To quantify the impact of these supplemental data, data denial studies were performed for both the dropwindsonde and supplemental rawinsonde observations. Analyses were created excluding both supplemental data types using the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) data assimilation scheme. Then the NCEP Global Forecast System (GFS) model was re-run using the GSI analyses that excluded the supplemental observations. A control version of the GFS was also re-run that included all operationally available data. Differences between the control GFS forecasts and the data-denial forecasts will be used to quantify the impact of the supplemental observations on the GFS track forecasts of Hurricane Irene. Results indicate that the dropwindsondes and supplemental rawindsondes on average contributed to a small overall improvement in the track forecast of Irene in the GFS, however there were large cycle-to-cycle variations in the improvement or degradation due to the supplemental data. Interestingly, the positive contribution from both supplemental data types was enhanced for the 0600 and 1800 UTC GFS cycles, suggesting that these supplemental data were more beneficial for these off time model runs. Bulk statistical results from the experiments, including statistical significance tests, will be presented, along with more detailed analysis of selected individual model cycles.