Nonparametric Estimates of the Sea State Bias for the Jason-1 Radar Altimeter

Abstract

The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved.