## 1 Introduction  
Maritime convection exhibits characteristics on a wide variety of spatial and temporal scales. Unlike continental storms that exhibit strong diurnal variability in energy exchanges, maritime storms are characterized by fluxes with less variability and lower peak values. Precipitation radars onboard satellites have revealed that mesoscale convective systems (MCSs; [1]) are globally distributed and constitute an important source of rainfall [2, 3] and severe weather within the tropics and sub-tropics. They can have different morphological characteristics but they all contain convective and stratiform precipitation [4]. Maritime MCSs are characterized by less intense convective region but larger stratiform region than the continental ones [4]. The precipitation process can produce strong downdrafts through both precipitation loading and evaporation of rain into a dry mid-level environment, transporting cooler and drier air to the boundary layer [5]. As the gravity-driven downdraft reaches the surface, it spreads out laterally in a turbulent manner, increasing the flow velocity closer to the surface. As the fast propagating flow surges ahead of the convection, it forms a distinctive boundary that separates cooler air from the surrounding air, namely the outflow boundary or gust front. Gust fronts and other wind discontinuities are of key importance in high-impact weather events, as they focus moisture convergence and lead to the development of new precipitation systems [6; 7; 8; 9] that can ultimately sustain mesoscale-organized convection [10; 11]. A well-defined form of outflow boundary is generated when convective and mesoscale downdrafts form a density current on the surface, known as a cold pool [12]. Over the past several decades, satellite scatterometers operating at different wavelengths have been providing valuable sea surface wind field information. Sea surface temperature effects are more pronounced when receiving and transmitting both at vertical polarization and at higher incidence angles, however C-band scatterometers are rather insensitive to such effects compared to Ku-bands [13]. On the other hand, Ku-band scatterometers are more sensitive to wind speed (especially variations at low wind speed) and direction, but they are subject to much higher variability than C-band scatterometers [14]. Therefore the C-band Advanced Scatterometer (ASCAT) is expected to provide a more robust wind retrieval associated with the outflow boundaries characteristics near precipitation than scatterometer satellite-1 (Scatast-1; [15; 16; 17]). In case the cold pool edges are found within the precipitation itself, scatterometer retrievals may be degraded. Although ASCAT operates at C-band, rain contamination can be significant in case of heavy precipitation, resulting in ocean splash, which alters the backscattered signal [18]. Detailed examination of the wind solutions can help to mitigate such errors. ASCAT wind retrievals are performed at a 50-km resolution, with 25-km cell spacing, and a 25-km resolution, hereinafter referred as the coastal product, with 12.5-km cell spacing. The surface winds are generated using a geophysical model function (GMF) that relates normalized radar cross section (\(\sigma_{0}\)) to the observing geometry and the mean wind vector in a cell. Due to the ambiguous solutions of the GMF with respect to the wind direction, the maximum likelihood estimation (MLE) principle is implemented to rank the probabilities of these solutions. During the 2 DVAR process an analyzed wind field is obtained [19]. Subsequently, at each wind vector cell, the wind vector ambiguity solution that lies closest to the analyzed field results in a final selected scatterometer wind field. In order to minimize the noise in the \(\sigma_{0}\) data (level 1b), a Hamming window [20] is applied to derive the 50-km product, so that the main contribution of weighted \(\sigma_{0}\) on a 100 km by 100 km area originates from the central 50 km by 50 km.