Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-577-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-577-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2022
Research article |
| 25 Feb 2022
| 25 Feb 2022
Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance
Jussi Leinonen
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Ulrich Hamann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
Urs Germann
Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
John R. Mecikalski
Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama, USA
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Short summary
We evaluate the usefulness of different data sources and variables to the short-term prediction (
nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
We evaluate the usefulness of different data sources and variables to the short-term prediction...
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Final-revised paper
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