Improving the continuity of the Jason SSB time-series

Ngan Tran (CLS, France)


Gérald Dibarboure (CNES, France); Nicolas Picot (CNES, France)

Event: 2018 Ocean Surface Topography Science Team Meeting

Session: Instrument Processing: Propagation, Wind Speed and Sea State Bias

Presentation type: Type Poster

Most of the operational versions of the Sea State Bias (SSB) correction are computed empirically with the nonparametric estimation technique based on kernel smoothing described in Gaspar et al [2002]. These solutions are derived from 10-day SSH differences (i.e. collinear analysis of repeat cycles of data or from crossover differences). Since only SSB differences are observed, the SSB solution can only be determined to within a constant when solving the equation system. This leads to potentially observe some solution shift related to the imposed constraint to have a SSB value equal to 0 for a flat surface between two versions of the SSB correction. This (constant) shift can reach a few centimeters when the SSB correction version is updated to consider SSH standard changes due to large uncertainty in data-poor region close to (SWH=0, WS=0) to correctly constrain the estimation of SSB(0, 0).
This causes annoying disturbances every time that SSB solutions are updated for the monitoring of multi-mission altimeter biases at in-situ Cal/Val sites or for the intermission bias alignment needed to tie up the different global mean sea level time-series together.
This presentation will seek to propose changes in SSB model development to tackle/reduce the SSB constant shift issue that exists between different correction versions for a same altimetric mission or for different missions all operating at a same radar frequency. The work will focus on the Jason altimeters time-series, both Ku- and C-band data, to better connect the past and current missions.


Poster show times:

Room Start Date End Date
Foyer, Salao Nobre & tent Thu, Sep 27 2018,18:00 Thu, Sep 27 2018,20:00
Foyer, Salao Nobre & tent Fri, Sep 28 2018,14:00 Fri, Sep 28 2018,15:00
Ngan Tran