Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

WRF Modeling of Deep Convection and Hail for Wind Power Applications

TitleWRF Modeling of Deep Convection and Hail for Wind Power Applications
Publication TypeJournal Article
Year of Publication2020
AuthorsLetson, F., Shepherd T. J., Barthelmie R. J., and Pryor S. C.
JournalJournal of Applied Meteorology and Climatology
Volume59
Number10
Pages1717-1733
Abstract / Summary

Deep convection and the related occurrence of hail, intense precipitation, and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading-edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the U.S. southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) Model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modeling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements, and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates, and precipitation totals show good agreement with observations. The occurrence of precipitation during power-producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modeled with a mean proportion correct of 0.77 and an odds ratio of 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry, and there is evidence for positive model bias in cloud reflectivity.

URLhttp://dx.doi.org/10.1175/jamc-d-20-0033.1
DOI10.1175/jamc-d-20-0033.1
Journal: Journal of Applied Meteorology and Climatology
Year of Publication: 2020
Volume: 59
Number: 10
Pages: 1717-1733
Publication Date: 10/2020

Deep convection and the related occurrence of hail, intense precipitation, and wind gusts represent a hazard to a range of energy infrastructure including wind turbine blades. Wind turbine blade leading-edge erosion (LEE) is caused by the impact of falling hydrometeors onto rotating wind turbine blades. It is a major source of wind turbine maintenance costs and energy losses from wind farms. In the U.S. southern Great Plains (SGP), where there is widespread wind energy development, deep convection and hail events are common, increasing the potential for precipitation-driven LEE. A 25-day Weather Research and Forecasting (WRF) Model simulation conducted at convection-permitting resolution and using a detailed microphysics scheme is carried out for the SGP to evaluate the effectiveness in modeling the wind and precipitation conditions relevant to LEE potential. WRF output for these properties is evaluated using radar observations of precipitation (including hail) and reflectivity, in situ wind speed measurements, and wind power generation. This research demonstrates some skill for the primary drivers of LEE. Wind speeds, rainfall rates, and precipitation totals show good agreement with observations. The occurrence of precipitation during power-producing wind speeds is also shown to exhibit fidelity. Hail events frequently occur during periods when wind turbines are rotating and are especially important to LEE in the SGP. The presence of hail is modeled with a mean proportion correct of 0.77 and an odds ratio of 4.55. Further research is needed to demonstrate sufficient model performance to be actionable for the wind energy industry, and there is evidence for positive model bias in cloud reflectivity.

DOI: 10.1175/jamc-d-20-0033.1
Citation:
Letson, F, T Shepherd, R Barthelmie, and SC Pryor.  2020.  "WRF Modeling of Deep Convection and Hail for Wind Power Applications."  Journal of Applied Meteorology and Climatology 59(10): 1717-1733.  https://doi.org/10.1175/jamc-d-20-0033.1.