Climate studies require long data records extending the lifetime of a single remote sensing satellite mission. Precise satellite altimetry exploring global and regional evolution of the sea level has now completed a two decade data record. A consistent long-term data record has to be constructed from a sequence of different, partly overlapping altimeter systems which have to be carefully cross-calibrated. This cross-calibration is realized globally by adjusting an extremely large set of single- and dual-satellite crossover differences performed between all contemporaneous altimeter systems. The total set of crossover differences creates a highly redundant network and enables a robust estimate of radial errors with a dense and rather complete sampling for all altimeter systems analyzed. An iterative variance component estimation is applied to obtain an objective relative weighting between altimeter systems with different performance. The final time series of radial errors is taken to estimate (for each of the altimeter systems) an empirical auto-covariance function. Moreover, the radial errors capture relative range biases and indicate systematic variations in the geo-centering of altimeter satellite orbits. The procedure has the potential to estimate for all altimeter systems the geographically correlated mean errors which is not at all visible in single-satellite crossover differences but maps directly to estimates of the mean sea surface.

Multi-mission altimeter observations are successfully used for global cross calibration of altimeters. The approach utilizes a least squares adjustment minimizing single and dual satellite crossover differences as well as consecutive differences of the radial component of single satellites. The method is applied to obtain a characterization of the radial errors of the first year of OSTM/Jason-2 data. A mean relative range bias of about 7.5 ± 0.2 cm with respect to Jason-1 was computed. The radial errors show increased auto-correlation at the orbit revolution period, which is related to geographically correlated error pattern with up to about 2 cm amplitude.