Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1553-2021
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, Felix Kleinert, and Martin G. Schultz

Related authors

Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022,https://doi.org/10.5194/gmd-15-8913-2022, 2022
Short summary
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz
Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021,https://doi.org/10.5194/gmd-14-1-2021, 2021
Short summary
Calculating the turbulent fluxes in the atmospheric surface layer with neural networks
Lukas Hubert Leufen and Gerd Schädler
Geosci. Model Dev., 12, 2033–2047, https://doi.org/10.5194/gmd-12-2033-2019,https://doi.org/10.5194/gmd-12-2033-2019, 2019
Short summary

Related subject area

Atmospheric sciences
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025,https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025,https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025,https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025,https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025,https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary

Cited articles

Bentayeb, M., Wagner, V., Stempfelet, M., Zins, M., Goldberg, M., Pascal, M., Larrieu, S., Beaudeau, P., Cassadou, S., Eilstein, D., Filleul, L., Le Tertre, A., Medina, S., Pascal, L., Prouvost, H., Quénel, P., Zeghnoun, A., and Lefranc, A.: Association between long-term exposure to air pollution and mortality in France: a 25-year follow-up study, Environ. Int., 85, 5–14, https://doi.org/10.1016/j.envint.2015.08.006, 2015. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, 2006. a
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A., Balzarini, A., Baró, R., Bianconi, R., Chemel, C., Curci, G., Forkel, R., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Im, U., Knote, C., Makar, P., Manders-Groot, A., van Meijgaard, E., Neal, L., Pérez, J. L., Pirovano, G., San Jose, R., Schröder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Hogrefe, C., and Galmarini, S.: Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2, Atmos. Environ., 115, 470–498, https://doi.org/10.1016/j.atmosenv.2014.12.032, 2015. a
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, arXiv: 1406.1078, available at: http://arxiv.org/abs/1406.1078 (last access: 10 March 2021), 2014. a
Download
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.
Share