A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly due to a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this paper we use the WRF single column model (SCM), coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART), to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining z<
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, the time-varying sea-surface roughness length, we conduct four WRF-SCM/DART experiments over the October-December 2006 period. The two methods for determining z<
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are the default Fairall-adjusted Charnock formulation in WRF, and using parameter estimation techniques to estimate z<
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in DART. Using DART to estimate z<
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is found to reduce 1-h forecast errors of wind speed over the Charnock-Fairall z<
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ensembles by 4%?22%. Finally, however, parameter estimation of z<
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does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.