SPEI (R package)
Open Source
The reference R implementation of SPEI and SPI from the index's own authors.
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NEOPRENE
ActiveNeyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
by IHCantabria
- Pricing
- Open Source
- License
- GPL-3.0
- Commercial use
- Permitted
- Ecosystem
- Python, Jupyter
- Platform
- Windows, macOS, Linux
Install
pip install NEOPRENEAbout 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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