Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4429-2021
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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Cited articles

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Astitha, M., Lelieveld, J., Abdel Kader, M., Pozzer, A., and de Meij, A.: Parameterization of dust emissions in the global atmospheric chemistry-climate model EMAC: impact of nudging and soil properties, Atmos. Chem. Phys., 12, 11057–11083, https://doi.org/10.5194/acp-12-11057-2012, 2012. a, b
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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.