Improved sea state bias estimation for altimeter reference missions with altimeter-only three-parameter models

Pires, Nelson; Fernandes, M. Joana; Gommenginger, Christine ORCID:; Scharroo, Remko. 2018 Improved sea state bias estimation for altimeter reference missions with altimeter-only three-parameter models. IEEE Transactions on Geoscience and Remote Sensing, 57 (3). 1448-1462.

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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 (Hₛ) and wind speed (U₁₀), a third predictor acting as a mediator parameter gathered by the mean wave period (Tz) 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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 0196-2892
Date made live: 02 Oct 2018 15:23 +0 (UTC)

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