mlsanityData to 7 Oct 2026

Towards Physically-consistent, Data-driven Models of Convection

arXiv 2002.08525 ↗First posted 20 Feb 2020physics.ao-ph cs.LG physics.comp-phcited by 13

Not on the map. Why?

Abstract (arXiv)

Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations. However, they may violate key physical constraints and lack the ability to generalize outside of their training set. Here, we show that physical constraints can be enforced in neural networks, either approximately by adapting the loss function or to within machine precision by adapting the architecture. As these physical constraints are insufficient to guarantee generalizability, we additionally propose to physically rescale the training and validation data to improve the ability of neural networks to generalize to unseen climates.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 2; 2025: 1; 2026: 1.

13 papers.

Sort:

  1. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  2. 2021Climate-Invariant Machine Learningcited by 24
  3. 2025A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfactioncited by 11
  4. 2022Physics-Informed Learning of Aerosol Microphysicscited by 9
  5. 2023Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networkscited by 6
  6. 2021Deep Learning Based Cloud Cover Parameterization for ICONcited by 5
  7. 2022Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 modelcited by 5
  8. 2021Emulating Aerosol Microphysics with Machine Learningcited by 3
  9. 2023Inductive biases in deep learning models for weather predictioncited by 3
  10. 2024Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1cited by 2
  11. 2024Towards Physically Consistent Deep Learning For Climate Model Parameterizationscited by 1
  12. 2022Physics-constrained deep learning postprocessing of temperature and humiditycited by 0
1 more
  1. 2026No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisationcited by 0