Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1553-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-1553-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Mar 2021
Model description paper |
| 17 Mar 2021
| 17 Mar 2021
MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series
Lukas Hubert Leufen
CORRESPONDING AUTHOR
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
Institute of Geosciences, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
Felix Kleinert
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
Institute of Geosciences, Rhenish Friedrich Wilhelm University of Bonn, Bonn, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Research Centre Jülich, Jülich, Germany
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Short summary
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using machine learning (ML). MLAir is adaptable to a wide range of ML use cases, focusing in particular on deep learning. The user has a free hand with the ML model itself and can select from different methods during preprocessing, training, and postprocessing. MLAir offers tools to track the experiment conduction, documents necessary ML parameters, and creates a variety of publication-ready plots.
MLAir provides a coherent end-to-end structure for a typical time series analysis workflow using...
