Presented at the 2018 Ocean Salinity Science Team and Salinity Continuity Processing MeetingRain-induced freshening of the top meter of the ocean can cause an error in validating satellite salinity measurements with in situ salinity data measured at depths below the surface fresh layer. In order to determine the importance of this error, the Rain-Impact Model (RIM) was developed (Santos-Garcia et al., 2014). RIM is a semi-empirical model that estimates the rain-induced freshening between the surface and a depth of 1 m using global rain products and a one-dimensional model of vertical diffusion in the upper ocean. In regions where there is significant rainfall and low wind speeds, it has been demonstrated that RIM can explain the observed difference between the in situ measurements and satellite-retrieved SSS (Santos-Garcia et al., 2016; Santos-Garcia et al., 2014). However, RIM is less effective in regions with intermediate wind speeds (Santos-Garcia et al., 2016). The magnitude of the salinity gradient and the lifetime of the fresh lens combine to determine its effect on satellite salinity measurements. Both the magnitude and lifetime are functions of the rain rate, total amount of rainfall, and the vertical mixing at the ocean surface. For example, rain events with high peak rain rates that occur when wind speeds are low generate fresh lenses with strong near-surface salinity gradients that can have lifetimes on the order of hours. In contrast, rain events at high wind speeds have smaller gradients and shorter lifetimes because vertical mixing is larger. Similarly, rain events with long duration and low peak rain rates generate fresh lenses with smaller vertical gradients. Therefore, ensuring that RIM provides as accurate a description of vertical mixing as possible is important for the model to be applicable over a wide range of oceanic conditions. As currently configured RIM assumes that the vertical mixing that drives the evolution of the salinity gradient in the fresh lens is constant as a function of wind speed. RIM estimates of rain-induced freshening could be improved by extending RIM to include the effects of wind on the vertical mixing. As a first step in this, an empirical parameterization of the vertical turbulent diffusivity in terms of wind speed and rain rate has been developed using the numerical data from Drushka et al. (2016). This parameterization can be incorporated into RIM to allow it to include the effects of wind speed on the error caused by surface freshening.