Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-331-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-5-331-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Mar 2020
Research article |
| 06 Mar 2020
| 06 Mar 2020
Radar-derived precipitation climatology for wind turbine blade leading edge erosion
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, New York, USA
Rebecca J. Barthelmie
Department of Earth and Atmospheric Sciences, Cornell University,
Ithaca, New York, USA
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, New York, USA
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Cited
26 citations as recorded by crossref.
- Rain Erosion Load and Its Effect on Leading-Edge Lifetime and Potential of Erosion-Safe Mode at Wind Turbines in the North Sea and Baltic Sea C. Hasager et al. 10.3390/en14071959
- Sub-Regional Variability in Wind Turbine Blade Leading-Edge Erosion Potential F. Letson et al. 10.1088/1742-6596/1618/3/032046
- Leading edge erosion of wind turbine blades: Understanding, prevention and protection L. Mishnaevsky et al. 10.1016/j.renene.2021.01.044
- Mapping of Meteorological Observations over the Island of Ireland to Enhance the Understanding and Prediction of Rain Erosion in Wind Turbine Blades J. Nash et al. 10.3390/en14154555
- Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations J. Visbech et al. 10.5194/wes-8-173-2023
- Rainfall Kinetic Energy in Denmark: Relationship with Drop Size, Wind Speed, and Rain Rate A. Tilg et al. 10.1175/JHM-D-19-0251.1
- Methodology for the energetic characterisation of rain erosion on wind turbine blades using meteorological data: A case study for The Netherlands L. Bartolomé & J. Teuwen 10.1002/we.2597
- The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study N. Hoksbergen et al. 10.3390/ma15031170
- Aerodynamic Characteristics of Wind Turbines Operating under Hazard Environmental Conditions: A Review E. Douvi & D. Douvi 10.3390/en16227681
- Spatiotemporal Variability in Wind Turbine Blade Leading Edge Erosion S. Pryor et al. 10.3390/en18020425
- Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research S. Pryor et al. 10.3390/en15228553
- Quality assessment of the GPM IMERG product for lifetime prediction of turbine blades in complex terrain K. Dimitriadou et al. 10.1088/1742-6596/2767/4/042010
- WRF Modeling of Deep Convection and Hail for Wind Power Applications F. Letson et al. 10.1175/JAMC-D-20-0033.1
- Drop-size-dependent effects in leading-edge rain erosion and their impact on erosion-safe mode operation N. Barfknecht & D. von Terzi 10.5194/wes-10-315-2025
- From Hydrometeor Size Distribution Measurements to Projections of Wind Turbine Blade Leading-Edge Erosion F. Letson & S. Pryor 10.3390/en16093906
- Intense windstorms in the northeastern United States F. Letson et al. 10.5194/nhess-21-2001-2021
- Spatiotemporal variability and trends of hailstorms over India A. Subba Rao et al. 10.1007/s11069-024-06866-w
- Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions S. Pryor et al. 10.3390/en17246285
- Climate change impacts on wind power generation S. Pryor et al. 10.1038/s43017-020-0101-7
- Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer W. Shaw et al. 10.5194/wes-7-2307-2022
- Rain erosion atlas for wind turbine blades based on ERA5 and NORA3 for Scandinavia Á. Hannesdóttir et al. 10.1016/j.rineng.2024.102010
- Lifetime prediction of turbine blades using global precipitation products from satellites M. Badger et al. 10.5194/wes-7-2497-2022
- Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images J. Aird et al. 10.3390/en16062820
- Fast prediction of rain erosion in wind turbine blades using a data-based computational tool J. Gimenez et al. 10.1007/s42241-024-0044-4
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
- Modelling Hail and Convective storms with WRF for Wind Energy Applications F. Letson et al. 10.1088/1742-6596/1452/1/012051
25 citations as recorded by crossref.
- Rain Erosion Load and Its Effect on Leading-Edge Lifetime and Potential of Erosion-Safe Mode at Wind Turbines in the North Sea and Baltic Sea C. Hasager et al. 10.3390/en14071959
- Sub-Regional Variability in Wind Turbine Blade Leading-Edge Erosion Potential F. Letson et al. 10.1088/1742-6596/1618/3/032046
- Leading edge erosion of wind turbine blades: Understanding, prevention and protection L. Mishnaevsky et al. 10.1016/j.renene.2021.01.044
- Mapping of Meteorological Observations over the Island of Ireland to Enhance the Understanding and Prediction of Rain Erosion in Wind Turbine Blades J. Nash et al. 10.3390/en14154555
- Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations J. Visbech et al. 10.5194/wes-8-173-2023
- Rainfall Kinetic Energy in Denmark: Relationship with Drop Size, Wind Speed, and Rain Rate A. Tilg et al. 10.1175/JHM-D-19-0251.1
- Methodology for the energetic characterisation of rain erosion on wind turbine blades using meteorological data: A case study for The Netherlands L. Bartolomé & J. Teuwen 10.1002/we.2597
- The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study N. Hoksbergen et al. 10.3390/ma15031170
- Aerodynamic Characteristics of Wind Turbines Operating under Hazard Environmental Conditions: A Review E. Douvi & D. Douvi 10.3390/en16227681
- Spatiotemporal Variability in Wind Turbine Blade Leading Edge Erosion S. Pryor et al. 10.3390/en18020425
- Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research S. Pryor et al. 10.3390/en15228553
- Quality assessment of the GPM IMERG product for lifetime prediction of turbine blades in complex terrain K. Dimitriadou et al. 10.1088/1742-6596/2767/4/042010
- WRF Modeling of Deep Convection and Hail for Wind Power Applications F. Letson et al. 10.1175/JAMC-D-20-0033.1
- Drop-size-dependent effects in leading-edge rain erosion and their impact on erosion-safe mode operation N. Barfknecht & D. von Terzi 10.5194/wes-10-315-2025
- From Hydrometeor Size Distribution Measurements to Projections of Wind Turbine Blade Leading-Edge Erosion F. Letson & S. Pryor 10.3390/en16093906
- Intense windstorms in the northeastern United States F. Letson et al. 10.5194/nhess-21-2001-2021
- Spatiotemporal variability and trends of hailstorms over India A. Subba Rao et al. 10.1007/s11069-024-06866-w
- Prioritizing Research for Enhancing the Technology Readiness Level of Wind Turbine Blade Leading-Edge Erosion Solutions S. Pryor et al. 10.3390/en17246285
- Climate change impacts on wind power generation S. Pryor et al. 10.1038/s43017-020-0101-7
- Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer W. Shaw et al. 10.5194/wes-7-2307-2022
- Rain erosion atlas for wind turbine blades based on ERA5 and NORA3 for Scandinavia Á. Hannesdóttir et al. 10.1016/j.rineng.2024.102010
- Lifetime prediction of turbine blades using global precipitation products from satellites M. Badger et al. 10.5194/wes-7-2497-2022
- Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images J. Aird et al. 10.3390/en16062820
- Fast prediction of rain erosion in wind turbine blades using a data-based computational tool J. Gimenez et al. 10.1007/s42241-024-0044-4
- Overview of preparation for the American WAKE ExperimeNt (AWAKEN) P. Moriarty et al. 10.1063/5.0141683
1 citations as recorded by crossref.
Latest update: 30 May 2025
Short summary
Wind turbine blade leading edge erosion (LEE) is potentially a significant source of energy loss and expense for wind farm operators. This study presents a novel approach to characterizing LEE potential from precipitation across the contiguous USA based on publicly available National Weather Service dual-polarization RADAR data. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments.
Wind turbine blade leading edge erosion (LEE) is potentially a significant source of energy loss...
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