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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 12
Geosci. Model Dev., 10, 4619-4646, 2017
https://doi.org/10.5194/gmd-10-4619-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 10, 4619-4646, 2017
https://doi.org/10.5194/gmd-10-4619-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 19 Dec 2017

Methods for assessment of models | 19 Dec 2017

A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)

David Hassell1, Jonathan Gregory1,2, Jon Blower3, Bryan N. Lawrence1, and Karl E. Taylor4 David Hassell et al.
  • 1National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK
  • 2Met Office Hadley Centre, Exeter, Exeter, UK
  • 3Institute for Environmental Analytics, University of Reading, Reading, UK
  • 4Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA, USA

Abstract. The CF (Climate and Forecast) metadata conventions are designed to promote the creation, processing, and sharing of climate and forecasting data using Network Common Data Form (netCDF) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully understood and interpreted by someone familiar with the rules and relationships specified in the conventions documentation. To aid in development of CF-compliant software and to capture with a minimal set of elements all of the information contained in the CF conventions, we propose a formal data model for CF which is independent of netCDF and describes all possible CF-compliant data. Because such data will often be analysed and visualised using software based on other data models, we compare our CF data model with the ISO 19123 coverage model, the Open Geospatial Consortium CF netCDF standard, and the Unidata Common Data Model. To demonstrate that this CF data model can in fact be implemented, we present cf-python, a Python software library that conforms to the model and can manipulate any CF-compliant dataset.

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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata...
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