###### Abstract  
Mean dynamic topography (MDT) is crucial for research in oceanography and climatology. The optimal interpolation method (OIM) is applied to MDT modeling, where the error variance-covariance information of the observations is established. The global geopotential model (GGM) derived from GOCE (Gravity Field and Steady-State Ocean Circulation Explorer) gravity data and the mean sea surface model derived from satellite altimetry data are combined to construct MDT. Numerical experiments in the Kuroshio over Japan show that the use of recently released GOCE-derived GGM derives a better MDT compared to the previous models. The MDT solution computed based on the sixth-generation model illustrates a lower level of root mean square error (77.0 mm) compared with the ocean reanalysis data, which is 2.4 mm (5.4 mm) smaller than that derived from the fifth-generation (fourth-generation) model. This illustrates that the accumulation of GOCE data and updated data preprocessing methods can be beneficial for MDT recovery. Moreover, the results show that the OIM outperforms the Gaussian filtering approach, where the geostrophic velocity derived from the OIM method has a smaller misfit against the buoy data, by a magnitude of 10 mm/s (17 mm/s) when the zonal (meridional) component is validated. This is mainly due to the error information of input data being used in the optimal interpolation method, which may obtain more reasonable weights of observations than the Gaussian filtering method. m 2021 30 May 2021 7 June 2021 7 June 2021 79  
the difference between them is the MDT. However, limited by the observation technique, the GGM would contribute to a higher level of errors than the MSS in solving the MDT [5]. In recent years, with the development of gravity satellites, especially the successive launch of the Gravity Recovery and Climate Experiment mission (GRACE), the Gravity Field and Steady-State Ocean Circulation Explorer mission (GOCE), and the GRACE Follow-On mission, the accuracy and resolution of GGM have been significantly improved [6; 7; 8]. These dedicated satellite missions facilitate many applications in earth science, including understanding ocean circulations in oceanography and studying tectonic-geodynamic activities [9]. Currently, the highest degree and order (d/o) of the satellite-only GGM can be up to 300 d/o, corresponding to a spatial resolution of about 66 km [10]. However, the spatial resolution of MSS can currently reach 1\({}^{\prime}\) (~10 km), which is much higher than that of the GGM [11]. As a result, because of the spectral inconsistency, it will cause problems such as spectral aliasing and spectral leakage when removing the geoid signal directly from the MSS [5]. Accordingly, a filtering procedure is mandatory to unify the spectrum between the MSS and the geoid. Researchers now have investigated different filtering methods for reasonable MDT solutions, such as Gaussian filtering and wavelet filtering [12; 13; 14]. The weighting scheme of Gaussian filtering is usually only related to distance, which may not be the best method. In addition, [PERSON] et al. [15] developed an adaptive filtering method based on singular spectrum analysis to solve high-resolution MDT. This method is adequate for the finite domain MDT processing since it minimizes smoothing and does not lose boundary points. The signal and noise can effectively separate by this method. But these filtering methods still lead to signal leakage and distortion problems, and it is difficult to give the error distribution of the MDT model. Compared with the previous studies, [PERSON] et al. [5; 16] introduced an optimal interpolation method (OIM) in a combination of the MSS and GGM, which considered the covariance between observations. The estimated MDT can be smoothed while preserving more detailed signals, which mitigates the problems of signal leakage or distortion, and error information of the MDT is estimated.