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

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Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach. / Hollaway, M. J.; Henrys, P. A.; Killick, R. et al.
In: Environmental Modelling and Software, Vol. 139, 104993, 31.05.2021.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hollaway, MJ, Henrys, PA, Killick, R, Leeson, A & Watkins, J 2021, 'Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach', Environmental Modelling and Software, vol. 139, 104993. https://doi.org/10.1016/j.envsoft.2021.104993

APA

Hollaway, M. J., Henrys, P. A., Killick, R., Leeson, A., & Watkins, J. (2021). Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach. Environmental Modelling and Software, 139, Article 104993. https://doi.org/10.1016/j.envsoft.2021.104993

Vancouver

Hollaway MJ, Henrys PA, Killick R, Leeson A, Watkins J. Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach. Environmental Modelling and Software. 2021 May 31;139:104993. Epub 2021 Feb 19. doi: 10.1016/j.envsoft.2021.104993

Author

Hollaway, M. J. ; Henrys, P. A. ; Killick, R. et al. / Evaluating the ability of numerical models to capture important shifts in environmental time series : A fuzzy change point approach. In: Environmental Modelling and Software. 2021 ; Vol. 139.

Bibtex

@article{a5a45f49df094e838fc82c0a60d5b338,
title = "Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach",
abstract = "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.",
keywords = "Changepoints, Data science, Evaluation framework, Fuzzy-logic, Uncertainty",
author = "Hollaway, {M. J.} and Henrys, {P. A.} and R. Killick and A. Leeson and J. Watkins",
year = "2021",
month = may,
day = "31",
doi = "10.1016/j.envsoft.2021.104993",
language = "English",
volume = "139",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Evaluating the ability of numerical models to capture important shifts in environmental time series

T2 - A fuzzy change point approach

AU - Hollaway, M. J.

AU - Henrys, P. A.

AU - Killick, R.

AU - Leeson, A.

AU - Watkins, J.

PY - 2021/5/31

Y1 - 2021/5/31

N2 - 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.

AB - 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.

KW - Changepoints

KW - Data science

KW - Evaluation framework

KW - Fuzzy-logic

KW - Uncertainty

U2 - 10.1016/j.envsoft.2021.104993

DO - 10.1016/j.envsoft.2021.104993

M3 - Journal article

AN - SCOPUS:85101941056

VL - 139

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

M1 - 104993

ER -