# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 4 Discussion  
Focusing first on the existing broad agreement between CYGNSS and matched ASCAT-A and -B wind data near precipitation systems, both types of sensors--despite their very different approaches to wind retrievals--agree that when light precipitation is present wind speeds are approximately 1 m s\({}^{-1}\) greater than background (Figure 5). This is largely consistent with the independent studies of [3,4]. Additionally, regardless of the analysis approaches used in this study, the mean wind speeds from CYGNSS and matched ASCAT agree to within approximately 0.5 m s\({}^{-1}\) (Tables 1-4). Finally, and most importantly for this paper, both CYGNSS and ASCAT agree that--if there is a difference in wind speeds near lightning-producing and non-lightning-producing precipitation systems, in the aggregate this effect is small (-0.2-0.6 m s\({}^{-1}\)), and the overall percentage change in wind speed is much smaller than the observed percentage change in precipitation rate when lightning is occurring (i.e., -50% change in mean wind speed vs. \(>\)200% change in precipitation rate; Tables 2-4). These findings demonstrate the utility of combining distinct satellite-based precipitation and wind datasets to infer wind characteristics near precipitation systems. Despite the above agreement, there are important differences observed that speak to remaining uncertainties in wind retrievals in and near rainfall. For example, regardless of whether lightning is occurring, ASCAT shows a stronger positive relationship between wind speed and IMERG precipitation rate compared to CYGNSS (Figure 5). Moreover, CYGNSS and ASCAT tend to fundamentally disagree on the sign of the change in wind speed between precipitation systems with and without lightning--ASCAT tends to observe slightly stronger winds when lightning is present, while CYGNSS tends to observe the opposite. There is substantial evidence (e.g., Tables 1-4, Figures 3-6) that this disagreement stems from fundamental differences in winds measurements between the two sensors in rain. Because CYGNSS is an L-band sensor, it is thought to be potentially less sensitive to rain impacts compared to the C-band ASCAT [20]. The relatively larger sensitivity of the ASCAT results to precipitation rate compared to the CYGNSS results (e.g., Tables 2-4, Figures 5 and 6) supports this inference. Indeed, as [16, 17, 18] have shown, ASCAT wind retrievals in rain rates above \(\sim\)6 mm h\({}^{-1}\) are likely less accurate. Thus, it makes sense that controlling for precipitation in this study's dataset showed much greater response in the ASCAT dataset. Note also that disagreements between CYGNSS and ASCAT results were reduced when controlling for rain. However, CYGNSS likely does experience some impact on its wind speed measurements in rainfall, particularly when wind speed is low [38, 39, 40, 41]. Moreover, uncertainties in the representativeness of both sensors' geophysical model functions (GMFs) near convection need to be considered. Apart from effects of signal attenuation and capillary waves from rain splash, winds near convection tend to be highly variable and change much faster than the ocean surface waves influenced by background winds can react [42]. Such phenomena will affect the wind retrievals from both types of sensors, and likely causes some of the observed differences since the CYGNSS and ASCAT observations are not matched perfectly in time (approximately a \(\pm\)45-min window, based on the matching criteria described in Section 2.2). Tropical cyclones were included in the analysis but were not broken out in the statistics. As argued in Section 2.1, they likely did not significantly impact the results due to their rarity relative to conventional tropical precipitation systems. However, it is possible that tropical cyclones were responsible for a small part of the observed ASCAT/CYGNSS discrepancies.