## 3 Results
### Influence of Measurement Strategies and Instrument Metrologies  
As described above, precipitation events exhibit a wide range of durations and fluctuating RR during individual events. This is illustrated using data from OTT Parsivel2 instruments operating in the US SGP and in Denmark. Ten percent of all 60 min periods when precipitation occurred at US SGP exhibit a 1 min maximum RR \(>\) 4.5 mmhr\({}^{-1}\) (Figure 14). When the time series are averaged to 30 min sampled periods the 90 th percentile RR (i.e., that exceeded by 10% of the data) is 2.1 mmhr\({}^{-1}\). Given the non-linear dependence of the DSD on RR (see Equations (6)-(10) and Figures 5-9), use of 30 min or 60 min average RR will greatly under-sample the presence of large droplets and thus kinetic energy transfer into the blades. Data from two OTT Parsivel2 instruments operated at the Denmark site with different integration periods also illustrates the expected dependence of RR on the  
Figure 13: Heatmaps of the joint probabilities (in %, colorbar and also numeric values shown in each cell) of RR and wind speeds at onshore Denmark. The joint probabilities are computed for periods with liquid precipitation. averaging period. Based on 1 min sampling, the 99 th percentile RR is 8.6 mmhr\({}^{-1}\). When sampled at 10 min the 99 th percentile RR is 7.2 mmhr\({}^{-1}\). For accurate estimation of kinetic energy transfer to wind turbine blades it is thus recommended to use very high frequency (ideally 1-min) sampling of DSD and RR. Data from the OTT Parsivel\({}^{2}\) disrometer at the US SGP also emphasizes important differences in DSD as a function of hydrometeor phase. The mean DSD when the data are conditionally sampled to select all 1133 periods when the WC indicates the presence of hail and/or graupel (mean RR \(\sim\)14 mmhr\({}^{-1}\)), and that from an equal number of periods with similar RR but only liquid hydrometeors. The results show clear evidence of a considerably higher frequency of large (\(D\) > 3 mm) droplets when hail is indicated (Figure 15). While this comparison is contingent on correct assignment of the WC by the disrometer software, it reemphasizes the importance of research to better characterize DSD for hail and also that, even neglecting the influence from hydrometeor hardness, hail events are therefore likely be associated with higher kinetic energy transfer into the blade and potentially greater induced material stresses. Thus, we recommend enhancing efforts to include hail into material fatigue experiments. The hail DSD shown in Figure 15 also emphasizes the need for such experiments to include hailstones with diameters below 15 mm that was the lower limit considered in the study of [PERSON] [50]. Given the challenges in use of frozen water in such analyses particularly for smaller \(D\)[50], it may be preferable to use polylactic acid pellets to replicate the effects of hail impacts on wind turbine blades, since they have approximately the right hardness and tend to shatter on impact [130]. The regional differences in hydroclimates and the joint probability distributions of wind speeds and RR implied by the data summaries described in Section 3.1 have important implications for the spatial variability of WT blade LEE. However, it is important to note that they are also subject to differences associated with instrument metrology. Efforts to quantify this effect in the hydroclimatic parameters based on new data analyses are described below and presented in the context of the existing literature. Figure 14: Rainfall rate (RR, in mmhr\({}^{-1}\)) as a function of averaging period based on 4 years of 1 min data collected with the OTT Parsivel\({}^{2}\) disrometer at the US SGP. Each 60 min time window when precipitation occurred is sampled to determine the maximum RR for averaging periods of 1, 5, 10, 20, 30 and 60 min. The results are presented as the 10 th:10:90 th percentile values for each averaging period. A number of previous studies have evaluated the relative accuracy of different disorders.