Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 3
Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1153-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1153-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 3
Development and technical paper
 | 
12 Feb 2024
Development and technical paper |  | 12 Feb 2024

GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou

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Latest update: 08 May 2024
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Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
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