# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 3 Results
### Lightning Analysis  
#### 3.2.1 Overall Statistics  
The statistics for track matchups with WWLLN are shown in Table 2. Recall that the WWLLN period of analysis was August-October during 2018 and 2019. Surprisingly, the CYGNSS FDS winds not associated with lightning had slightly higher speeds (by \(\sim\)0.2 m s\({}^{-1}\)) compared to winds associated with lightning. This difference was small but greatly exceeded the 99% confidence intervals for the mean of either category. The difference was also robust if the restrictions were placed, either requiring RCG to exceed 10 or the rain rate to be less than 6 mm h\({}^{-1}\) (to control for any possible rain effects). YSLF winds also showed a similar pattern, but with reduced differences between the two categories. Figure 4: (**a**) Monthly time series of RMSD and mean offsets between CYGNSS FDS and ASCAT-A wind speeds, for zero and nonzero IMERG rain rates. (**b**) Monthly time series of RMSD and mean offsets between CYGNSS FDS and ASCAT-B wind speeds, for zero and nonzero IMERG rain rates. All data are for May 2017 through December 2019. In a sensitivity study to gauge differences in wind variability in precipitation with and without lightning, the standard deviation of CYGNSS FDS wind speeds in each contiguous region of precipitation along a track (e.g., each region marked by a magenta bar in Figure 2b) was analyzed. The results were similar to the mean wind speeds, with winds in precipitation without lightning about 30% more variable than in precipitation with lightning. Compared to CYGNSS, an opposite pattern was observed for ASCAT winds, which were stronger when WWLLN lightning was present (by \(\sim\)0.4 m s\({}^{-1}\)) than without. However, the magnitude of this difference decreased to only \(\sim\)0.1 m s\({}^{-1}\) when controlling for higher rain rates. Note that when ASCAT-A and -B winds were both matched to a particular CYGNSS specular point, all of the analyses in this Section 3.2 defaulted to ASCAT-A. As seen in Section 3.1, A and B are well calibrated against each other so this choice had no significant impact on the results compared to using B (or averaging) instead. The largest differences between WWLLN lightning and no WWLLN lightning were for IMERG rain rates, which were larger by a factor of 3 on average when WWLLN lightning was present. In summary, the overall trend for CYGNSS was that winds were slightly stronger and more variable when WWLLN lightning was not present, even as precipitation rate increased significantly when lightning occurred. The opposite wind trend was observed in ASCAT winds, but there were fewer samples available, and the ASCAT difference was affected when controlling for precipitation rate. Since WWLLN is most sensitive to CG lightning, it was possible that some intracloud- (IC-) only thunderstorms were being mistakenly included in the \"winds without lightning\" categories. Because GLM-16 is sensitive to total (CG + IC) lightning, it should be able to control for that uncertainty. Table 3 shows the equivalent statistics as Table 2, but for GLM lightning. The period of GLM analysis was July-December 2018 and 2019. This lengthened period enabled comparable sample numbers to the WWLLN analysis.