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NEOPRENE

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Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.

by IHCantabria

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Pricing
Open Source
License
GPL-3.0
Commercial use
Permitted
Ecosystem
Python, Jupyter
Platform
Windows, macOS, Linux

Install

pip install NEOPRENE

About NEOPRENE

NEOPRENE (Neyman-Scott Process Rainfall Emulator), from IHCantabria (Javier Diez-Sierra, Salvador Navas, Manuel del Jesus), implements the Neyman-Scott Rectangular Pulses model for synthetic rainfall: calibrate to observed statistics, then generate arbitrarily long single-site (NSRPM) or spatially consistent multi-site (STNSRPM) rainfall series that preserve the extremes and intermittency structure design work cares about.

Its natural jobs are continuous-simulation inputs longer than the observed record, temporal disaggregation, and stochastic downscaling of climate scenarios. GPL-3 licensed with notebook-based examples, and unusually approachable for this class of model — there are even packaged installers with JupyterLab bundled.

Where it fits in the workflow

TerrainCatchmentRainfallRunoffRoutingHydraulicsMappingReporting

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