The majority of data assimilation schemes rely on linearity assumptions. However as the resolution and complexity of both the numerical models and observations increases, these linearity assumptions become less appropriate. Particle filters are a nonlinear data assimilation method that avoid the need for such assumptions and hence can represent the full posterior pdf. Unfortunately standard particle filters suffer from filter degeneracy which makes them inapplicable in high dimensional systems. Like the Implicit particle filter, the equivalent-weights particle filter is an adaptation to the standard particle filter which aims to avoid filter degeneracy and hence theoretically gives a representation of the full posterior pdf even in high dimensional systems. The difference between the Implicit particle filter and the equivalent-weights particle filter lies in the choice of proposal density with which the standard particle filter is adapted. Here the formulation of the equivalent-weights particle filter and its relation to both the SIR filter, the optimal proposal density and the Implicit particle filter is presented. It is demonstrated that filter degeneracy does not occur with the scheme in a 65,000 variable barotropic vorticity model and so consideration is given as to how well the scheme is able to represent the true posterior pdf.