Evaluating the ability of numerical models to capture important shifts in environmental time series: a fuzzy change point approach

Hollaway, M.J. ORCID: https://orcid.org/0000-0003-0386-2696; Henrys, P.A. ORCID: https://orcid.org/0000-0003-4758-1482; Killick, R.; Leeson, A.; Watkins, J. ORCID: https://orcid.org/0000-0002-3518-8918. 2021 Evaluating the ability of numerical models to capture important shifts in environmental time series: a fuzzy change point approach. Environmental Modelling & Software, 139, 104993. 10, pp. 10.1016/j.envsoft.2021.104993

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Official URL: http://dx.doi.org/10.1016/j.envsoft.2021.104993

Abstract/Summary

Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations. The fuzzy union based metric factors in uncertainty of the changepoint location to calculate individual similarity scores between the numerical model and reality for each changepoint in the observed record. The application of the method is demonstrated through a case study on a high resolution model dataset which was able to pick up observed changepoints in temperature records over Greenland to varying degrees of success. The case study is presented using the DataLabs framework, a cloud-based collaborative platform which simplifies access to complex statistical methods for environmental science applications.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.envsoft.2021.104993
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-) Soils and Land Use (Science Area 2017-)
ISSN:	1364-8152
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	changepoints, fuzzy-logic, data science, uncertainty, evaluation framework
NORA Subject Terms:	Data and Information
Date made live:	15 Mar 2021 14:25 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529832

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.envsoft.2021.104993

UKCEH and CEH Sections/Science Areas:

Pollution (Science Area 2017-)
Soils and Land Use (Science Area 2017-)

ISSN:

1364-8152

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

changepoints, fuzzy-logic, data science, uncertainty, evaluation framework