Multi-Mission Cross-Calibration of Satellite Altimeters: Constructing a Long-Term Data Record for Global and Regional Sea Level Change Studies

Abstract

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.

Impact of Atmospheric and Oceanic De-aliasing Level-1B (AOD1B) products on precise orbits of altimetry satellites and altimetry results

“…The algorithm used within this paper is documented in Bosch et al (2014) and provides not only global mean range biases but also radial errors for each altimeter measurement under the assumption that the TOPEX mission provides offset-free observations (range bias of zero). The distribution of the influences on different missions is done automatically by means of variance component estimation.…”

“…By averaging the mean values of corresponding grid cells a gridded estimate of the GCE can be derived. This method is explained in more detail in Bosch et al (2014). Since the GCE are computed based on the radial errors from MMXO, the GCE represent the sum of all influences where orbit effects are only one part.…”

“…geographically correlated errors) or other systematic and random errors. Detailed information on the method can be found in Bosch et al (2014) and the results from MMXO for the calibration of single missions are given, for example, in Dettmering & Bosch (2010) and Dettmering et al (2015).…”

“…First, they are separated to 10-d time periods. Then, the radial errors are used to estimate mean range bias r, as well as centre-of-origin shifts x, y, and z for each mission and each 10-d period following (Bosch et al 2014). The x-and z-components of the shifts do not show any systematics, however, the y-component representing the East-West shift reveals some annual oscillation.…”

Potential of ENVISAT Radar Altimetry for Water Level Monitoring in the Pantanal Wetland

“…Even though no multi-mission approach is followed, inter-mission biases are taken into account by applying radial error corrections to all measurements. These corrections are computed by a global multi-mission crossover analysis as described in Bosch et al [35]. Their application ensures the estimation of absolute water levels, since the known offset (about 0.5 m) of ENVISAT heights is taken into account.…”