Paper Info
Title
Improved Sea State Bias Estimation for Altimeter Reference Missions With Altimeter-Only Three-Parameter Models
Abstract
This paper presents an in-depth study concerning the development of a sea state bias (SSB) model designed with three parameters exclusively derived from altimeter data and globally applied to all reference altimeter missions. The proposed technique, first tested for the Jason-1 mission, proves to have a good performance for a wide range of ocean conditions when compared with the state-of-the-art SSB corrections currently in use. In addition to the significant wave height ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{s}$ </tex-math></inline-formula> ) and wind speed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$U~_{{10}}$ </tex-math></inline-formula> ), a third predictor acting as a mediator parameter gathered by the mean wave period ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T_{z}$ </tex-math></inline-formula> ) has been used. Two different empirical algorithms for altimeter ocean wave period have been tested and implemented, improving the SSB model performance in some ocean regions. The methodology relies on nonparametric modulation and statistical techniques based on smoothing splines embedded in a generalized additive model. This SSB modeling approach shows good performance when applied to all reference missions, in particular to TOPEX and Jason-2 missions, slightly reducing the explained variance of sea-level anomaly (SLA) when compared with the established SSB models. The approach is computationally efficient, capable of generating a stable SSB model using a small training data set when little information is available, as is the case with the recent Jason-3 mission. Model performance is assessed by comparison with existing SSB corrections for each reference mission, intercomparisons during the period of the tandem phases, and by SLA variance analysis, providing a consistent set of SSB corrections for the four reference missions.
Year
DOI
Venue
2019
10.1109/TGRS.2018.2866773
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Amplitude modulation,Data models,Estimation,Radar,Surface waves,Sea surface,Predictive models
Radar,Wind wave,Data modeling,Altimeter,Significant wave height,Remote sensing,Smoothing spline,Nonparametric statistics,Sea state,Mathematics
Journal
Volume
Issue
ISSN
57
3
0196-2892
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Nelson Pires172.27
M. Joana Fernandes2174.42
Christine P. Gommenginger3449.89
Remko Scharroo482.65