Lifetime nowcasting of thunderstorms over Germany using a multi-source data-based fuzzy-logic approach

Due to technical progress in sensor technology and computer power, observation and model data are operationally available today with a high spatial and temporal resolution, suitable for thunderstorm detection and prediction. However, nowcasting the remaining lifetime of an observed thunderstorm is still a challenge to date. To improve nowcasting of deep convective cells, we developed the algorithm LOC-lifetime that predicts the remaining lifetime of thunderstorms based on life-cycle signatures present in satellite, radar, lightning and numerical weather prediction model data. We use the mathematical method “fuzzy logic” to combine this multi-source input and to categorize the thunderstorm evolution into the life-cycle sets growth and decay. We analyzed a data set of almost 1,800 thunderstorms that occurred during the summer months June 2016, May, June, July 2017 and June 2018. The data reveal highly variable life cycles which make it difficult to predict the remaining lifetime on basis of life-cycle statistics. Nevertheless, LOC-lifetime offers an improved nowcasting quality compared to a simpler nowcasting method as it increases the probability of correct prediction and reduces the root-mean-square error. Therefore, we propose the lifetime prediction via LOC-lifetime as a useful tool in combination with other existing algorithms to nowcast and forecast thunderstorms.