Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 7
Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4429-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4429-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 7
Development and technical paper
 | 
19 Jul 2021
Development and technical paper |  | 19 Jul 2021

Climate-model-informed deep learning of global soil moisture distribution

Klaus Klingmüller and Jos Lelieveld

Viewed

Total article views: 2,962 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,833 1,034 95 2,962 106 51 44
  • HTML: 1,833
  • PDF: 1,034
  • XML: 95
  • Total: 2,962
  • Supplement: 106
  • BibTeX: 51
  • EndNote: 44
Views and downloads (calculated since 17 Feb 2021)
Cumulative views and downloads (calculated since 17 Feb 2021)

Viewed (geographical distribution)

Total article views: 2,962 (including HTML, PDF, and XML) Thereof 2,649 with geography defined and 313 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Nov 2024
Download
Short summary
Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.
Read more