Effect of the satellite laser ranging network distribution on geocenter motion estimation

peer reviewedSLR network translations estimated between a quasi-instantaneous station position set, theoretically expressed with respect to the center of mass of the Earth (CM), and a secular reference frame are the signature of the motion of the CM with respect to the Earth crust. Geocenter motion is defined here to be the motion of the CM with respect to the geometric center of the solid Earth surface (CF). SLR translational variations cannot be rigorously interpreted as identical to geocenter motion due to the sparse and nonuniform distribution of the SLR network. Their difference is called the network effect, which should be dominated at subdecadal timescales by loading signals.We have computed translation time series of the SLR network using two independent geophysically based loading models. One is a displacement model estimated from surface fluid data (Green’s function approach), called forward model, and the other is a displacement model estimated from GPS and ocean bottom pressure (OBP) data, called inverse model. The translation models have been subtracted from their respective geocenter motion models computed from degree-1 mass load coefficients in order to evaluate their network effect biases. Scatter due to the SLR network effect is at the level of 1.5 mm RMS. It could slightly shift the phase of the annual SLR geocenter motion estimate by less than 1 month and could affect X and Z annual geocenter motion amplitudes at the 1-mm level, which is about one third of the expected signal. Two distinct methods are suggested to account for network effect when comparing SLR translations to geocenter motion models. The first is to add the network effect term predicted by a displacement model to the geocenter motion loading model. The second relies on an adequate combination of SLR and GPS products to estimate SLR translation that could be better compared with geocenter motion