Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 5
Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2033-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2033-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 5
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

Viewed

Total article views: 2,572 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,679 836 57 2,572 80 68
  • HTML: 1,679
  • PDF: 836
  • XML: 57
  • Total: 2,572
  • BibTeX: 80
  • EndNote: 68
Views and downloads (calculated since 21 Nov 2018)
Cumulative views and downloads (calculated since 21 Nov 2018)

Viewed (geographical distribution)

Total article views: 2,572 (including HTML, PDF, and XML) Thereof 2,325 with geography defined and 247 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
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.
Read more