## 5 Summary and Conclusions  
In this study we applied a novel technique based on the wind gradient, termed gradient feature method [PERSON] et al. (2016) to identify outflow boundaries induced by MCSs from scatterometer data. Such systems produce intense precipitation at the ocean's surface, which can degrade the quality of scatterometer retrievals. To mitigate these effects we adjust the retrievals of rain-afflicted wind vector cells by examining the available solution obtained from the GMF and ground-based radar single-Doppler analysis wind retrievals. In order to assess the ability of scatterometers to accurately captured low-level wind features associated with convective systems, we examine the response of ASCAT wind retrievals on the different background winds as a result of the ambiguity removal process. Leveraging the close time interval of overpasses between the MetOp-A and -B satellites, the GF method was applied over an MCS located offshore of Houston for adjusted wind retrievals at spatial resolutions of 50-, 25- and 7-km. An increase in the wind speed was observed in front of the MCS by all resolutions, whereas the change in the wind direction was more variable. Increased wind gradient magnitude was also observed by all resolutions, indicating the ability of the method to detect outflow boundaries. Noticeably, the wind gradient magnitude for the 50-km product (\(10^{-4}\) s\({}^{-1}\)) was one order of magnitude less than the 25- and 7-km (\(10^{-3}\) s\({}^{-1}\)), since smoothing at the coarser resolution can obscure sudden changes in the wind speed and/or direction, thus leading to much smoother variation between adjacent wind vector cells. Differences in the wind gradient field were also observed between the 25-km and 7-km product, with the latter capturing fragments and larger variations in magnitude along the front edge of the MCS. At all three resolutions, a gradient feature was captured that encompassed a large area of the MCS and its outflow boundary. The area of the GF increased with resolution as more wind vectors were available close to the coast. The importance of spatial resolution became apparent by looking at the location of the GF-detected outflow boundary relative  
Figure 8: UHR wind vectors are depicted in black and divergence field in green and dark pink for **(a)** MERRA-2 and **(b)** RAP background winds at 02-51 UTC (ASCAT-A) on 28 May 2016. Similarly, **(c)** MERRA-2 and **(d)** RAP background winds at 03:42 UTC (ASCAT-B). KHGX rainfall rate is depicted in light blue filled contours and reported at 02:57 UTC for ASCAT-A and 03:46 UTC for ASCAT-B. to the MCS. In the 25-km product, the gradient feature included an area that extended further outwards from the 1 mm h\({}^{-1}\) rainfall rate border, whereas in the 7-km, the feature was within the edge of the precipitation. This indicated that the high resolution product identified a more detailed structure of the outflow boundary that can be associated with its evolution. Additionally, smaller-scale features at 7-km resolution were also detected by the GF method, however, their nature was uncertain due to a lack of buoy observations over the respective area. Scatterometer wind retrievals depend on the background wind field obtained from weather models, which is an input in the ambiguity selection process. Global models are characterized by low spatial resolution and therefore are unlikely to detect mesoscale phenomena. In this study, 25-km ECMWF and MERRA-2 winds failed to accurately simulate winds near the MCS, instead showed uniform wind speed and direction over the domain. The 13-km RAP model was able to detect surface-wind modulations induced by the MCS throughout the overpasses of ASCAT-A and -B. Buoy wind measurements along the coast of the domain were also in better agreement with the RAP winds, providing confidence that the simulated wind field was more correct.