Shingle 2.0: Generalising self-consistent and automated domain discretisation for multi-scale geophysical models

Adam S. Candy*, Julie D. Pietrzak

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
158 Downloads (Pure)


The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model- or application-specific, and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, to ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics. Library code, verification tests, and examples available in the repository at</a. Further details of the project presented at

Original languageEnglish
Pages (from-to)213-234
Number of pages22
JournalGeoscientific Model Development
Issue number1
Publication statusPublished - 17 Jan 2018


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