Ocean State Estimation in CESM via a Localized Particle Filter: Joint Assimilation of Satellite SST and In Situ TS Profiles

The recently developed localized particle filter (LPF) is extended to a fully coupled general circulation model (CGCM), specifically the Community Earth System Model (CESM), to assess its efficacy in assimilating multisource ocean observations, including satellite sea surface temperature (SST) and in situ temperature and salinity (TS) profiles. The LPF introduces localization in the weighting and resampling steps to avoid the filter degeneracy problem, thereby enhancing its performance in assimilating nonlinear systems. Data assimilation experiments using real ocean observations reveal that the LPF has notable advantages in improving the quality of subsurface and deep ocean temperature and salinity, particularly below 200 m. The results are evaluated against objective analysis data, confirming the potential applicability of the LPF in operational settings. Furthermore, a comparative analysis with the ensemble adjustment Kalman filter (EAKF) elucidates the merits and limitations of the LPF, and further underscores the pronounced advantage of LPF in the deep ocean. However, when TS profiles are already assimilated, supplementing the LPF with additional SST data produces adverse effects, a behavior markedly different from that of the EAKF. This discrepancy signals the need for refined data pre-processing strategies within the LPF in real operational applications.