Sea level determination via satellite altimetry is subject to numerous error sources. One of these is the sea state bias where changing surface wave conditions alter an altimeter's estimate of mean sea level. Recent work suggests that present day methods for correcting this bias, based solely on wave and wind information from the altimeter, may be improved if additional surface gravity wave field measurements become available. This paper tests this hypothesis by developing several new sea state bias correction models using a year long combination of Jason-1 data with wave field statistics generated from an hindcast of the WaveWatch3 ocean wave model. Each candidate model is produced in the same manner; through a nonparametric mapping between Jason-1 sea surface height anomaly estimates and two correlatives. The first is always the significant wave height from Jason-1 and the model differences come through choice of the second variable. Past studies dictate our selection of these second parameters and they include terms related to the local wind speed, swell energy, wave age, wave period, and wave slope. Evaluation of model skill is conducted in term of explained variance. Several candidate models do indicate promise for wave model use in future empirical developments. Results show that in the low latitudes the models developed using the swell height and mean period, modestly outperform the operational parameterisation of the bias model. Thus this study indicates that systematic regional error in the present sea level corrections may be improved by inclusion of wave model information.