Measured signals obtained by sensors during dynamic events such as earthquake, wind, and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new approach is presented for modal parameter identification of structures particularly suitable for very large real-life structures such as super high-rise building structures based on the integration of discretized synchrosqueezed wavelet transform, the Hilbert transform, and the linear least-square fit. Its effectiveness is demonstrated first by application to a two-dimensional frames from the literature, and then to the 123-story Lotte World Tower (LWT) under construction in Seoul, Korea. The LWT measurements are very low-amplitude ambient vibrations. Extracting the natural frequencies and damping ratios from such low-amplitude signals are known to be very challenging. Further, the new methodology was compared with the empirical mode decomposition. It is demonstrated that the new method is capable of extracting both natural frequencies and damping rations from low-amplitude signals effectively and with a higher accuracy compared with the empirical mode decomposition approach. The results of this research indicate a super high-rise building like LWT has a damping ratio in the range 0.7-3.4%. The new method is quite promising for practical implementations of health monitoring of large real-life structures.