Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4429–4441, 2021
https://doi.org/10.5194/gmd-14-4429-2021
Geosci. Model Dev., 14, 4429–4441, 2021
https://doi.org/10.5194/gmd-14-4429-2021

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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2020-434', Anonymous Referee #1, 15 Mar 2021
  • RC2: 'Comment on gmd-2020-434', Anonymous Referee #2, 19 Mar 2021
  • RC3: 'Comment on gmd-2020-434', Anonymous Referee #3, 24 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Klaus Klingmueller on behalf of the Authors (17 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (29 May 2021) by Rohitash Chandra
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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.