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<p>Satellite altimetry data can offer substantial aid in initializing forecasts. However, the assimilation of such data has been unsatisfactory, primarily due to the limitation in the specification of an appropriate mean dynamical topography (MDT). Here we propose a new approach to construct a hybrid MDT for assimilating sea level anomaly data by combining the model MDT and the observational MDT using a simple ensemble-based optimal interpolation approach. The sea level anomaly data are assimilated into a coupled general circulation model using the ensemble adjustment Kalman filter. The hybrid MDT is compared against commonly used MDTs, including the observational MDT and the MDT obtained from an existing assimilation run. Results indicate that the choice of MDT has a great impact on the performance of altimetric data assimilation, particularly concerning ocean temperature and salinity. Notably, the proposed hybrid MDT not only generates a more accurate and consistent MDT, but also achieve the best performance among all assimilation experiments conducted in this study. Furthermore, the proposed method allows for the MDT to be updated during assimilation, which can further enhance the performance of altimetric data assimilation. This study contributes valuable insights into the assimilation of satellite altimetry data.</p>

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Enhancing satellite sea level anomaly data assimilation in a coupled general circulation model with a hybrid mean dynamical topography

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Yihao Chen,
  • Youmin Tang,
  • Zheqi Shen,
  • Yi Li

摘要

Satellite altimetry data can offer substantial aid in initializing forecasts. However, the assimilation of such data has been unsatisfactory, primarily due to the limitation in the specification of an appropriate mean dynamical topography (MDT). Here we propose a new approach to construct a hybrid MDT for assimilating sea level anomaly data by combining the model MDT and the observational MDT using a simple ensemble-based optimal interpolation approach. The sea level anomaly data are assimilated into a coupled general circulation model using the ensemble adjustment Kalman filter. The hybrid MDT is compared against commonly used MDTs, including the observational MDT and the MDT obtained from an existing assimilation run. Results indicate that the choice of MDT has a great impact on the performance of altimetric data assimilation, particularly concerning ocean temperature and salinity. Notably, the proposed hybrid MDT not only generates a more accurate and consistent MDT, but also achieve the best performance among all assimilation experiments conducted in this study. Furthermore, the proposed method allows for the MDT to be updated during assimilation, which can further enhance the performance of altimetric data assimilation. This study contributes valuable insights into the assimilation of satellite altimetry data.