Articles | Volume 22, issue 2
Research article
25 Feb 2022
Research article |  | 25 Feb 2022

Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance

Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-171', Tomeu Rigo, 07 Aug 2021
    • AC1: 'Reply on RC1', Jussi Leinonen, 24 Sep 2021
  • RC2: 'Comment on nhess-2021-171', Anonymous Referee #2, 16 Aug 2021
    • AC2: 'Reply on RC2', Jussi Leinonen, 24 Sep 2021
  • RC3: 'Comment on nhess-2021-171', Anonymous Referee #3, 16 Aug 2021
    • AC3: 'Reply on RC3', Jussi Leinonen, 27 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (11 Oct 2021) by Maria-Carmen Llasat
AR by Jussi Leinonen on behalf of the Authors (13 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (18 Dec 2021) by Maria-Carmen Llasat
RR by Tomeu Rigo (20 Dec 2021)
RR by Anonymous Referee #2 (11 Jan 2022)
ED: Publish as is (29 Jan 2022) by Maria-Carmen Llasat
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.
Final-revised paper