12 citations as recorded by crossref.

1. Stability Dependence of the Turbulent Dissipation Rate in the Convective Atmospheric Boundary Layer Y. Lv et al. 10.1029/2023GL103326
2. How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
3. Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea J. Kim et al. 10.3390/atmos12070837
4. Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. 10.1029/2022MS003072
5. Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error D. Vassallo et al. 10.5194/wes-6-295-2021
6. Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
7. A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. 10.1016/j.atmosres.2022.106542
8. Data-driven prediction of mean wind turbulence from topographic data B. Morais da Costa et al. 10.1088/1757-899X/1201/1/012005
9. Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain A. Wise et al. 10.5194/wes-7-367-2022
10. Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments F. Späth et al. 10.1029/2021JD035697
11. Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble N. Bodini et al. 10.5194/wes-6-1363-2021
12. Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine‐Height Winds in the MYNN‐EDMF Parameterization L. Berg et al. 10.1029/2020JD034000