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A hierarchical modeling framework to discover new machine learning-based equations for cloud cover, including symbolic regression

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EyringMLClimateGroup/grundner23james_EquationDiscovery_CloudCover

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Data-Driven Equation Discovery of a Cloud Cover Parameterization

A hierarchical modeling framework to discover new machine learning-based equations for cloud cover, including symbolic regression

Grundner, A., Beucler, T., Gentine, P., & Eyring, V. (2023). Data-Driven Equation Discovery of a Cloud Cover Parameterization. Preprint

Author: Arthur Grundner, arthur.grundner@dlr.de

The current release on zenodo can be found here: DOI

List of Figures

  • Fig 1, Code: Comparison of the coarse-grained DYAMOND and ERA5 data
  • Fig 2, Code: All cloud cover schemes in a performance x complexity plot
  • Fig 3, Code: Predicted cloud cover distributions
  • Fig 4, Code: Transfer to higher resolutions
  • Fig 5.1, Code: Transfer learning to ERA5 data (selected schemes)
  • Fig 5.2, Code: Transfer learning to ERA5 data (polynomials & NNs)
  • Fig 6.1, Code: Plots of the terms I_1, I_2, I_3
  • Fig 6.2, Code: Conditional average w.r.t. RH and T
  • Fig 6.3, Code: Conditional average w.r.t. dzRH
  • Fig 7.1, Code: Contour plot of dzRH
  • Fig 7.2, Code: Cloud cover w.r.t. RH with and without modification to satisfy the RH-physical constraint
  • Fig 8, Code: Ablation study of our analytic scheme on DYAMOND and ERA5 data
  • Fig A1.1, Fig A1.2, Fig A1.3, Fig A1.4, Fig A1.5, Fig A1.6, Code: Maps of I1, I2, I3 on a specific vertical layer on ~1490m averaged over 10 days of DYAMOND data. Maps of the a5-term on three different altitudes
  • Fig B1.1, Fig B1.2, Code: The distributions of cloud water and cloud ice on storm-resolving scales

Data

To reproduce the results it is first necessary to have access to accounts on DKRZ/Levante. Then one can coarse-grain and preprocess the DYAMOND and ERA5/ERA5.1 data sets:

It suffices to coarse-grain the variables: clc/cc, cli/ciwc, clw/clwc, hus/q, pa, ta/t, ua/u, va/v, zg/z

Dependencies

The results were produced with the version numbers indicated below:

To create a working environment you can run the following line:

conda install -c conda-forge tensorflow==2.7.0 scipy==1.8.1 sympy==1.10.1 scikit-learn==1.0.2 mlxtend==0.20.0 pysr==0.10.1

To install the GP-GOMEA dependency please refer to their website.

License

This code is released under Apache 2.0. See LICENSE for more information.

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A hierarchical modeling framework to discover new machine learning-based equations for cloud cover, including symbolic regression

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