Articles | Volume 16, issue 13
Model description paper
11 Jul 2023
Model description paper |  | 11 Jul 2023

Deep learning for stochastic precipitation generation – deep SPG v1.0

Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-163', Anonymous Referee #1, 20 Sep 2022
    • AC1: 'Reply on RC1', Leroy Bird, 03 Mar 2023
  • RC2: 'Comment on gmd-2022-163', Anonymous Referee #2, 06 Jan 2023
    • AC2: 'Reply on RC2', Leroy Bird, 03 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Leroy Bird on behalf of the Authors (31 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (04 May 2023) by Travis O'Brien
RR by Christopher Paciorek (16 May 2023)
ED: Publish subject to technical corrections (18 May 2023) by Travis O'Brien
AR by Leroy Bird on behalf of the Authors (26 May 2023)  Manuscript 
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.