Preprints
https://doi.org/10.5194/gmd-2023-172
https://doi.org/10.5194/gmd-2023-172
Submitted as: development and technical paper
 | 
18 Aug 2023
Submitted as: development and technical paper |  | 18 Aug 2023
Status: this preprint is currently under review for the journal GMD.

GPEP v1.0: a Geospatial Probabilistic Estimation Package to support Earth Science applications

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

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 a 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.

Guoqiang Tang et al.

Status: open (until 13 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-172', Anonymous Referee #1, 18 Sep 2023 reply
  • RC2: 'Comment on gmd-2023-172', Anonymous Referee #2, 29 Sep 2023 reply
  • RC3: 'Comment on gmd-2023-172', Anonymous Referee #3, 03 Oct 2023 reply

Guoqiang Tang et al.

Guoqiang Tang et al.

Viewed

Total article views: 341 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
237 95 9 341 3 2
  • HTML: 237
  • PDF: 95
  • XML: 9
  • Total: 341
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 18 Aug 2023)
Cumulative views and downloads (calculated since 18 Aug 2023)

Viewed (geographical distribution)

Total article views: 329 (including HTML, PDF, and XML) Thereof 329 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Oct 2023
Download
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 a 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.