Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1553–1574, 2021
https://doi.org/10.5194/gmd-14-1553-2021
Geosci. Model Dev., 14, 1553–1574, 2021
https://doi.org/10.5194/gmd-14-1553-2021

Model description paper 17 Mar 2021

Model description paper | 17 Mar 2021

MLAir (v1.0) – a tool to enable fast and flexible machine learning on air data time series

Lukas Hubert Leufen et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lukas Hubert Leufen on behalf of the Authors (08 Jan 2021)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (11 Jan 2021) by Christoph Knote
RR by Anonymous Referee #1 (22 Jan 2021)
ED: Publish subject to minor revisions (review by editor) (22 Jan 2021) by Christoph Knote
AR by Lukas Hubert Leufen on behalf of the Authors (29 Jan 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (02 Feb 2021) by Christoph Knote
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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.