## 3 Results
### Hydroclimate and Wind Regimes at the Focus Sites
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Location** & & & & & & & & \\
**Label Used** & **Site** & **Instrument Type** & **Sampling Period** & **Hail/Gaupel \({}^{*}\)** & **(RR \textgreater{} 0 mmhr\({}^{-1}\))** & **(RR \textgreater{} 0.2 mmhr\({}^{-1}\))** & **Porentle Values** \\  & & & & & & & (\%) & of RR (mmhr\({}^{-1}\)) \\ \cline{3-8} US SGP & De ARM, Lamont, & OTT Parsivel\({}^{2}\) & & 0.059 & 4.20 & 2.81 & 4.18, 7.72, 31.3 \\  & SGP, USA\({}^{*}\) & 2D Video & January & N/A & 6.28 & 2.40 & 2.45, 4.70, 20.1 \\ \cline{3-8}  & & Impact & & N/A & 6.53 & 1.76 & 1.79, 3.68, 15.5 \\ \cline{3-8} Canada & WEICan, & CSI PWCS100 & October 2018–December 2020 & None reported & 7.47 & 3.81 & 1.99, 3.18, 9.52 \\ \cline{3-8} Coastal UK & WAO, UK & Thies LPM & February 2017–September 2019 & 0.0094 & 12.3 & 5.17 & 1.69, 2.97, 9.44 \\ \hline  & & OTT Parsivel\({}^{2}\) & January & & & & & \\  & & 2016–December 2021 & 0.08 & 20.5 & 14.3 & 2.43, 3.98, 10.1 \\ \cline{3-8} Norway & Bergen, & MRR (100 m & N/A & 18.6 & 10.4 & 1.98, 3.48, 9.37 \\ \cline{3-8} coastal & Bergen, & AGL) & January & N/A & 18.2 & 12.9 & 3.23, 5.60, 19.4 \\ \cline{3-8}  & & MRR (300 m & N/A & 18.1 & 13.4 & 3.76, 6.56, 19.4 \\ \cline{3-8}  & & OTT Parsivel\({}^{2}\) & December 2018–October 2021 & N/A & 6.9 & 4.1 & 1.68, 2.73, 7.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of precipitation statistics at the six primary long-term study sites for all available instruments. PoP denotes probability of precipitation and is computed using two different rainfall rate (RR) thresholds. \({}^{*}\) The values for the three disdrometers at US SGP reflect all 1 min periods when all three instruments reported valid data. Probability of hail/graupel is defined using hail as Weather Codes (WC) 87, 88, 89, 90, except for Bergen where the OTT Parsivel\({}^{2}\) indicates a very high prevalence of WC 87 or WC 88 (2.8% of all observations have one of these two codes). Unless otherwise stated the sampling intervals over which the data are averaged is 1-min. See DSD for these sites in Figures 5–9. The DSDs from the Parsivel\({}^{2}\) dishorrometer at the US SGP site sampled for different RR emphasize a long-recognized feature of precipitation: That, although there is a high degree of event-to-event variability (Figure 5) [139], on average higher RR are associated with increases in droplet number plus increases in the median droplet diameter (\(D_{0}\)) and mass-weighted mean diameter (\(D_{m}\)) due to the presence of more larger droplets [57]. This finding is equally applicable to measured DSD from the five other sites (Figures 6-9). Neither the Best nor Marshall-Palmer approximations of the DSD fully represent the shape of the observed DSD from the dishorometers deployed at US SGP, or Norway, or coastal UK or Denmark (Figures 5-7 and 9). The Marshall-Palmer approximation yields DSD that are in closer accord with the observations but tends to overestimate the number of smaller droplets and underestimates the number of larger droplets. The underestimation is evident for \(D>2.5\) mm for all three RR at the US SGP site, for \(D>1.25\) mm in Denmark and \(D>0.7\) mm at coastal Norway. Data from the Thies LPM operated WAO (coastal UK) exhibit DSD that are lower than either Best and Marshall Palmer approximations up to droplet \(D\) of 1.5 mm. However, larger droplet (\(D>3\) mm) number concentrations are overestimated by the Best approximation and underestimated by Marshall-Palmer (Figure 6). These results indicate that assessment of accumulated kinetic energy transfer to wind turbine blades and the resulting materials stress, and blade lifetimes that use measured RR combined with assumed DSD are likely to result in inaccurate assessments that may be mis-leading to windfarm developers. We recommend that where possible such any such assessments be predicated on either on-site or near-by DSD measurements.