Abstract: Implicit equal-weights particle filter (IEWPF) has beaten the "curse of dimensionality" in high-dimensional non-linear geophysical models, which has plagued the particle filters community for decades. The IEWPF draws samples from the proposal density with different covariances for each particle and construct equal-weights for all the particles. The IEWPF is implemented in a high-dimensional two-layer quasi-geostrophic model to check its features and properties. The experiment results show that the new particle filter method has a very good consistency and yields excellent data assimilation results. The horizontal kinetic energy spectrum test indicates that the new method can keep the original balance of the model variables. The comparison between IEWPF and equivalent weights particle filter (EWPF) reveals that IEWPF has a better computation cost than EWPF, while both of them are very good when compared with the analyses. Therefore, IEWPF opens a door for solving the advanced data assimilation problems such as parametrization, etc.