Statistical relevance of meteorological ambient conditions and cell attributes for nowcasting the life cycle of convective storms

Abstract

The usually short lifetime of convective storms and their rapid development during unstable weather conditions makes forecasting these storms challenging. It is necessary, therefore, to improve the procedures for estimating the storms' expected life cycles, including the storms' lifetime, size, and intensity development. We present an analysis of the life cycles of convective cells in Germany, focusing on the relevance of the prevailing atmospheric conditions. Using data from the radar‐based cell detection and tracking algorithm KONRAD of the German Weather Service, the life cycles of isolated convective storms are analysed for the summer half‐years from 2011 to 2016. In addition, numerous convection‐relevant atmospheric ambient variables (e.g., deep‐layer shear, convective available potential energy, lifted index), which were calculated using high‐resolution COSMO‐EU assimilation analyses (0.0625°), are combined with the life cycles. The statistical analyses of the life cycles reveal that rapid initial area growth supports wider horizontal expansion of a cell in the subsequent development and, indirectly, a longer lifetime. Specifically, the information about the initial horizontal cell area is the most important predictor for the lifetime and expected maximum cell area during the life cycle. However, its predictive skill turns out to be moderate at most, but still considerably higher than the skill of any ambient variable is. Of the latter, measures of midtropospheric mean wind and vertical wind shear are most suitable for distinguishing between convective cells with short lifetime and those with long lifetime. Higher thermal instability is associated with faster initial growth, thus favouring larger and longer living cells. A detailed objective correlation analysis between ambient variables, coupled with analyses discriminating groups of different lifetime and maximum cell area, makes it possible to gain new insights into their statistical connections. The results of this study provide guidance for predictor selection and advancements of nowcasting applications.