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Deep learning and process understanding for data-driven Earth system science

Nature volume 566, pages 195–204 (2019)Cite this article

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Abstract

Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

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Fig. 1: Big data challenges in the geoscientific context.
Fig. 2: Four examples of typical deep learning applications (left panels) and the geoscientific problems they can be applied to (right panels).
Fig. 3: Linkages between physical models and machine learning.
Fig. 4: Interpretation of hybrid modelling as deepening a deep learning architecture by adding one or several physical layers after the multilayer neural network to make the model more physically realistic.

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Acknowledgements

We thank D. Frank and L. Maack for proofreading, help with the literature and technical help, and F. Gans for programming help in Julia. This research was supported by a grant by the Alexander von Humboldt Foundation (Max Planck Research Prize) to M.R. G.C.-V. was supported by the European Research Council (ERC) under the ERC Consolidator Grant ERC-CoG-2014 SEDAL (grant agreement 647423).

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Authors and Affiliations

  1. Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany

    Markus Reichstein, Martin Jung & Nuno Carvalhais

  2. Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany

    Markus Reichstein & Joachim Denzler

  3. Image Processing Laboratory (IPL), University of València, Valencia, Spain

    Gustau Camps-Valls

  4. Max Planck Institute for Meteorology, Hamburg, Germany

    Bjorn Stevens

  5. Computer Vision Group, Computer Science, Friedrich Schiller University, Jena, Germany

    Joachim Denzler

  6. CENSE, Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal

    Nuno Carvalhais

  7. National Energy Research Supercomputing Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Prabhat

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  1. Markus Reichstein
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M.R. conceived the work, created a first outline with Prabhat and ran the numerical experiment. All authors wrote the manuscript.

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Correspondence to Markus Reichstein.

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Reichstein, M., Camps-Valls, G., Stevens, B. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019). https://doi.org/10.1038/s41586-019-0912-1

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