A simple taxonomy for describing the spatio-temporal structure of environmental modelling data
Section snippets
Software availability
An xml encoding of the full Spatio-temporal Modelling Taxonomy is given in the Spatio-temporal Modelling Taxonomy github repository: https://github.com/QHarpham/Spatio-temporal-Modelling-Taxonomy.
Motivation
Three independent technical innovations help to motivate and shape the derivation of the taxonomy for describing the spatio-temporal structure of the data supporting environmental numerical modelling: common spatial structures, the Climate Science Modelling Language (CSML) and usage of adaptors in OpenMI.
Derivation
The three motivations discussed above (common spatial structures, CSML and OpenMI) adopt an implementation of Point, Polyline and Polygon in some form. The common spatial structures and OpenMI refer to these directly (with grids constructed from polygons) and CSML implements Points, Trajectories (including a time dimension to the structure) and Grids. We therefore begin with purely spatial structures in 2-dimensions with these three, adopting the terminology ‘Point’, ‘Polyline’ and ‘Polygon’,
Examples and use cases
The Spatio-temporal Modelling Taxonomy has been derived from a set of principles and has a logical structure and symmetry. However, the overriding objective is for it to relate to real-world situations and be of practical use. It may be pleasing in theory, but is it applicable in practice? Indeed, a simpler or a more complex and holistic taxonomy may be possible, but any such structured approach must be appropriately balanced between utility and completeness. Its very existence, not least any
Discussion and conclusions
This narrative has so far shown that a taxonomy built around a common pattern (see Fig. 8) can be created based on established principles and formalising commonly used terms. It has also been demonstrated to be relevant to a wide variety of environmental modelling use cases. This is not surprising since the vast majority (if not all) environmental modelling data can be said to be applicable in some way to both time and space. Basing the taxonomy around spatio-temporal structures will inevitably
Declaration of competing interest
I confirm that there is no conflict of interest concerning this publication.
Acknowledgements
This work is a further development of PhD studies undertaken at and funded by HR Wallingford as an associated research centre of the Open University (Harpham, 2019). Much of the understanding and examples used in this paper originate from the development and application of the OpenMI 2.0 standard. This is one of the results of over a decade of research by a wide variety of individuals. The projects include: HarmonIT (EC contract: EVK1-CT-2001-00090) and OpenMI-Life (Grant agreement number
References (59)
- et al.
The FUNWAVE model application and its validation using laboratory data
Coast. Eng.
(2009) - et al.
Advancing the open modelling interface (OpenMI) for integrated water resources modeling
(2018) - et al.
Position paper: open web-distributed integrated geographic modelling and simulation to enable broader participation and applications
Earth Sci. Rev.
(2020) - et al.
A Bayesian method for improving probabilistic wave forecasts by weighting ensemble members
Environ. Model. Software
(2016) - et al.
Introductory overview: the OpenMI 2.0 standard for integrating numerical models
Environ. Model. Software
(2019) - et al.
Integrated environmental modeling: a vision and roadmap for the future
Environ. Model. Software
(2013) - et al.
A component-based approach to integrated modeling in the geosciences: the design of CSDMS
Comput. Geosci.
(2013) - et al.
- et al.
The NCEP–FNMOC combined wave ensemble product expanding benefits of interagency probabilistic forecasts to the oceanic environment
(2013) Development of an ensemble prediction system for ocean surface waves in a coastal area
Ocean Dynam.
(2015)
A third-generation wave model for coastal regions, Part I, Model description and validation
J. Geophys. Res.
Performance of the ocean wave ensemble forecast at NCEP
NOAA Marine Modelling and Analysis Branch (MMAB) Technical Note No.
Teamwork-oriented integrated modeling method for geo-problem solving
Environ. Model. Software
Design and implementation of components in the earth system modeling framework
Int. J. High Perform. Comput. Appl.
Towards an operational dengue early warning system for Vietnam
CPL6: the new extensible, high performance parallel coupler for the community climate system model
Int. J. High Perform. Comput. Appl.
Development and performance of a new version of the OASIS coupler
Rehabilitation of Hartlepool town wall
Setup an hydro-meteo experiment in minutes: the DRIHM e-infrastructure for hydrometeorology research
Advanced numerical modelling of tsunami wave propagation, transformation and run-up
Proc. ICE - Eng. Comput. Mech.
Optimized generation and absorption for three-dimensional numerical wave and current facilities
J. Waterw. Port, Coast. Ocean Eng.
Consensus forecasts of modeled wave parameters
Weather Forecast.
The DRIHM project: a flexible approach to integrate HPC, grid and cloud resources for hydro-meteorological research, SC ’14
OpenMI: open modelling interface
Journal of Hydroinformatics Issue
Using Spatio-Temporal Feature Type Structures for Coupling Environmental Numerical Models to Each Other and to Data Sources
Towards standard metadata to support models and interfaces in a hydro-meteorological model chain
J. Hydroinf.
Facilitating the Re-use and Exchange of Experimental Data - Data Standards Report
The Fluid earth 2 implementation of OpenMI 2.0
J. Hydroinf.
Cited by (1)
Spatio-Temporal Conceptual Data Modeling of Urban Road Based on Geographic Information System
2021, IOP Conference Series: Earth and Environmental Science