The increasing demand in the air transportation has been bringing about increased workload to air traffic controllers. Reducing the workload, hence increasing the airspace capacity could be enabled by developing automated air traffic management tools. Our previous work presented a new hybrid system description, namely automated ATCo, modeling the decision process of the air traffic controllers in en-route and approach operations. The developed tool also considers enhanced air traffic and aircraft dynamics. The hybrid system provides realistic conflict resolution m aneuvers in 3D space in reasonable computation times. The trajectory prediction infrastructure behind the developed tool accepts mainly flight plans and aircraft performance variables (i.e. initial conditions, performance model) as inputs to yield trajectories. However, some aircraft specific parameters are not exactly known for ground based systems. These can be described as random variables. This phenomena results in uncertainties in trajectory prediction. In this paper, trajectory predictions during climb phase are improved through model driven state estimation. The algorithm uses observed track of an aircraft obtained from a period of time and recovers the take-off mass error considering the conservation of energy rates. It is shown that trajectories are improved in both in time and spatial terms compared to predictions with nominal states.