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

Abstract

Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.