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Polarimetric passive measurements of sea surface brightness temperature have been proposed as a means of inferring wind speed and direction. A limited set of circle flight measurements of the wind direction dependence has demonstrated that there may be enough independent information in the polarimetric measurement to make this feasible. A predictive model by Yueh reproduces the observations closely enough that the dominant mechanisms are probably included. Optimizing the fit of this type of model with a growing dataset is made difficult by the close coupling of the Yueh approach with a particular wind-wave spectral model. This makes it unclear as to how to parameterize the model, a prerequisite of any systematic optimization technique. Here, we present an alternate formulation, using the Baum-Irisov model to isolate the particular properties of the wavy ocean surface that affect the radiance, in the form of six discrete parameters. Iterative local linearization techniques are used to optimize the values of these parameters with respect to any large dataset. The parameters are functions of only two variables (radiometer frequency and wind speed), while the effects of incidence angle, polarization, sea surface temperature, salinity, and wind direction are derived front the model. Since the data need only be binned by these two variables, a relatively small number of on-orbit/ground-truth datasets is required to evaluate the parameters.