Low dimensional models have proven essential for feedback control and estimation of flow fields such as separated flow over airfoils, cavities and wakes. While feedback control based on global flow estimation can be very efficient, it is often difficult to estimate the flow state if structures of very different length scales are present in the flow. The conventional snapshot based Proper Orthogonal Decomposition (POD), a popular method for low order modeling, does not reliably separate the structures according to size, since it optimizes modes based on energy only. A modified methodology, using a "Filtered POD" approach is developed in this effort and the effect of spatial filtering to precondition snapshot sets for the derivation of POD modes are investigated. Three dimensional flow data from the simulation of turbulent flow over a circular cylinder wake at Re=20,000 are used to evaluate the contribution of filtered POD modeling for flow state estimation. The effect of the amount of spatial filtering is investigated using different spatial filters. A spatial filtering method based on Fast Fourier Transform (FFT) is developed and applied to the data to identify the large scale structures better from the POD analysis of the three dimensional data. Results show that the FFT based filtered POD approach is able to capture the large scale features of the flow, such as the von Kàrmàn vortex street, while not being contaminated by small scale turbulent structures, as evident from the mode amplitudes, energy content and modes presented. The developed approach is promising and makes it an attractive alternative means for modeling of 3D flows.