Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events

Article


Gharineiat, Zahra and Deng, Xiaoli. 2015. "Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events." Marine Geodesy. 38 (3), pp. 261-276. https://doi.org/10.1080/01490419.2015.1036183
Article Title

Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events

ERA Journal ID1931
Article CategoryArticle
AuthorsGharineiat, Zahra (Author) and Deng, Xiaoli (Author)
Journal TitleMarine Geodesy
Journal Citation38 (3), pp. 261-276
Number of Pages17
Year2015
Place of PublicationUnited States
ISSN0149-0419
1521-060X
Digital Object Identifier (DOI)https://doi.org/10.1080/01490419.2015.1036183
Web Address (URL)http://www.tandfonline.com/doi/full/10.1080/01490419.2015.1036183
Abstract

This paper determines sea level fields with nonlinear components along the northern coast of Australia using a state-of-the-art approach of the Multi-Adaptive Regression Splines (MARS).The 20 years of data from multi-missions of satellite altimetry (e.g. Topex, Jason-1 and Jason-2)and 14 tide gauges are combined to provide a consistent view of sea levels. The MARS is chosen because it is capable of dividing measured sea levels into distinct time intervals where different linear relationships can be identified, and of weighting individual tide gauge according to the importance of their contributions to predicted sea levels. In the study area, the mean R-squared (R2) of 0.62 and Root Mean Squared (RMS) error of 6.73 cm are obtained from modelling sea levels by MARS. The comparison of the MARS with the multiple-regression shows an improved sea level prediction, as MARS can explain 62% of sea level variance while multiple-regression only accounts for 44% of variance. The predicted sea levels during six tropical cyclones are validated against sea level observations at three independent tide-gauge sites. The comparison results show a strong correlation (~99%) between modelled and observed sea levels, suggesting that the MARS can be used for efficiently monitoring sea level extremes.

Keywordssatellite radar altimetry; tropical cyclone; coastal sea level; multiple regression; multi adaptive regression spline
ANZSRC Field of Research 2020419999. Other environmental sciences not elsewhere classified
370803. Physical oceanography
370899. Oceanography not elsewhere classified
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Byline AffiliationsUniversity of Newcastle
Institution of OriginUniversity of Southern Queensland
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