Articles | Volume 5, issue 1
Wind Energ. Sci., 5, 331–347, 2020
https://doi.org/10.5194/wes-5-331-2020

Special issue: Wind Energy Science Conference 2019

Wind Energ. Sci., 5, 331–347, 2020
https://doi.org/10.5194/wes-5-331-2020
Research article
06 Mar 2020
Research article | 06 Mar 2020

Radar-derived precipitation climatology for wind turbine blade leading edge erosion

Frederick Letson et al.

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Cited articles

Allen, J. T. and Tippett, M. K.: The characteristics of United States hail reports: 1955–2014, E-Journal of Severe Storms Meteorology, 10, 1–31, 2015. 
Allen, J. T., Tippett, M. K., and Sobel, A. H.: An empirical model relating US monthly hail occurrence to large-scale meteorological environment, J. Adv. Model. Earth Sy., 7, 226–243, 2015. 
Allen, J. T., Tippett, M. K., Kaheil, Y., Sobel, A. H., Lepore, C., Nong, S., and Muehlbauer, A.: An extreme value model for US hail size, Mon. Weather Rev., 145, 4501–4519, 2017. 
Alpert, J. C. and Kumar, V. K.: Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system, Mon. Weather Rev., 135, 1090–1109, 2007. 
Amirzadeh, B., Louhghalam, A., Raessi, M., and Tootkaboni, M.: A computational framework for the analysis of rain-induced erosion in wind turbine blades, part I: Stochastic rain texture model and drop impact simulations, J. Wind Eng. Ind. Aerod., 163, 33–43, 2017a. 
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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.