Physics-guided multi-task learning for subgrid scale turbulence parameterization: a comparative study of physics integration strategies

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Panda, S. K., Jones, T. R. ORCID: https://orcid.org/0000-0002-7669-1499, Shahzad, M. ORCID: https://orcid.org/0009-0002-9394-343X, Lawrence, B. ORCID: https://orcid.org/0000-0001-9262-7860 and Ellis, A.-L. (2026) Physics-guided multi-task learning for subgrid scale turbulence parameterization: a comparative study of physics integration strategies. In: International Joint Conference on Neural Networks (IJCNN), SS03 Physics-Informed Neural Networks: Advancements and Applications, 21-26 June 2026, Maastricht, The Netherlands. (In Press)

Abstract/Summary

Neural network emulation of subgrid-scale (SGS) turbulence parameterizations in atmospheric models offers the promise of computational acceleration but suffers from brittle cross-regime generalization that limits operational deployment. We present a systematic evaluation of physics-guided multi-task learning strategies for SGS coefficient prediction, comparing six conditioning approaches across three neural architectures (MLP, ResMLP, TabTransformer) on contrasting atmospheric regimes: idealized tropical deep convection (Radiative Convective Equilibrium) and mid-latitude shallow convection (Atmospheric Radiation Measurement). Through evaluation of 222.6 million predictions (inference), we reveal dataset-dependent behaviour with important implications for neural network design. Simple MLPs with explicit Richardson number conditioning achieve improved cross-regime generalization on ARM data (R2 = 0.67 viscosity, 0.73 diffusivity), outperforming architecturally complex alternatives by 158% in viscosity R2 (0.432 vs 0.167) on dynamically important active turbulence regions. Residual networks that achieve strong performance on idealized RCE simulations (R2 > 0.78) produce predictions indicative of numerical instability on the ARM regime, producing negative R2 values with variance ratios exceeding 3× the ground truth. Kling-Gupta Efficiency decomposition reveals systematic positive bias across all model configurations, reflecting training data sparsity in active turbulence regions rather than fundamental physics incompatibility. These findings establish design principles for atmospheric ML parameterization development: architectural simplicity with targeted physics conditioning provides improved offline predictive robustness compared to complex architectures relying on implicit pattern learning. Online coupling experiments remain essential to validate operational stability under feedback dynamics.

Item Type Conference or Workshop Item (Paper)
URI https://centaur.reading.ac.uk/id/eprint/129190
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords Physics-guided neural networks, multi-task learning, atmospheric turbulence parameterization, cross-regime generalization, large eddy simulation, Scientific Machine Learning
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