Paper Review: Reichstein (2019). Deep learning. Nature

· β˜• 2 min read · ✍️ Hoontaek Lee
  • #Research
  • #Paper Review
  • #2020
  • 7 February, 2020 (Reichstein, Nature, Deep learning)

    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).

    • Question:

      1. How have machine learning algorithms, especially deep learning, been applied to Earth system science and what is the future direction of the application?
    • Context:

      • Our ability to produce a deluge of data outpaces our ability to assimilate the information.
      • The advance in computing system, statistical and machine learning modeling have combined with the plethora of Earth system data to create a new avenue of modeling: deep learning.
    • Answer

      1. Machine learning approach (such as deep learning) have successfully been applied to many problems in Earth system science.
      2. Machine learning and the physical models can complement each other, creating a new way of modeling: hybrid modeling.
      3. Strengths of the hybrid modeling
        • Improving parameterizations: finding optimal values and pertinent patterns (e.g. using ML-derived parameters instead of the pre-defined PFTs).
        • Replacing a physical submodel; many submodels are of semi-empirical, where the functional form has little theoretical basis.
        • Analysis of model-observation mismatch: ML can help to identify, visualize, and understand the patterns of model error, which allows us also to correct model outputs.
        • Constraining submodels
        • Surrogate modeling: ML can be used for faster simulation.
    • Implication:

      • Deep learning and other ML algorithms have promising potential to be used for Earth system science.
      • Hybrid modeling, which integrates physical- and data-driven modeling, is recommended.
    • Unanswered:

      • -
    • Comment:

      • Bayesian framework is effective in quantifying uncertainties or in tracing the propagation of them.
      • I think that Reichstein is a great writer.
      • deluge, soothsaying, pertinent, notwithstanding, yet, tedious, exemplify, ad hoc, circumvent,
    Share on

    Hoontaek Lee
    Hoontaek Lee
    Tree-Forest-Climate Researcher

    What's on this Page