14 citations as recorded by crossref.

1. Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence S. Silva et al. https://doi.org/10.1029/2021MS002881
2. Assessing modifications to the Abdul-Razzak and Ghan aerosol activation parameterization (version ARG2000) to improve simulated aerosol–cloud radiative effects in the UK Met Office Unified Model (UM version 13.0) P. Ghosh et al. https://doi.org/10.5194/gmd-18-4899-2025
3. Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0) P. Sturm & A. Wexler https://doi.org/10.5194/gmd-15-3417-2022
4. Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. https://doi.org/10.1029/2021GH000570
5. Technical note: Parameterising cloud base updraft velocity of marine stratocumuli J. Ahola et al. https://doi.org/10.5194/acp-22-4523-2022
6. Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals A. Varble et al. https://doi.org/10.5194/acp-23-13523-2023
7. An ANN-Based Inversion Method for High-Precision Layer Thicknesses in Coal–Rock Recognition P. Zhu et al. https://doi.org/10.1109/TGRS.2026.3651337
8. Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. https://doi.org/10.5194/esd-15-1319-2024
9. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul https://doi.org/10.3390/atmos13020180
10. A Nudge to the Truth: Atom Conservation as a Hard Constraint in Models of Atmospheric Composition Using a Species-Weighted Correction P. Sturm & S. Silva https://doi.org/10.1021/acsestair.4c00220
11. Neural emulator based on physical fields for accelerating the simulation of surface chlorophyll in an Earth System Model B. Wu et al. https://doi.org/10.1016/j.ocemod.2024.102491
12. Forecasting estuarine salt intrusion in the Rhine–Meuse delta using an LSTM model B. Wullems et al. https://doi.org/10.5194/hess-27-3823-2023
13. Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks J. Rader et al. https://doi.org/10.1029/2021MS002941
14. Deep learning representation of the aerosol size distribution D. Barahona et al. https://doi.org/10.5194/gmd-19-2437-2026