Preprints
https://doi.org/10.5194/egusphere-2022-1104
https://doi.org/10.5194/egusphere-2022-1104
27 Feb 2023
 | 27 Feb 2023
Status: this preprint is open for discussion.

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

Abstract. Long time series of rainfall at different levels of aggregation (daily or hourly in most cases) constitute the basic input for hydrological, hydraulic and climate studies. However, often times the length, completeness, time resolution or spatial coverage of the available records fall short of the minimum requirements to build robust estimations. Here, we introduce NEOPRENE, a Python library to generate synthetic time series of rainfall. NEOPRENE simulates multi-site synthetic rainfall that reproduces observed statistics at different time aggregations. Three case studies exemplify the use of the library, focusing on extreme rainfall, as well as on dissagregating daily rainfall observations into hourly rainfall records. NEOPRENE is distributed from GitHub with an open license (GPLv3), free for research and commercial purposes alike. We also provide Jupyter notebooks with the example uses cases to promote its adoption by researchers and practitioners involved in vulnerability, impact and adaptation studies.

Javier Diez-Sierra et al.

Status: open (until 24 Apr 2023)

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 reply
  • CEC1: 'Comment on egusphere-2022-1104', Astrid Kerkweg, 14 Mar 2023 reply

Javier Diez-Sierra et al.

Model code and software

NEOPRENE Javier Diez-Sierra, Salvador Navas and Manuel del Jesus https://github.com/IHCantabria/NEOPRENE

Javier Diez-Sierra et al.

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Short summary
NEOPRENE is an open source freely available library allowing scientist and practitioners to generate synthetic time series and maps of rainfall. These outputs will us help explore plausible events, never observed in the past, that 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.