In the past decade, hurricane model forecast skill has improved as a result of better physical parameterizations and data assimilation schemes. Despite these improvements, the models still show some lack of skill when forecasting specific aspects such as precipitation, surface winds and cyclogenesis. Comparisons between model-predicted hurricane track and intensity, and official forecasts have been traditionally used as the main verification of the performance of the models. This method does not provide an accurate means of assessing the model performance because it does not evaluate the dynamical structure of the model. The objective of this study is to examine existing observational data sets, other than track and intensity, to evaluate the model's performance and find better-quality indicators of forecast skill. A model verification study has been developed using the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model and analyses from H*Wind, a global tropical cyclone observational analysis system developed at the Hurricane Research Division of NOAA's AOML. Data from Hurricane Ivan (2004) is used to provide an insight on the model's ability to simulate the surface wind field structure. Preliminary results show that the model surface wind fields exhibit a symmetrical structure for all four quadrants. The intensity of the storm was under-predicted for approximately 70% of the forecast times.