To improve the safety of commercial air transport, pilots are required to train on simulators to recognize the characteristics of an impending stall and subsequently correctly recover from it. To prevent negative training, it is important that the accuracy of the used simulation models is sufficiently high. A key approach for modeling the nonlinear, unsteady aerodynamic effects during the stall is by using Kirchhoff's theory of flow separation. However, widespread difficulties exist in correctly estimating the stall-related parameters of nonlinear flow separation models from flight test data. Therefore, the research in this paper aims to increase the obtained model accuracy by making optimal use of already existing flight data via introduction of a slice-based modeling method. This is done by analyzing the change in the parameter estimate values when applying the system identification procedure to sliced partitions of simulated flight data, for both the pre-stall and post-stall phases. These partitions incrementally increase in size with time from the stall initiation. The simulation data is generated to be representative of the available flight test data, but with known ‘truth’ values for all estimated model parameters. The estimated value for each partition was compared to the true parameter setting in the simulation model used to create the data. It was also investigated whether this coincided with points of increased Fisher information in the data. Manually, an optimal window was found for each parameter for which the estimated value and truth value were equal and sufficient Fisher information was present. For the stall-related parameters the optimal window is often not more than 10 s wider than the stall. For the linear stability and control derivatives it is found that using more data generally results in a better estimate. Finally, the optimal window sizes were used for parameter estimation on the real flight test data. Even though this method represents a prototype, in more than half of the validation cases a decrease in MSE of 10% to 35% was achieved. This shows that the new slice-based modeling method is able to improve the accuracy of nonlinear stall models without the need to gather more flight data and may have applications that reach beyond the realm of stall modeling.