39 citations as recorded by crossref.

1. Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model A. Belochitski & V. Krasnopolsky 10.5194/gmd-14-7425-2021
2. Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow R. Stoffer et al. 10.5194/gmd-14-3769-2021
3. Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi 10.1029/2023MS003658
4. Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations O. Watt‐Meyer et al. 10.1029/2021GL092555
5. How to Calibrate a Dynamical System With Neural Network Based Physics? B. Balogh et al. 10.1029/2022GL097872
6. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg 10.1029/2021MS002554
7. Stochastic rectification of fast oscillations on slow manifold closures M. Chekroun et al. 10.1073/pnas.2113650118
8. Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit D. Suleimenova et al. 10.1016/j.jocs.2021.101402
9. An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry M. Kelp et al. 10.1029/2021MS002926
10. Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes X. Wang et al. 10.5194/gmd-15-3923-2022
11. Periodic orbits in chaotic systems simulated at low precision M. Klöwer et al. 10.1038/s41598-023-37004-4
12. Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model R. Parthipan et al. 10.5194/gmd-16-4501-2023
13. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. 10.1029/2020GL092133
14. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions J. Yuval & P. O’Gorman 10.1038/s41467-020-17142-3
15. Deep reinforcement learning for turbulence modeling in large eddy simulations M. Kurz et al. 10.1016/j.ijheatfluidflow.2022.109094
16. Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model M. Janssens & S. Hulshoff 10.1029/2021MS002490
17. Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. 10.1029/2022MS003235
18. Learning subgrid-scale models with neural ordinary differential equations S. Kang & E. Constantinescu 10.1016/j.compfluid.2023.105919
19. Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere J. Yuval & P. O’Gorman 10.1029/2023MS003606
20. Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision J. Yuval et al. 10.1029/2020GL091363
21. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
22. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
23. Semi‐Automatic Tuning of Coupled Climate Models With Multiple Intrinsic Timescales: Lessons Learned From the Lorenz96 Model R. Lguensat et al. 10.1029/2022MS003367
24. Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model H. Song et al. 10.1029/2021GL095043
25. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. 10.1038/s43017-023-00452-7
26. Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al. 10.5194/tc-17-2965-2023
27. Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts L. Mansfield et al. 10.1029/2023MS003715
28. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
29. Differentiable programming for Earth system modeling M. Gelbrecht et al. 10.5194/gmd-16-3123-2023
30. A multifidelity deep operator network approach to closure for multiscale systems S. Ahmed & P. Stinis 10.1016/j.cma.2023.116161
31. A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model S. Roberts et al. 10.1137/20M1386281
32. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
33. Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022
34. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows S. Pawar & O. San 10.1103/PhysRevFluids.6.050501
35. Physics-informed learning of aerosol microphysics P. Harder et al. 10.1017/eds.2022.22
36. Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks S. Driscoll et al. 10.1016/j.jocs.2024.102231
37. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
38. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
39. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180