Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning

Abstract

Abstract. This work presents the importance of polarimetric variables as an additional data source for nowcasting thunderstorm hazards using an existing neural network architecture with recurrent-convolutional layers. The model can be trained to predict different target variables, which enables nowcasting of hail, lightning, and heavy rainfall for lead times up to 60 min with a 5 min resolution, in particular. The exceedance probabilities of Swiss thunderstorm warning thresholds are predicted. This study is based on observations from the Swiss operational radar network, which consists of five operational polarimetric C-band radars. The study area of the Alpine region is topographically complex and has a comparatively very high thunderstorm activity. Different model runs using combinations of single- and dual-polarimetric radar observations and radar quality indices are compared to the reference run using only single-polarimetric observations. Two case studies illustrate the performance difference when using all predictors compared to the reference model. The importance of the predictors is quantified by investigating the final training loss of the model, with skill scores such as critical success index (CSI), precision, recall, precision–recall area under the curve, and the Shapley value. Results indicate that single-polarization radar data are the most important data source. Adding polarimetric observations improves the model performance compared to reference model in term of the training loss for all three target variables. Adding quality indices does so, too. Including both polarimetric variables and quality indices at the same time improves the accuracy of nowcasting heavy precipitation and lightning, with the largest improvement found for heavy precipitation. No improvement could be achieved for nowcasting of the probability of hail in this way.