Design and Comparison of Filters of Gravity Field Inversion from GOCE Satellite Gravity Gradient Data

  Objectives  The filtering of colored noise and low-frequency systematic errors of gravity gradient data from gravity field and steady-state ocean circulation explorer (GOCE) satellite is a key issue in the inversion of high-precision Earth gravity fields.

  Methods  For the filtering of GOCE satellite gravity gradient data, we design two kinds of low-frequency systematic error filters based on moving average (MA) method and circle per revolution (CPR) empirical parameter method, and then combine them with the colored noise filter based on auto-regressive moving average (ARMA) model to form cascaded filters. To analyze the actual effect of these filters, the gravity field models GOGR-MA (MA+ARMA cascade filtering) and GOGR-CPR (CPR+ARMA cascade filtering) gravity field models of order 224 are inversed by using 70 d of GOCE data based on the space-wise least square method, respectively. The inversion models are compared with the first generation of GOCE series models, and GOCE and GRACE combined model GOCO01S solved by contemporaneous data. Then the model is checked by high-precision global positioning system leveling data and mean dynamic topography model.

  Results  The results show that both the MA filter and CPR empirical parameter filter can weaken the influence of low-frequency system errors, and the latter is better. Moreover, ARMA decorrelation filter can effectively whiten the colored noise of gravity gradient data. The combined models GOGR-MA and GOGR-CPR have similar accuracy and they perform better than GOCO01S model developed by European Space Agency (ESA) using the same period data.

  Conclusions  The inversion results verify the correctness and effectiveness of the proposed cascaded filters, which can provide a reference for filtering of GOCE gravity gradient data and inversion of high-precision gravity field.