Articles | Volume 16, issue 17
https://doi.org/10.5194/gmd-16-5035-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
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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,
https://doi.org/10.1007/s00477-018-1613-2, 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,
https://doi.org/10/b8532z, 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,
https://doi.org/10.1016/j.jhydrol.2009.10.031, 2010. a
Cowpertwait, P. S. P.: Further Developments of the Neyman-scott Clustered Point
Process for Modeling Rainfall, Water Resour. Res., 27, 1431–1438,
https://doi.org/10.1029/91WR00479, 1991. a, b, c, d