Can Convective Initiation Provide Indicators for Convective Severity?

Thunderstorms pose a large risk for human safety and can strongly affect various economic sectors by their ground effects such as flash floods, hail, and lightning occurring during severe thunderstorm events. With a reliable thunderstorm forecast, people and vulnerable sectors can be warned of this danger in advance through warning systems. A thunderstorm forecast based on NWP models is not sufficient since convection develops quickly and on a subgrid-scale and cannot be fully resolved. To achieve a more reliable warning system for the next few hours, nowcasting applications based on observations are of particular interest. State-of-the-art nowcasting systems based on radar data have their limitations as convective cells cannot be detected before the onset of precipitation. An improvement of lead time for convective cell detection can potentially be achieved through geostationary satellite information, which is the focus of the present study. Satellite-derived cloud properties are calculated along storm tracks, and their correlation with the subsequently derived convective severity is analyzed. 28 convective cells over Germany in 2021 are examined and used to conduct a correlation analysis focusing on the strength and significance of the correlations. Based on these correlations, linear regression models are trained and verified by using an additional validation set consisting of eight cases. Using the multi-spectral radiance data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite, dynamical and microphysical cloud-top properties are derived to define the convective state during convective initiation (CI). Convective severity is assessed by precipitation properties determined from radar data from the Radar Climatology (RADKLIM) of the German Weather Service (DWD), as well as lightning properties derived from lightning data from Vaisala. The lead time of the cell detection based on these geostationary satellite data is found to depend on the threshold for the detection process. An improvement against radar data can be seen for a brightness temperature threshold of 260 K. Maximum 5 minute precipitation intensity shows significant correlations with CI conditions, and can be well predicted with a mean absolute error (MAE) of 0.24 mm. With a significant Pearson-R of -0.88, the maximum lightning amplitude also shows good predictive skill with a MAE of 44 kA. The predictions of the time of maximum precipitation intensity, time of maximum lightning frequency, and time of first lightning are associated with high uncertainties and no significant correlation. These results show that geostationary satellite data can provide an earlier detection of convective cells. Furthermore, it has the potential of providing information on the future development of convective cells. All in all, the integration of satellite-based information in nowcasting systems can enable more reliable early warnings and an enhanced assessment of the severe weather potential of thunderstorms.