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Ouranosinc/xsdba

xsdba: Statistical Downscaling and Bias Adjustment library

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Development Status Project Status: Active – The project has reached a stable, usable state and is being actively developed. Build Status Coveralls

Statistical correction and bias adjustment tools for xarray.

Features

  • The xsdba submodule provides a collection of bias-adjustment methods meant to correct for systematic biases found in climate model simulations relative to observations. Almost all adjustment algorithms conform to the train - adjust scheme, meaning that adjustment factors are first estimated on training data sets, then applied in a distinct step to the data to be adjusted. Given a reference time series (ref), historical simulations (hist) and simulations to be adjusted (sim), any bias-adjustment method would be applied by first estimating the adjustment factors between the historical simulation and the observation series, and then applying these factors to sim`, which could be a future simulation:
  • Time grouping (months, day of year, season) can be done within bias adjustment methods.
  • Properties and measures utilities can be used to assess the quality of adjustments.

Quick Install

xsdba can be installed from PyPI:

$ pip install xsdba

Documentation

The official documentation is at https://xsdba.readthedocs.io/

How to make the most of xsdba: Basic Usage Examples and In-Depth Examples.

Credits

This package was created with Cookiecutter and the Ouranosinc/cookiecutter-pypackage project template.

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