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Enabling Smart Dynamical Downscaling of Extreme Precipitation Events With Machine Learning

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Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere

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Deep Learning Parameterization of the Tropical Cyclone Boundary Layer

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An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

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Machine Learning for Clouds and Climate

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A Structured Graph Neural Network for Improving the Numerical Weather Prediction of Rainfall

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RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

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Uncertainty Quantification of a Machine Learning Subgrid‐Scale Parameterization for Atmospheric Gravity Waves

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Balogh, B., Saint‐Martin, D., & Ribes, A. (2021) · Geophysical Research Letters · doi:10.1029/2020gl092133 · Cited by 11 · see citing works

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Formulation and Calibration of CATKE, a One‐Equation Parameterization for Microscale Ocean Mixing

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Subgrid-scale parametrization of unresolved scales in forced Burgers equation using generative adversarial networks (GAN)

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Physically Constrained Adaptive Deep Learning for Ocean Vertical-Mixing Parameterization

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Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flowsPreprint

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A Fortran–Python interface for integrating machine learning parameterization into earth system models

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Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

Chen, J., Zhang, M., Zhang, T., Lin, W., & Xue, W. (2025) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2024ms004722 · Cited by 6 · see citing works

Neural general circulation models for modeling precipitation

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AI for atmosphere–ocean sciences: advancements, challenges and ways forward

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A predictive model for the estimation of industrial PM2.5 emissions for IoT-based devices

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Assessment of scale-up designs for a diameter-transformed fluidized bed reactor with MP-PIC simulation

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Adjoint‐Based Online Learning of Two‐Layer Quasi‐Geostrophic Baroclinic Turbulence

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A Neural-Network Based MPAS—Shallow Water Model and Its 4D-Var Data Assimilation System

Tian, X., Conibear, L., & Steward, J. (2023) · Atmosphere · doi:10.3390/atmos14010157 · Cited by 5 · see citing works

Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision

Yuval, J., O’Gorman, P. A., & Hill, C. (2021) · Geophysical Research Letters · doi:10.1029/2020gl091363 · Cited by 170 · see citing works

Prognostic validation of a neural network unified physics parameterization

Brenowitz, N., & Bretherton, C. (2018) · Open Science Framework · doi:10.17605/osf.io/eu3ax

Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

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Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes

Wang, X., Han, Y., Xue, W., Yang, G., & Zhang, G. J. (2022) · Geoscientific model development · doi:10.5194/gmd-15-3923-2022 · Cited by 72 · see citing works

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

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