Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2033-2019
https://doi.org/10.5194/gmd-12-2033-2019
Development and technical paper
 | 
24 May 2019
Development and technical paper |  | 24 May 2019

Calculating the turbulent fluxes in the atmospheric surface layer with neural networks

Lukas Hubert Leufen and Gerd Schädler

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
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
Geosci. Model Dev., 14, 1553–1574, https://doi.org/10.5194/gmd-14-1553-2021,https://doi.org/10.5194/gmd-14-1553-2021, 2021
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

Related subject area

Climate and Earth system modeling
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024,https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024,https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024,https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023,https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Getting the leaves right matters for estimating temperature extremes
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023,https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary

Cited articles

Andersen, T. and Martinez, T.: Cross validation and MLP architecture selection, in: IJCNN'99. International Joint Conference on Neural Networks. Proceedings, Washington, DC, USA, 10–16 July 1999, IEEE, 3, 1614–1619, 1999. a
Arya, P. S.: Introduction to micrometeorology, in: International Geophysics Series, San Diego, Calif., Academic Press, vol. 79, 2001. a, b, c, d
Braun, F. and Schädler, G.: Comparison of Soil Hydraulic Parameterizations for Mesoscale Meteorological Models., J. Appl. Meteorol., 44, 1116–1132, 2005. a
Broyden, C. G.: The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations, IMA J. Appl. Math., 6, 76–90, https://doi.org/10.1093/imamat/6.1.76, 1970.  a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-Profile Relationships in the Atmospheric Surface Layer, J. Atmos. Sci., 28, 181–189, https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2, 1971. a
Download
Short summary
An artificial neural network was used to calculate the scaling quantities u* and T*. To train and test the network, a large set of worldwide observations was used. Extensive sensitivity studies showed that a relatively small 6–3–2 network with six input parameters and one hidden layer yields satisfying results. An implementation of this network in a stand-alone land surface model showed that the neural network gives results equivalent to and sometimes better than the standard implementation.