NEOPRENE
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
Reference Python implementations of SPI, SPEI and Palmer indices for drought monitoring.
Install
pip install climate-indicesThe climate_indices package provides carefully validated Python implementations of the standard drought indices — SPI and SPEI (with proper distribution fitting), PET methods, PNP and the Palmer suite — designed for both single-station series and gridded NetCDF processing from the command line.
Drought indices are easy to implement subtly wrongly (fitting, calibration periods, zero-precipitation handling); this package exists so agencies and researchers converge on defensible numbers. It has been used in NOAA and NIDIS-adjacent monitoring contexts, which is exactly the pedigree a compliance-facing drought study wants.
Other climate & change analysis tools covering similar workflow stages.
Neyman-Scott stochastic rainfall generation in Python — synthetic series and disaggregation, single or multi-site.
The reference R implementation of SPEI and SPI from the index's own authors.
EPA's Climate Adjustment Tool for SWMM — apply downscaled climate projections to rainfall and evaporation inputs.
Programmatic access to ERA5 and the Copernicus Climate Data Store — the world's reanalysis workhorse.
Statistical downscaling and bias correction of climate projections in R — part of the climate4R framework.
Climate indices and bias adjustment on xarray — the operational-grade library for climate-data engineering.