the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Nathalie Rombeek
Jussi Leinonen
Ulrich Hamann
Abstract. Severe convective weather events, such as hail, lightning and heavy rainfall pose a great threat to humans and cause a considerable amount of economic damage. Nowcasting convective storms can provide warning signals and mitigate the impact of these storms. Dual-polarization weather radars are a crucial source of information for nowcasting severe convective events; nevertheless, they are most often not considered in nowcasting. These radars provide signatures of different hydrometeors. This work presents the importance of polarimetric variables as an additional data source for nowcasting thunderstorm hazards using an existing neural network architecture with convolutional and recurrent layers. This network has a common framework, which enables nowcasting of hail, lightning and heavy rainfall for lead times up to 60 min with a 5 min resolution. The study area is covered by the Swiss operational radar network, which consists of five operational polarimetric C-band radars. Results indicate that including polarimetric variables and quality indices improve the accuracy of nowcasting heavy precipitation and lightning, with the largest improvement found for heavy precipitation.
Nathalie Rombeek et al.
Status: open (until 29 Jun 2023)
Nathalie Rombeek et al.
Data sets
Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann https://doi.org/10.5281/zenodo.7760740
Model code and software
GitHub repository c4dl-polar Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann https://github.com/MeteoSwiss/c4dl-polar/
Nathalie Rombeek et al.
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