Shen et al. (2025) Ocean State Estimation in CESM via a Localized Particle Filter: Joint Assimilation of Satellite SST and In Situ TS Profiles

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Identification

• Journal: Atmosphere
• Year: 2025
• Date: 2025-09-13
• Authors: Zheqi Shen, Yulong Yao, Yanan Zhang
• DOI: 10.3390/atmos16091081

Research Groups

Researchers involved in Earth System Modeling and data assimilation, particularly those utilizing the Community Earth System Model (CESM).

Short Summary

This study extends the Localized Particle Filter (LPF) to the Community Earth System Model (CESM) for assimilating multisource ocean observations, demonstrating its significant improvement in subsurface and deep ocean states, but revealing challenges with sea surface temperature (SST) assimilation when temperature and salinity (TS) profiles are already used.

Objective

• To extend the Localized Particle Filter (LPF) to a fully coupled general circulation model (CESM) and assess its efficacy in assimilating multisource ocean observations (satellite sea surface temperature and in situ temperature and salinity profiles), comparing its performance with the Ensemble Adjustment Kalman Filter (EAKF).

Study Configuration

• Spatial Scale: Global ocean, as simulated by a fully coupled general circulation model.
• Temporal Scale: Not explicitly defined, but covers a period sufficient for data assimilation experiments using real ocean observations within a general circulation model.

Methodology and Data

• Models used: Localized Particle Filter (LPF), Community Earth System Model (CESM), Ensemble Adjustment Kalman Filter (EAKF).
• Data sources: Satellite sea surface temperature (SST) observations, in situ temperature and salinity (TS) profiles.

Main Results

• The LPF significantly improves the quality of subsurface and deep ocean temperature and salinity, particularly below 200 meters.
• Evaluation against objective analysis data confirms the LPF's potential for operational applicability.
• Comparative analysis with EAKF shows LPF's pronounced advantage in the deep ocean.
• However, when temperature and salinity profiles are already assimilated, supplementing LPF with additional SST data produces adverse effects, indicating a need for refined data pre-processing strategies.

Contributions

• First application and extension of the Localized Particle Filter (LPF) to a fully coupled general circulation model (CESM).
• Demonstration of LPF's superior performance in improving deep ocean temperature and salinity states compared to existing methods like EAKF.
• Identification of a critical limitation of LPF regarding the assimilation of redundant or conflicting observation types (SST when TS profiles are present), highlighting the need for advanced data pre-processing in operational settings.

Funding

• Not specified in the provided text.

Citation

@article{Shen2025Ocean,
  author = {Shen, Zheqi and Yao, Yulong and Zhang, Yanan},
  title = {Ocean State Estimation in CESM via a Localized Particle Filter: Joint Assimilation of Satellite SST and In Situ TS Profiles},
  journal = {Atmosphere},
  year = {2025},
  doi = {10.3390/atmos16091081},
  url = {https://doi.org/10.3390/atmos16091081}
}