Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4271-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4271-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Sep 2020
Development and technical paper |
| 16 Sep 2020
| 16 Sep 2020
Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Julie K. Lundquist
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA
National Renewable Energy Laboratory, Golden, CO, USA
Mike Optis
National Renewable Energy Laboratory, Golden, CO, USA
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Cited
15 citations as recorded by crossref.
- Eddy dissipation rates in the dryline boundary layer R. Solanki et al. 10.1007/s10652-023-09954-w
- Interpretable machine learning for weather and climate prediction: A review R. Yang et al. 10.1016/j.atmosenv.2024.120797
- How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
- Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea J. Kim et al. 10.3390/atmos12070837
- Statistical-dynamical analog ensemble system for real time quantitative precipitation forecasts (QPFs) at local scale in the north-west Himalaya (NWH), India D. Singh et al. 10.1007/s00703-024-01048-6
- 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
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. 10.1016/j.atmosres.2022.106542
- 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
- Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments F. Späth et al. 10.1029/2021JD035697
- 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
- Stability Dependence of the Turbulent Dissipation Rate in the Convective Atmospheric Boundary Layer Y. Lv et al. 10.1029/2023GL103326
- Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. 10.1029/2022MS003072
- Data-driven prediction of mean wind turbulence from topographic data B. Morais da Costa et al. 10.1088/1757-899X/1201/1/012005
- 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
14 citations as recorded by crossref.
- Eddy dissipation rates in the dryline boundary layer R. Solanki et al. 10.1007/s10652-023-09954-w
- Interpretable machine learning for weather and climate prediction: A review R. Yang et al. 10.1016/j.atmosenv.2024.120797
- How generalizable is a machine-learning approach for modeling hub-height turbulence intensity? N. Bodini et al. 10.1088/1742-6596/2265/2/022028
- Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea J. Kim et al. 10.3390/atmos12070837
- Statistical-dynamical analog ensemble system for real time quantitative precipitation forecasts (QPFs) at local scale in the north-west Himalaya (NWH), India D. Singh et al. 10.1007/s00703-024-01048-6
- 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
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. 10.1016/j.atmosres.2022.106542
- 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
- Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments F. Späth et al. 10.1029/2021JD035697
- 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
- Stability Dependence of the Turbulent Dissipation Rate in the Convective Atmospheric Boundary Layer Y. Lv et al. 10.1029/2023GL103326
- Improving Surface Wind Speed Forecasts Using an Offline Surface Multilayer Model With Optimal Ground Forcing J. Feng et al. 10.1029/2022MS003072
- Data-driven prediction of mean wind turbulence from topographic data B. Morais da Costa et al. 10.1088/1757-899X/1201/1/012005
Latest update: 13 Dec 2024
Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind...
