50 citations as recorded by crossref.

1. Reduced subgrid scale terms in three-dimensional turbulence R. Hoekstra & W. Edeling
2. Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations O. Watt‐Meyer et al.
3. Unsupervised super-resolution data assimilation using conditional variational autoencoders with estimating background covariances via super-resolution Y. Yasuda & R. Onishi
4. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg
5. An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry M. Kelp et al.
6. Periodic orbits in chaotic systems simulated at low precision M. Klöwer et al.
7. Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model R. Parthipan et al.
8. Knowledge-guided hybrid AI framework for GRACE data reconstruction and generalization in South Asia: A spatiotemporal perspective P. Paramaguru et al.
9. LEX v1.6.0: a new large-eddy simulation model in JAX with GPU acceleration and automatic differentiation X. Zhu et al.
10. Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al.
11. 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.
12. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al.
13. Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al.
14. Compound Parameterization to Improve the Accuracy of Radiation Emulator in a Numerical Weather Prediction Model H. Song et al.
15. Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology T. Finn et al.
16. 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.
17. Using neural networks to improve simulations in the gray zone R. Kriegmair et al.
18. Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks S. Driscoll et al.
19. Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model A. Belochitski & V. Krasnopolsky
20. Development of a large-eddy simulation subgrid model based on artificial neural networks: a case study of turbulent channel flow R. Stoffer et al.
21. Spatio‐Temporal Super‐Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks With Domain Generalization Y. Yasuda & R. Onishi
22. How to Calibrate a Dynamical System With Neural Network Based Physics? B. Balogh et al.
23. Stochastic rectification of fast oscillations on slow manifold closures M. Chekroun et al.
24. Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit D. Suleimenova et al.
25. Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes X. Wang et al.
26. A hybrid framework for global weather forecasting via low-resolution dynamical core and multigrid neural operator Y. Hu et al.
27. Development of an offline and online hybrid model for the Integrated Forecasting System A. Farchi et al.
28. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al.
29. Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0 X. Zhong et al.
30. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions J. Yuval & P. O’Gorman
31. Deep reinforcement learning for turbulence modeling in large eddy simulations M. Kurz et al.
32. Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang
33. Advancing Artificial Neural Network Parameterization for Atmospheric Turbulence Using a Variational Multiscale Model M. Janssens & S. Hulshoff
34. Learning subgrid-scale models with neural ordinary differential equations S. Kang & E. Constantinescu
35. Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere J. Yuval & P. O’Gorman
36. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al.
37. Semi‐Automatic Tuning of Coupled Climate Models With Multiple Intrinsic Timescales: Lessons Learned From the Lorenz96 Model R. Lguensat et al.
38. Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants N. Mücke & B. Sanderse
39. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al.
40. Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5) C. Arnold et al.
41. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg
42. Differentiable programming for Earth system modeling M. Gelbrecht et al.
43. A multifidelity deep operator network approach to closure for multiscale systems S. Ahmed & P. Stinis
44. A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model S. Roberts et al.
45. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al.
46. TorchClim v1.0: a deep-learning plugin for climate model physics D. Fuchs et al.
47. Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows S. Pawar & O. San
48. Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning W. Gregory et al.
49. Physics-informed learning of aerosol microphysics P. Harder et al.
50. Reduced data-driven turbulence closure for capturing long-term statistics R. Hoekstra et al.