Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5035-2023
https://doi.org/10.5194/gmd-16-5035-2023
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

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Latest update: 23 May 2024
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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.