Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model

S U M M A R Y In this study, we develop a methodology to estimate monthly variations in degree-1 and C20 coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data with oceanicmass anomalies (combination approach).With respect to themethod by Swenson et al., the proposed approach exploits noise covariance information of both input data sets and thus produces stochastically optimal solutions supplied with realistic error information. Numerical simulations show that the quality of degree-1 and -2 coefficients may be increased in this way by about 30 per cent in terms of RMS error. We also proved that the proposed approach can be reduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise in oceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C20 coefficients by estimating mass anomaly time-series within carefully selected validation areas, where mass transport is small. Our validation shows that, compared to selected Satellite Laser Ranging (SLR) and joint inversion degree-1 solutions, the proposed combination approach better complements GRACE solutions. The annual amplitude of the SLR-basedC10 is probably overestimated by about 1 mm. The performance of the C20 coefficients, on the other hand, is similar to that of traditionally used solution from the SLR technique.