14 citations as recorded by crossref.

1. Reconstruction of unstable atmospheric surface layer streamwise turbulence based on multi-layer perceptron neural network architecture C. Huang et al. https://doi.org/10.1016/j.euromechflu.2024.11.006
2. How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. https://doi.org/10.1088/1742-6596/2265/2/022028
3. Is an Artificial Neural Network Able to Reproduce Atmospheric Turbulent Fluxes of a Large Eddy Simulation? B. Körner et al. https://doi.org/10.3390/atmos17080748
4. A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. https://doi.org/10.1016/j.atmosres.2022.106542
5. Reducing Parametrization Errors for Polar Surface Turbulent Fluxes Using Machine Learning D. Cummins et al. https://doi.org/10.1007/s10546-023-00852-8
6. Estimation of the Surface Fluxes for Heat and Momentum in Unstable Conditions with Machine Learning and Similarity Approaches for the LAFE Data Set V. Wulfmeyer et al. https://doi.org/10.1007/s10546-022-00761-2
7. A Review of Machine Learning Applications in Land Surface Modeling S. Pal & P. Sharma https://doi.org/10.3390/earth2010011
8. Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study M. Zouzoua et al. https://doi.org/10.5194/gmd-18-3211-2025
9. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg https://doi.org/10.5194/gmd-16-6433-2023
10. Improved machine learning estimation of surface turbulent flux using interpretable model selection and adaptive ensemble algorithms over the Horqin Sandy Land area J. Zhao et al. https://doi.org/10.1016/j.atmosres.2025.107952
11. Model and Observation for surface–atmosphere interactions over heterogeneous landscape: MOSAI project F. Lohou et al. https://doi.org/10.1016/j.jemets.2025.100019
12. Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain? N. Bodini et al. https://doi.org/10.5194/gmd-13-4271-2020
13. Evaluation of an Artificial Neural Network based method for surface heat flux estimates over heterogeneous landscapes M. Jomé et al. https://doi.org/10.1016/j.jemets.2025.100021
14. Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning D. Cummins et al. https://doi.org/10.1029/2023GL105698