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| Titel |
MeteoIO 2.4.2: a preprocessing library for meteorological data |
| VerfasserIn |
M. Bavay, T. Egger |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 6 ; Nr. 7, no. 6 (2014-12-19), S.3135-3151 |
| Datensatznummer |
250115803
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| Publikation (Nr.) |
 copernicus.org/gmd-7-3135-2014.pdf |
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| Zusammenfassung |
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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