pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Boateng, Daniel; Mutz, Sebastian G.

doi:https://doi.org/10.5194/gmd-16-6479-2023

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.

https://doi.org/10.5194/gmd-16-6479-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 22

Model description paper

|

14 Nov 2023

Model description paper |

| 14 Nov 2023

pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Daniel Boateng and Sebastian G. Mutz

Data sets

Sub-catchment datasets Deutscher Wetterdienst https://cdc.dwd.de/portal/202209231028/mapview

ERA5 monthly averaged data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.6860a573

ERA5-Land monthly averaged data from 1950 to present J. Muñoz Sabater https://doi.org/10.24381/cds.68d2bb30

Supporting material for PyESDv1.0.1 An open-source Python framework for empirical-statistical downscaling of climate information Daniel Boateng and Sebastian G. Mutz https://doi.org/10.5281/zenodo.7767681

CMIP5 monthly data on pressure levels Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.3b4b5bc9

CMIP5 monthly data on single levels Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.9d44a987

Model code and software

Dan-Boat/PyESD: PyESDv1.0.1 (v1.0.1) Daniel Boateng https://doi.org/10.5281/zenodo.7767629

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.