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
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025,https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025,https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
NeuralMie (v1.0): an aerosol optics emulator
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025,https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025,https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025,https://doi.org/10.5194/gmd-18-1661-2025, 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