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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-332
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-332
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 23 Oct 2020

Submitted as: model description paper | 23 Oct 2020

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This preprint is currently under review for the journal GMD.

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

Lukas H. Leufen1,2, Felix Kleinert1,2, and Martin G. Schultz1 Lukas H. Leufen et al.
  • 1Research Centre Jülich, Jülich Supercomputing Centre, Germany
  • 2University of Bonn, Institute of Geosciences, Germany

Abstract. With MLAir (Machine Learning on Air data) we created a software environment that simplifies and accelerates the exploration of new machine learning (ML) models for the analysis and forecasting of meteorological and air quality time series. Thereby MLAir is not developed as an abstract workflow, but hand in hand with actual scientific questions. It thus addresses scientists with either a meteorological or a ML background. Due to their relative ease of use and spectacular results in other application areas, neural networks and other ML methods are gaining enormous momentum also in the weather and air quality research communities. Even though there are already many books and tutorials describing how to conduct a ML experiment, there are many stumbling blocks for a newcomer. In contrast, people familiar with ML concepts and technology often have difficulties understanding the nature of atmospheric data. With MLAir we have addressed a number of these pitfalls so that it becomes easier for scientists of both domains to rapidly start off their ML application. MLAir has been developed in such a way that it is easy to use and is designed from the very beginning as a standalone, fully functional experiment. Due to its flexible, modular code base, code modifications are easy and personal experiment schedules can be quickly derived. The package also includes a set of simple validation tools to facilitate the evaluation of ML results using standard meteorological statistics. MLAir can easily be ported onto different computing environments from desktop workstations to high-end supercomputers with or without graphics processing units (GPU).

Lukas H. Leufen et al.

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Model code and software

MLAir (v1.0.0) - a tool to enable fast and flexible machine learning on air data time series - Source Code Lukas Hubert Leufen, Felix Kleinert, and Martin Georg Schultz https://doi.org/10.34730/fcc6b509d5394dad8cfdfc6e9fff2bec

Lukas H. Leufen et al.

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Latest update: 01 Dec 2020
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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). Yet, it is adaptable to a variety of ML use cases. 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 the 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...
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