13 citations as recorded by crossref.

1. Impact of the Different Grid Resolutions of the WRF Model for the Forecasting of the Flood Event of 15 July 2020 in Palermo (Italy) G. Castorina et al. 10.3390/atmos13101717
2. A machine learning based deep convective trigger for climate models S. Kumar et al. 10.1007/s00382-024-07332-w
3. Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily G. Castorina et al. 10.3390/atmos14020390
4. Statistics of Isolated Deep Convection Initiation and Its Relation to Topography in the North China Area G. Lu et al. 10.1029/2022JD037949
5. Physics schemes in the first version of NCEP operational hurricane analysis and forecast system (HAFS) W. Wang et al. 10.3389/feart.2024.1379069
6. 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
7. An Entropy Generation Rate Model for Tropospheric Behavior That Includes Cloud Evolution J. Sekhar 10.3390/e25121625
8. Coupling human dynamics with the physics of climate: a path towards Human Earth Systems Models F. Tapiador & A. Navarro 10.1088/2752-5295/ad7974
9. Impacts of the stochastically perturbed parameterization on the precipitation ensemble forecasts of the Betts–Miller–Janjić (BMJ) scheme in Eastern China X. Qiao et al. 10.1016/j.atmosres.2023.107036
10. Distinct Mixing Regimes in Shallow Cumulus Clouds Y. Arieli et al. 10.1029/2023GL105746
11. Assessing Memory in Convection Schemes Using Idealized Tests Y. Hwong et al. 10.1029/2023MS003726
12. The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation L. Zhu et al. 10.1007/s00376-023-2357-6
13. Incorporating the Effect of Large‐Scale Vertical Motion on Convection Through Convective Mass Flux Adjustment in E3SMv2 X. Song et al. 10.1029/2022MS003553