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

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Cited articles

Alodah, A. and Seidou, O.: The Adequacy of Stochastically Generated Climate Time Series for Water Resources Systems Risk and Performance Assessment, Stoch. Env. Res. Risk A., 33, 253–269,, 2019. a
Burton, A., Kilsby, C. G., Fowler, H. J., Cowpertwait, P. S. P., and O'Connell, P. E.: RainSim: A Spatial–Temporal Stochastic Rainfall Modelling System, Environ. Modell. Softw., 23, 1356–1369,, 2008. a
Burton, A., Fowler, H. J., Blenkinsop, S., and Kilsby, C. G.: Downscaling Transient Climate Change Using a Neyman–Scott Rectangular Pulses Stochastic Rainfall Model, J. Hydrol., 381, 18–32,, 2010. a
Cowpertwait, P., Ocio, D., Collazos, G., de Cos, O., and Stocker, C.: Regionalised spatiotemporal rainfall and temperature models for flood studies in the Basque Country, Spain, Hydrol. Earth Syst. Sci., 17, 479–494,, 2013. a, b, c, d, e, f, g
Cowpertwait, P. S. P.: Further Developments of the Neyman-scott Clustered Point Process for Modeling Rainfall, Water Resour. Res., 27, 1431–1438,, 1991. a, b, c, d
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.