convective events in Germany are investigated which can be classified by the prevailing synoptic conditions into a strong and a weak forcing case. The strong forcing case exhibits a larger scale precipitation pattern caused by frontal ascent whereas scattered convection is dominating the convective activity in the weak forcing case. Other distinguished differences between the cases are faster movement of convective cells and larger regions with significant loss mainly due to severe gusts in the strong forcing case. A comprehensive set of various observations is used to characterise the two different events. The observations include measurements from a lightning detection network, precipitation radar, geostationary satellite and weather stations, as well as information from an automated cell detection algorithm based on radar reflectivity which is combined with severe weather reports, and damage data from insurances. Forecast performance at various time scales is analysed ranging from nowcasting and warning to short-range forecasting. Various methods and models are examined, including human warnings, observation-based nowcasting algorithms and high-resolution ensemble prediction systems. The analysis shows the advantages of a multi-sensor and multi-source approach in characterising convective events and their impacts. Using data from various sources allows to combine the different strengths of observational data sets, especially in terms of spatial coverage or data accuracy, e.g. damage data from insurances provide good spatial coverage with little meteorological information while measurements at weather stations provide accurate but pointwise observations. Furthermore, using data from multiple sources allow for a better understanding of the convective life cycle. Several parameters from different instruments are shown to have a predictive skill for convective development, these include satellite-based cloud-top cooling rates as measure for intensive convective growth, 3D-radar reflectivity, mesocyclone detection from doppler radar, overshooting top detection or lightning jumps to evaluate storm intensification and formation of severe weather. This synergetic approach can help to improve nowcasting algorihtms and thus the warning process. The predictability of the analysed severe convective events differs with different types of forcing which is reflected in both, convective-scale ensemble prediction system forecasts and human weather warnings. Human warnings show larger false alarm rates in the weak forcing case. Ensemble predictions are able to capture the characteristics of the convective precipitation. The forecast skill is connected strongly to the synoptic situation and the presence of large-scale forcing increases the forecast skill. This has to be considered for potential future warn-on-forecast strategies.