Articles | Volume 16, issue 20
Model evaluation paper
20 Oct 2023
Model evaluation paper |  | 20 Oct 2023

Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning

Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-276', Anonymous Referee #1, 25 Feb 2023
    • AC1: 'Reply on RC1', Daehyeon Han, 06 Aug 2023
  • RC2: 'Comment on gmd-2022-276', Anonymous Referee #2, 26 Apr 2023
    • AC2: 'Reply on RC2', Daehyeon Han, 06 Aug 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Daehyeon Han on behalf of the Authors (17 Aug 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Aug 2023) by Nicola Bodini
RR by Anonymous Referee #2 (01 Sep 2023)
ED: Publish subject to technical corrections (11 Sep 2023) by Nicola Bodini
AR by Daehyeon Han on behalf of the Authors (17 Sep 2023)  Manuscript 
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.