Coherency analysis between SGs at BFO and Strasbourg

Abstract

The twin satellite GRACE mission has provided high-precision, spaceborne measurements of the Earth time-varying gravity field. Independent validation of the GRACE derived gravity field models using superconducting gravimeters (SGs) has been discussed controversially in the literature, since SGs provide gravity observations at a stationary point with high-precision and low instrumental drift. To evaluate whether ground based gravity observations can be used to validate GRACE gravity field models we compare 3 years of continuous SG data from the Strasbourg Observatory (ST) and the Black Forest Observatory (BFO). These two stations are only 57.5 km apart which is twelve times smaller than the shortest resolved wavelength (about 700 km) in the weekly GRACE gravity field models. Thus, since GRACE derived models predict essentially the same temporal gravity field variations for both ST and BFO we require high correlation between ST and BFO at periods longer than one week. A lack of correlation at these long periods would point to more local sources of these gravity variations and would make a GRACE validation impossible, if BFO and ST are representatives of typical GGP stations. The coherence between the residuals of the two stations ranges from 0.65 to 0.9 at periods from 15 days to 30 days. We further investigate the local hydrology of the SG stations which may not be embodied in the GRACE predictions. Local hydrological signals are known to be a major signal in gravity residuals at period longer than 1 day. We inspect locally recorded precipitation and global hydrological models data in this context. The difference of residuals between the two stations is enlarged after correcting for the local hydrological effects. Correcting for the local hydrological effects using GLDAS model or ERA-interim does not improve the coherence from 15 days to 30 days significantly. The trends of SG residual variations before correcting for local hydrology agree well with GRACE predictions. We suggest more sophisticated measuring and modelling of local hydrology to better estimate the hydrological effect.