## 2 Materials and Methods
### Statistical Methods  
Analyses of the frequency and intensity of precipitation (RR) and the frequency of occurrence of hail across the six primary sampling locations presented herein are summarized using the following statistical approaches. The probability of hail is computed as the ratio of the frequency with which the hail flag is reported by the sensor divided by the total number of observational periods for which data are available. The probability of precipitation (PoP) is described using two different RR thresholds for precipitation detection; 0 mmhr\({}^{-1}\) and 0.2 mmhr\({}^{-1}\). The latter threshold is used to enable comparisons across instruments that may have different minimum detectable limits for precipitation. To characterize the upper percentiles of the probability distribution of RR, we also report the 90 th, 95 th and 99 th percentile RR (mmhr\({}^{-1}\)) during all periods with non-zero RR. These values thus provide information regarding RR when the heaviest precipitation is occurring. To illustrate the importance of high-frequency sampling of precipitation in the context of wind turbine blade LEE, data from the optical disdrometer at the US SGP site [95] (Figures 2a and 4a) are subject to an analysis wherein each hour of data where any precipitation occurred are sampled to compute the maximum RR with sampling durations of 1-, 5-, 10-, 20-, 30- and 60-min. The results are then summarized to compute the 10 th, 20 th,... 90 th percentile values of the resulting RR for each averaging period. Figure 4: **(a,b)** Maps of locations from which data are presented (see details in Table 1). **(c)** Probability distributions of wind speeds at 100 m AGL at those locations based on once-hourly output from the ERA5 reanalysis for 1979–2018. **(d)** Observed mean droplet size distributions for four of the locations based on measurements with the optical disdrometers for liquid precipitation and RR of 6–11 mmhr\({}^{-1}\). Colors used in frames **(c,d)** indicate the different locations and are as shown in frames **(a,b)**. Non-parametric Spearman (rank) correlation coefficients (\(r_{\text{s}}\)) are reported as a measure of the degree of agreement between two data sets. This measure of correlation is appropriate for use with data, such as RR, that are not Gaussian distributed [123]. Linear fitting is undertaken and is expressed in terms of the slope (\(m\)) and the intercept (\(c\)) of the general linear model, where \(x\) is the independent variable and \(y\) is the dependent variable:  
\[y\ =\ mx+c \tag{18}\]  
To illustrate RR and DSD from different instrument metrologies we examine data from three disdrometers deployed at the US SGP site (Figure 4a). The degree of closure between RR is assessed using three different thresholds (RR > 0.1, 1 and 10 mmhr\({}^{-1}\)) using contingency-table based metrics; the Hit Rate (HR) and the False Alarm Rate (FAR) [123]. The HR, which is also known as the proportion correct, is the ratio of the number of times data from the two instruments agree on precipitation occurrence for each of the thresholds, to the total number of times that the reference instrument (in this case the OTT Parsivel\({}^{2}\)) exhibited a RR in excess of that threshold. The FAR, is a measure of the number of times both instruments indicated RR below the specified threshold, to the number of times the reference instrument indicated RR below the specified threshold. Thus, perfect agreement would result in HR = 1 and FAR of 0. Wind speed data from both the ERA5 reanalysis [124] and NORA3 hindcast [125] for a height of 100 m AGL [124] plus observationally derived wind speeds at each site at/close to typical wind turbine hub-heights are presented.