28 citations as recorded by crossref.

1. On the thermodynamic invariance of fine‐grain and coarse‐grain fluid models T. Dubos 10.1002/qj.4830
2. Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
3. A Fortran–Python interface for integrating machine learning parameterization into earth system models T. Zhang et al. 10.5194/gmd-18-1917-2025
4. WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer X. Zhong et al. 10.5194/gmd-16-199-2023
5. Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
6. An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration Y. Han et al. 10.1029/2022MS003508
7. Deep Learning Based Cloud Cover Parameterization for ICON A. Grundner et al. 10.1029/2021MS002959
8. Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
9. Projections of Global Land Runoff Changes and Their Uncertainty Characteristics During the 21st Century C. Miao et al. 10.1029/2022EF003286
10. 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. 10.5194/gmd-17-3667-2024
11. 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
12. Confronting uncertainty: The future of hydropower in the himalayan region amidst climate ambiguity N. Devkota et al. 10.1016/j.esd.2025.101657
13. Deep Learning Parameterization of the Tropical Cyclone Boundary Layer L. Wang & Z. Tan 10.1029/2022MS003034
14. Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung 10.3390/atmos16010060
15. Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann 10.5194/nhess-23-3065-2023
16. Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan 10.5194/gmd-16-3241-2023
17. Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al. 10.3390/atmos15101219
18. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
19. On fast simulation of dynamical system with neural vector enhanced numerical solver Z. Huang et al. 10.1038/s41598-023-42194-y
20. Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model K. Nordling et al. 10.5194/acp-24-869-2024
21. Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection C. Sanford et al. 10.1029/2023MS003809
22. Projected increase in the frequency of extremely active Atlantic hurricane seasons H. Lopez et al. 10.1126/sciadv.adq7856
23. TorchClim v1.0: a deep-learning plugin for climate model physics D. Fuchs et al. 10.5194/gmd-17-5459-2024
24. 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
25. Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations S. Clark et al. 10.1029/2022MS003219
26. Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques Y. Wu et al. 10.1029/2022EF002963
27. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
28. Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations C. Bretherton et al. 10.1029/2021MS002794