Articles | Volume 24, issue 1
https://doi.org/10.5194/nhess-24-133-2024
https://doi.org/10.5194/nhess-24-133-2024
Research article
 | 
19 Jan 2024
Research article |  | 19 Jan 2024

Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning

Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann

Data sets

Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning N. Rombeek et al. https://doi.org/10.5281/zenodo.7760740

Data archive for ``Seamless lightning nowcasting with recurrent-convolutional deep learning'' J. Leinonen et al. https://doi.org/10.5281/zenodo.6802292

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Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
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