Abstract's details
A new proposal for SSB modelling with three parameters exclusively derived from altimetric data
CoAuthors
Event: 2015 Ocean Surface Topography Science Team Meeting
Session: Instrument Processing: Corrections
Presentation type: Type Oral
Contribution: PDF file
Abstract:
The sea state bias (SSB) is a geophysical altimetric correction caused by the influence of local sea-state in the altimetric radar pulse and is still one of the largest terms in satellite altimetry error budget. Research on the understanding the SSB and on the improvement of SSB retrieval methods is an up-to-date topic, crucial to the full exploitation of altimeter measurements made both in the conventional Low Resolution Mode (LRM) and the new Synthetic Aperture Radar (SAR) mode.
This study presents the recent developments at the University of Porto in SSB modelling.
Traditionally, SSB operational models have been developed using empirical relationships between altimeter range bias, significant wave height (SWH), wind speed (U10) or radar backscatter cross-section measurements (σ0). Past studies suggested that two-parameter SSB models may be insufficient for this range bias parametrization leading to new developments of SSB models with additional information from wave field statistics captured from numerical ocean wave models. Additionally, in another field of study, some models have been proposed to retrieve wave period information (Tm, Tz) exclusively from the radar altimeter signal, combining both SWH and sigma0 fitted with buoys measurements.
A new global SSB model has been developed at University of Porto, based on 3 parameters, solely derived from altimetric data (SWH, U10 and Tz). This SSB model was designed using penalized regression splines embedded in a general framework provided by Generalized Additive Models (GAMs), allowing to model not only the relationship between SSB and each of the three parameters, but also their interdependencies, taking into account the fact that they are not independent. The adopted methodology has been applied to Jason-1 and TOPEX using wave period information retrieved from altimetric data and wave field statistics generated from WW3.
The chosen criteria for this new SSB model design and obtained results when implemented for Jason-1 and TOPEX altimetric missions, using wave field information derived from both altimetric data and numerical ocean wave models, will be presented.
This study presents the recent developments at the University of Porto in SSB modelling.
Traditionally, SSB operational models have been developed using empirical relationships between altimeter range bias, significant wave height (SWH), wind speed (U10) or radar backscatter cross-section measurements (σ0). Past studies suggested that two-parameter SSB models may be insufficient for this range bias parametrization leading to new developments of SSB models with additional information from wave field statistics captured from numerical ocean wave models. Additionally, in another field of study, some models have been proposed to retrieve wave period information (Tm, Tz) exclusively from the radar altimeter signal, combining both SWH and sigma0 fitted with buoys measurements.
A new global SSB model has been developed at University of Porto, based on 3 parameters, solely derived from altimetric data (SWH, U10 and Tz). This SSB model was designed using penalized regression splines embedded in a general framework provided by Generalized Additive Models (GAMs), allowing to model not only the relationship between SSB and each of the three parameters, but also their interdependencies, taking into account the fact that they are not independent. The adopted methodology has been applied to Jason-1 and TOPEX using wave period information retrieved from altimetric data and wave field statistics generated from WW3.
The chosen criteria for this new SSB model design and obtained results when implemented for Jason-1 and TOPEX altimetric missions, using wave field information derived from both altimetric data and numerical ocean wave models, will be presented.