Articles | Volume 16, issue 17
Model description paper
01 Sep 2023
Model description paper |  | 01 Sep 2023

NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1104', Anonymous Referee #1, 13 Mar 2023
    • AC2: 'Reply on RC1', Manuel del Jesus, 21 Apr 2023
  • CEC1: 'Comment on egusphere-2022-1104', Astrid Kerkweg, 14 Mar 2023
    • AC1: 'Reply on CEC1', Manuel del Jesus, 19 Apr 2023
  • RC2: 'Comment on egusphere-2022-1104', Anonymous Referee #2, 04 May 2023
    • AC3: 'Reply on RC2', Manuel del Jesus, 08 May 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Manuel del Jesus on behalf of the Authors (09 May 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Reconsider after major revisions (16 May 2023) by Taesam Lee
ED: Referee Nomination & Report Request started (14 Jun 2023) by Taesam Lee
RR by Anonymous Referee #1 (17 Jun 2023)
ED: Publish as is (17 Jul 2023) by Taesam Lee
AR by Manuel del Jesus on behalf of the Authors (18 Jul 2023)
Short summary
NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.