Statistically optimal estimation of degree-1 and C<sub>20</sub> coefficients based on GRACE data and an ocean bottom pressure model

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In this study, we develop a methodology to estimate monthly variations in degree-1 andC20 coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data withoceanic mass anomalies (combination approach).With respect to the method by Swenson et al.,the proposed approach exploits noise covariance information of both input data sets and thusproduces stochastically optimal solutions supplied with realistic error information. Numericalsimulations show that the quality of degree-1 and -2 coefficients may be increased in this wayby about 30 per cent in terms of RMS error.We also proved that the proposed approach can bereduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise inoceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C20coefficients by estimating mass anomaly time-series within carefully selected validation areas,where mass transport is small. Our validation shows that, compared to selected Satellite LaserRanging (SLR) and joint inversion degree-1 solutions, the proposed combination approachbetter complementsGRACE solutions. The annual amplitude of the SLR-based C10 is probablyoverestimated by about 1 mm. The performance of the C20 coefficients, on the other hand, issimilar to that of traditionally used solution from the SLR technique.