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Deep Learning Based Cloud Cover Parameterization for ICON
Arthur Grundner1,2, Tom Beucler3, Pierre Gentine2
1Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany.
Journal of Advances in Modeling Earth Systems
|April 10, 2023
Summary
Deep learning models accurately estimate cloud cover using storm-resolving model data. Neighborhood-based neural networks offer a balance of accuracy and generalizability for climate projections.
Area of Science:
- Climate Science
- Atmospheric Physics
- Machine Learning
Background:
- Improving climate model parameterizations is crucial for accurate climate projections.
- Storm-resolving models (SRMs) provide high-fidelity data for training.
Purpose of the Study:
- To develop and evaluate deep learning-based cloud cover parameterizations for climate models.
- To assess the generalizability of neural networks trained on SRM data.
Main Methods:
- Utilized the ICOsahedral Non-hydrostatic (ICON) modeling framework.
- Trained neural networks (NNs) on coarse-grained data from ICON SRMs.
- Employed SHapley Additive exPlanations for model interpretability.
Main Results:
- NNs accurately estimated sub-grid scale cloud cover.
- Globally trained NNs showed good performance on regional data.
- Identified overemphasis on specific humidity and cloud ice as generalization limitations.
Conclusions:
- Deep learning can derive accurate and interpretable cloud cover parameterizations.
- Neighborhood-based NNs present a promising compromise between accuracy and generalizability.
- Interpretability tools aid in understanding NN behavior and improving climate models.
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