Articles | Volume 19, issue 7
Atmos. Chem. Phys., 19, 4367–4382, 2019
https://doi.org/10.5194/acp-19-4367-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 19, 4367–4382, 2019
https://doi.org/10.5194/acp-19-4367-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
04 Apr 2019
Research article
| 04 Apr 2019
Spatial and temporal variability of turbulence dissipation rate in complex terrain
Nicola Bodini et al.
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Cited
12 citations as recorded by crossref.
- Effect of wind veer on wind turbine power generation L. Gao et al. 10.1063/5.0033826
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Atmospheric diffusion profiles and health risks of typical VOC: Numerical modelling study T. Zhang et al. 10.1016/j.jclepro.2020.122982
- Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1029/2019GL082636
- Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea J. Kim et al. 10.3390/atmos12070837
- Estimation of turbulence dissipation rate from Doppler wind lidars and in situ instrumentation for the Perdigão 2017 campaign N. Wildmann et al. 10.5194/amt-12-6401-2019
- Mountain waves can impact wind power generation C. Draxl et al. 10.5194/wes-6-45-2021
- Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al. 10.5194/wes-5-959-2020
- Remote-sensing and radiosonde datasets collected in the San Luis Valley during the LAPSE-RATE campaign T. Bell et al. 10.5194/essd-13-1041-2021
- Turbulence dissipation rate estimated from lidar observations during the LAPSE-RATE field campaign M. Sanchez Gomez et al. 10.5194/essd-13-3539-2021
- Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain? N. Bodini et al. 10.5194/gmd-13-4271-2020
12 citations as recorded by crossref.
- Effect of wind veer on wind turbine power generation L. Gao et al. 10.1063/5.0033826
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Atmospheric diffusion profiles and health risks of typical VOC: Numerical modelling study T. Zhang et al. 10.1016/j.jclepro.2020.122982
- Influence of atmospheric stability on wind farm performance in complex terrain W. Radünz et al. 10.1016/j.apenergy.2020.116149
- U.S. East Coast Lidar Measurements Show Offshore Wind Turbines Will Encounter Very Low Atmospheric Turbulence N. Bodini et al. 10.1029/2019GL082636
- Characteristics of Energy Dissipation Rate Observed from the High-Frequency Sonic Anemometer at Boseong, South Korea J. Kim et al. 10.3390/atmos12070837
- Estimation of turbulence dissipation rate from Doppler wind lidars and in situ instrumentation for the Perdigão 2017 campaign N. Wildmann et al. 10.5194/amt-12-6401-2019
- Mountain waves can impact wind power generation C. Draxl et al. 10.5194/wes-6-45-2021
- Decreasing wind speed extrapolation error via domain-specific feature extraction and selection D. Vassallo et al. 10.5194/wes-5-959-2020
- Remote-sensing and radiosonde datasets collected in the San Luis Valley during the LAPSE-RATE campaign T. Bell et al. 10.5194/essd-13-1041-2021
- Turbulence dissipation rate estimated from lidar observations during the LAPSE-RATE field campaign M. Sanchez Gomez et al. 10.5194/essd-13-3539-2021
- Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain? N. Bodini et al. 10.5194/gmd-13-4271-2020
Latest update: 09 Feb 2023
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather...
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