Integrating Hydrography Observations and Geodetic Data for Enhanced Dynamic Topography Estimation

Abstract

Dynamic topography (DT) refers to the time-varying component of the sea surface height influenced by factors like ocean currents, temperature, and salinity gradients. Accurate estimation of DT is crucial for comprehending oceanic circulation patterns and their impact on climate. This study introduces two approaches to estimating DT: (1) utilizing satellite altimetry to directly observe sea surface height and (2) considering the steric and non-steric components of sea level anomalies. The steric term is calculated using salinity and temperature data obtained from local buoy data, Argo observations, and the World Ocean Atlas model. The non-steric term is calculated using GRACE Satellite gravimetry data. To estimate the assimilated DT, four methods are utilized, including variance component estimation (VCE), Bayesian theory, Kalman filter, and 3D variational (3DVAR). These methods assimilate the two aforementioned schemes. The validity of the estimated DT is assessed by comparing the calculated sea surface current, derived from the obtained DT, with observations from local current meter stations. The results indicate that the VCE method outperforms other methods in determining the final DT. Furthermore, incorporating the steric and non-steric terms of sea level in determining DT in coastal areas enhances the accuracy of estimating sea surface currents.

Keywords

• assimilation
• dynamic topography
• geodetic data
• satellite altimetry
• sea surface height
• steric and non-steric