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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 6
Geosci. Model Dev., 7, 3135–3151, 2014
https://doi.org/10.5194/gmd-7-3135-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 3135–3151, 2014
https://doi.org/10.5194/gmd-7-3135-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 19 Dec 2014

Model description paper | 19 Dec 2014

MeteoIO 2.4.2: a preprocessing library for meteorological data

M. Bavay1 and T. Egger2 M. Bavay and T. Egger
  • 1WSL Institute for Snow and Avalanche Research SLF, Flüelastrasse 11, 7260 Davos Dorf, Switzerland
  • 2Egger Consulting, Postgasse 2, 1010 Vienna, Austria

Abstract. Using numerical models which require large meteorological data sets is sometimes difficult and problems can often be traced back to the Input/Output functionality. Complex models are usually developed by the environmental sciences community with a focus on the core modelling issues. As a consequence, the I/O routines that are costly to properly implement are often error-prone, lacking flexibility and robustness. With the increasing use of such models in operational applications, this situation ceases to be simply uncomfortable and becomes a major issue.

The MeteoIO library has been designed for the specific needs of numerical models that require meteorological data. The whole task of data preprocessing has been delegated to this library, namely retrieving, filtering and resampling the data if necessary as well as providing spatial interpolations and parameterizations. The focus has been to design an Application Programming Interface (API) that (i) provides a uniform interface to meteorological data in the models, (ii) hides the complexity of the processing taking place, and (iii) guarantees a robust behaviour in the case of format errors, erroneous or missing data. Moreover, in an operational context, this error handling should avoid unnecessary interruptions in the simulation process.

A strong emphasis has been put on simplicity and modularity in order to make it extremely easy to support new data formats or protocols and to allow contributors with diverse backgrounds to participate. This library is also regularly evaluated for computing performance and further optimized where necessary. Finally, it is released under an Open Source license and is available at http://models.slf.ch/p/meteoio.

This paper gives an overview of the MeteoIO library from the point of view of conceptual design, architecture, features and computational performance. A scientific evaluation of the produced results is not given here since the scientific algorithms that are used have already been published elsewhere.

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The open-source MeteoIO library has been designed to perform the data preprocessing required by numerical models using large meteorological data sets, with a strong emphasis on simplicity and modularity. It retrieves, filters and resamples the data if necessary as well as provides spatial interpolations and parameterizations. It presents a uniform interface to meteorological data in the models, hides the complexity of the preprocessing and guarantees a robust behaviour in case of data errors.
The open-source MeteoIO library has been designed to perform the data preprocessing required by...
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