on the location, long-term wind speed product used, or instrument from which reference observations are taken. To include this uncertainty component in a systematic way across the 472 locations considered in our analysis, we adopt an ensemble uncertainty approach [@taylor2009wind; @zhang2015comparison] and use as proxy the variability of the wind resource between different reanalysis products. Therefore, at each Monte Carlo iteration at each site, we randomly select wind resource data from one of the three considered reanalysis products.

-   Linear regression model uncertainty. We adopt a novel way, directly enabled by the use of Monte Carlo, to incorporate this uncertainty component in the operational AEP assessment. We sample the regression slope and intercept values from a multivariate normal distribution centered on their best-fit values and covariance matrix equal to one of the best-fit parameters. The diagonal terms in the covariance matrix are given by the square of the slope and intercept standard errors. For a regression model between an independent variable, $x$, and a dependent variable, $y$, the standard error of the regression is defined [@iso1995guide] as: $$e_y = \sqrt{\frac{\sum \left(y_i - \hat{y_i} \right)^2}{n-2}},$$ where $\hat{y_i}$ is the regression-predicted value for $y_i$ and $n$ is the number of data points 