Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1153-2024
https://doi.org/10.5194/gmd-17-1153-2024
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

Data sets

Test cases for the Geospatial Probabilistic Estimation Package (GPEP) Guoqiang Tang and Andy Wood https://doi.org/10.5281/zenodo.8222851

Model code and software

NCAR/GPEP: Version 1.0.0-alpha release Guoqiang Tang and Andy Wood https://doi.org/10.5281/zenodo.8223174

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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.