The unstructured-grid finite-volume community ocean model (FVCOM) offers the potential to build a high-resolution operational prediction system because it can effectively adapt to complex boundaries and flexibly perform local mesh encryption. A matching assimilation system is an indispensable part of an operational forecast system, while the existing assimilation method of the FVCOM [e.g., the ensemble Kalman filter (EnKF)] has difficulty accommodating strong nonlinear model/observation operators and non-Gaussian errors. The localized weighted EnKF (LWEnKF) is a particle filter (PF) that combines the advantages of the EnKF and the PF, which can effectively address nonlinearity and non-Gaussianity and can provide a more accurate initial field for models. An observation preprocessing system and a new data assimilation system based on the LWEnKF for the FVCOM are developed. For the preprocessing system, a K-dimensional tree-based observation thinning method is proposed that can accommodate representative observation efficiently for unstructured grids. Through a 1-month global ocean data assimilation experiment, the effect of our assimilation system is compared with the EnKF and the Mercator analysis forecast product, which comes from a reduced-order Kalman filter system. The statistical verification includes five metrics for various sea surface fields, temperature, and salinity profiles. The results show that the system performs better than the EnKF, although there is still a gap with Mercator in some aspects.