33 citations as recorded by crossref.

1. On the thermodynamic invariance of fine‐grain and coarse‐grain fluid models T. Dubos
2. Machine learning for the physics of climate A. Bracco et al.
3. A Fortran–Python interface for integrating machine learning parameterization into earth system models T. Zhang et al.
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.
5. Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al.
6. An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration Y. Han et al.
7. Deep Learning Based Cloud Cover Parameterization for ICON A. Grundner et al.
8. Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky
9. The operation optimization of multipurpose reservoir between ARIMA, continuous, and Markov Chain models on Jatigede reservoir, Indonesia M. Jayanti et al.
10. Projections of Global Land Runoff Changes and Their Uncertainty Characteristics During the 21st Century C. Miao et al.
11. 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.
12. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al.
13. Confronting uncertainty: The future of hydropower in the himalayan region amidst climate ambiguity N. Devkota et al.
14. Deep Learning Parameterization of the Tropical Cyclone Boundary Layer L. Wang & Z. Tan
15. Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung
16. Developing Intelligent Earth System Models : An AI scheme of K-profile parameterization and stable coupling into CESM with FTA B. Mu et al.
17. Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann
18. Advanced solar radiation prediction using combined satellite imagery and tabular data processing M. Attya et al.
19. Progress and Perspective of Convection and Cloud Parameterizations in Numerical Models Y. Wang et al.
20. Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan
21. Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al.
22. From black box to physically interpretable: Trustworthy computing for AI-driven decision-making and control Y. Song & A. Zhang
23. Integrated strategies for water sustainability in the Yellow River Basin: Roles of water transfer, reclaimed water, and irrigation efficiency W. Zhang et al.
24. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg
25. CondensNet: enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints X. Wang et al.
26. On fast simulation of dynamical system with neural vector enhanced numerical solver Z. Huang et al.
27. Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model K. Nordling et al.
28. On the Stable Integration of Neural Network Parameterization in Numerical Models Y. Wang et al.
29. Applying deep learning to a chemistry-climate model for improved ozone prediction Z. Liu et al.
30. Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection C. Sanford et al.
31. Projected increase in the frequency of extremely active Atlantic hurricane seasons H. Lopez et al.
32. 基于机器学习的气候模式次网格参数化方案研究 超. 严 & 世. 苗
33. TorchClim v1.0: a deep-learning plugin for climate model physics D. Fuchs et al.