LAUSR

Uncertainties of the wave state parameters in a sea spray parameterization scheme can be a major source of errors for air-sea turbulent (momentum, sensible and latent) fluxes parameterizations resulting in biases in numerical ocean simulations and forecasts. In this study, we explore applications of the ensemble adjustment Kalman filter (EAKF) data assimilation method to optimize the wave states parameters the 1-D POM ocean model for a full range of wind speed conditions. Thus, we assimilate sea surface temperature (SST) synthesized observations to improve our estimates of the SST analysis and prediction skill from low to high winds. Two types of experiments are conducted. In the first type, in a “twin” experiment framework, the SST “observations” generated by a “truth” model are assimilated into an imperfect, biased model to investigate the extent to which the parameters are able to be optimized, with respect to the “truth” values based on data from Station Papa and the Kuroshio Extension Observatory (KEO). In the second type, real SST observations from KEO are assimilated to obtain optimized parameters. With these optimized parameters, the SST analysis and prediction errors are significantly reduced, especially for high wind conditions.