Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1631-2022
https://doi.org/10.5194/hess-26-1631-2022
Research article
 | 
25 Mar 2022
Research article |  | 25 Mar 2022

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Bora Shehu and Uwe Haberlandt

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-248', Ruben Imhoff, 27 May 2021
    • AC1: 'Reply on RC1', Bora Shehu, 02 Sep 2021
  • RC2: 'Comment on hess-2021-248', Seppo Pulkkinen, 04 Jul 2021
    • AC2: 'Reply on RC2', Bora Shehu, 02 Sep 2021
  • RC3: 'Comment on hess-2021-248', Georgy Ayzel, 09 Jul 2021
    • AC3: 'Reply on RC3', Bora Shehu, 02 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) (16 Sep 2021) by Nadav Peleg
AR by Bora Shehu on behalf of the Authors (30 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (03 Dec 2021) by Nadav Peleg
RR by Ruben Imhoff (09 Dec 2021)
RR by Seppo Pulkkinen (06 Jan 2022)
ED: Publish subject to minor revisions (review by editor) (07 Jan 2022) by Nadav Peleg
AR by Bora Shehu on behalf of the Authors (17 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (19 Jan 2022) by Nadav Peleg
Download
Short summary
In this paper we investigate whether similar storms behave similarly and whether the information obtained from past similar storms can improve storm nowcast based on radar data. Here a nearest-neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably, especially for convective storm nowcast.