## 3 Results
### Influence of Measurement Strategies and Instrument Metrologies
For example, for a hydrometeor of approximately 2 mm diameter the mean number density for all periods when both the optical and video disdrometer exhibited RR > 10 mmhr\({}^{-1}\), are 0.0178 m\({}^{-3}\) mm\({}^{-1}\) from the Parsivel\({}^{2}\) and 0.5282 from the video disdrometer. Similar results for when both the impact and optical disdrometers exhibit RR > 10 mmhr\({}^{-1}\) indicate a mean droplet abundance of 0.0274 and 0.0222 m\({}^{-3}\) mm\({}^{-1}\). In contrast, a previous study [153] comparing data from two 2-D video disdrometers and two OTT Parsivel\({}^{1}\) disdrometers found that total accumulated rainfall during extended periods of precipitation (many hours) from the optical instrument was biased high particularly during high RR (RR > 20 mmhr\({}^{-1}\)). However, consistent with the data from the US SGP site, the RR during co-identified precipitation periods are correlated. The Pearson (parametric) correlation coefficient (r) for 3 min running mean RR from the optical and video disdrometers was 0.87 [153]. The difference in inferences these two analyses (US SGP versus the previous study [153]) may be partially attributable to use of different generations of OTT Parsivel disdrometers but it may also illustrate the importance of the decision to use a screening tool to exclude droplets with atypically low fall velocities from the US SGP optical disdrometer. This again emphasizes the need to develop best-practice for the processing of data from these instruments. The following common inferences can be drawn from these and other instrument intercomparison studies. First, it appears that there is a persistent difference between ground-based disdometers and MRR in terms of the droplet size distribution with MRR showing higher values for smaller droplets, while the ground-based disdometers exhibit higher concentrations for larger diameter droplets. The crossing point appears to be in the \(D\) range of 0.5 to 1 mm. This tends to lead to a negative bias in RR from the MRR. Second, while there is some dependence on the precise precipitation regime, the OTT Parsivel instrument appears to exhibit DSD with a greater presence of larger droplets than the Theis LPM. Third, DSD discrepancies between instruments are amplified at high RR. We further note that while many studies find disdometers tend to underestimate accumulated precipitation relative to rain gauges this is not uniformly the case. The instrument comparisons based on primary data analysis for the US SGP facility and previous work summarized in Table 3 also provide important context for the comparisons provided earlier between DSD from the OTT Parsivel\({}^{2}\) disdrometer and the Best and Marshall-Palmer approximations. In those comparisons, data from the OTT Parsivel\({}^{2}\) instruments tend to indicate that both Best and Marshall-Palmer underestimate the droplet number concentrations for large \(D\). Comparisons between the 2 DVD and OTT Parsivelat US SGP suggest that for high RR the under-counting of large droplets by the Best and Marshall-Palmer approximations may be even more pronounced. Mean DSD from the MRR and Parsivel2 didrometer at the Norway site when conditionally sampled for a moderate RR of 6-11 mmhr\({}^{-1}\) also exhibit notable differences (Figure 7, Table 3). While some of the difference might be attributable to sampling of different data periods and thus different precipitation events, given the long time series it is likely that such an effect is small. Thus, \(>\)5-fold difference in droplet number concentrations for \(D>2\) mm (Figure 7) for a moderate RR of 6-11 mmhr\({}^{-1}\) appears to derive primarily from differences in the measurement height and fundamental differences due to metrology. Consistent with expectations, the differences between mean DSD from the OTT Parsivel2 and MRR for \(D\)\(\sim\)2.5-4 mm exceed those for the ground-based disdometers at the US SGP site (Figure 16).