Articles | Volume 16, issue 22
https://doi.org/10.5194/gmd-16-6479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-6479-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Nov 2023
Model description paper |
| 14 Nov 2023
| 14 Nov 2023
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Daniel Boateng
CORRESPONDING AUTHOR
Department of Geosciences, University of Tübingen, Tübingen, Germany
Sebastian G. Mutz
Department of Geosciences, University of Tübingen, Tübingen, Germany
School of Geographical and Earth Sciences, University of Glasgow, Scotland, UK
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Short summary
We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
We present an open-source Python framework for performing empirical-statistical downscaling of...
