Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
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,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-01-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/11/213/2018/gmd-11-213-2018.pdf |
| Summary: | 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.
<br><br>
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.
<br><br>
Library code, verification tests, and examples available in the repository at
<a href="https://github.com/shingleproject/Shingle" target="_blank">https://github.com/shingleproject/Shingle</a>. Further details of the
project presented at <a href="http://shingleproject.org" target="_blank">http://shingleproject.org</a>.</p> |
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| ISSN: | 1991-959X 1991-9603 |