Revisiting Force Model Error Modeling in GRACE Gravity Field Recovery

The gravity field recovery from GRACE (Gravity Recovery and Climate Experiment) mission data is contaminated by both observation noise and dynamic force errors, especially the temporal aliasing errors. To reduce their influence, four approaches are widely adopted, namely the estimation of empirical accelerations (ACC approach), the estimation of K-band range-rate parameters (KBR approach), the incorporation of the full variance–covariance matrix of observations into the least-squares adjustment (COV approach), and the time series model-based filtering (FILT approach). Essentially, the ACC and KBR approaches can be grouped into the method of functional model compensation, while the COV and FILT approaches belong to the method of stochastic model compensation. The four approaches are systematically revisited in this paper concerning their connections and differences from both theoretical perspectives and numerical simulations. Results show that all of them can significantly reduce errors in the recovered monthly gravity field models compared to the nominal approach not applying any of the four approaches. Moreover, their performances are quite consistent in the simulation case where only white observation noise is included. When both colored observation noise and temporal aliasing effects are considered, however, their performances are different. The noise reduction ratio can reach up to 87% by the ACC, COV and FILT approaches, while it is 79% in the KBR approach. The discrepancy can be explained by the compromise between noise reduction and signal absorption in the KBR approach due to the lack of constraints on empirical parameters. Moreover, in the spectral domain, ACC and KBR approaches function as high-pass filters, whereas the stochastic method, COV or FILT approach, can competently cope with colored noise in a full-spectrum manner.