Received: 23 Dec 2020 – Accepted for review: 15 Feb 2021 – Discussion started: 17 Feb 2021
Abstract. We present a deep neural network (DNN) that produces accurate predictions of observed surface soil moisture, based on meteorological data from a climate model. The network was trained on daily satellite retrievals of soil moisture from the European Space Agency (ESA) Climate Change Initiative (CCI). The predictors precipitation, temperature and humidity were simulated with the ECHAM/MESSy atmospheric chemistry-climate model (EMAC). Our evaluation shows that predictions of the trained DNN are highly correlated with the observations, both, spatially and temporally, and free of bias. This offers an alternative for parametrisation schemes in climate models, especially in simulations that use, but may not focus on soil moisture, which we illustrate with the threshold wind speed for mineral dust emissions. Moreover, the DNN can provide proxies for missing values in satellite observations to produce realistic, comprehensive, high resolution global datasets. As the approach presented here could be similarly used for other variables and observations, the study is a proof of concept for basic but expedient machine learning techniques in climate modelling, which may motivate additional applications.
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.
Soil moisture is of great importance for weather and climate. We present a machine learning...