Using the COAsT Python package to develop a standardised validation workflow for ocean physics models

Byrne, David; Polton, Jeff ORCID: https://orcid.org/0000-0003-0131-5250; O'Dea, Enda; Williams, Joanne ORCID: https://orcid.org/0000-0002-8421-4481. 2023 Using the COAsT Python package to develop a standardised validation workflow for ocean physics models. Geoscientific Model Development, 16 (13). 3749-3764. https://doi.org/10.5194/gmd-16-3749-2023

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Official URL: http://dx.doi.org/10.5194/gmd-16-3749-2023

Abstract/Summary

Validation is one of the most important stages of a model's development. By comparing outputs to observations, we can estimate how well the model is able to simulate reality, which is the ultimate aim of many models. During development, validation may be iterated upon to improve the model simulation and compare it to similar existing models or perhaps previous versions of the same configuration. As models become more complex, data storage requirements increase and analyses improve, scientific communities must be able to develop standardised validation workflows for efficient and accurate analyses with an ultimate goal of a complete, automated validation. We describe how the Coastal Ocean Assessment Toolbox (COAsT) Python package has been used to develop a standardised and partially automated validation system. This is discussed alongside five principles which are fundamental for our system: system scaleability, independence from data source, reproducible workflows, expandable code base and objective scoring. We also describe the current version of our own validation workflow and discuss how it adheres to the above principles. COAsT provides a set of standardised oceanographic data objects ideal for representing both modelled and observed data. We use the package to compare two model configurations of the Northwest European Shelf to observations from tide gauge and profiles.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/gmd-16-3749-2023
ISSN:	1991-9603
Date made live:	10 Jul 2023 10:14 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535348

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.5194/gmd-16-3749-2023

ISSN:

1991-9603

Date made live:

10 Jul 2023 10:14 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/535348