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

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Baez-Villanueva, O. M., Zambrano-Bigiarini, M., Beck, H. E., McNamara, I., Ribbe, L., Nauditt, A., Birkel, C., Verbist, K., Giraldo-Osorio, J. D., and Thinh, N. X.: RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements, Remote Sens. Environ., 239, 111606, https://doi.org/10.1016/j.rse.2019.111606, 2020. 
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1 Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. 
Bunn, P. T. W., Wood, A. W., Newman, A. J., Chang, H.-I., Castro, C. L., Clark, M. P., and Arnold, J. R.: Improving Station-Based Ensemble Surface Meteorological Analyses Using Numerical Weather Prediction: A Case Study of the Oroville Dam Crisis Precipitation Event, J. Hydrometeorol., 23, 1155–1169, https://doi.org/10.1175/JHM-D-21-0193.1, 2022. 
Caillouet, L., Vidal, J.-P., Sauquet, E., Graff, B., and Soubeyroux, J.-M.: SCOPE Climate: a 142 year daily high-resolution ensemble meteorological reconstruction dataset over France, Earth Syst. Sci. Data, 11, 241–260, https://doi.org/10.5194/essd-11-241-2019, 2019. 
Chen, Z. and Zhong, B.: TFInterpy: A high-performance spatial interpolation Python package, SoftwareX, 20, 101229, https://doi.org/10.1016/j.softx.2022.101229, 2022. 
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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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