Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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The main objective of this paper is to integrate Non-Tidal Sea Level (NSL) from the joint TOPEX, Jason-1 and Jason-2 satellite altimetry with tide gauge data at the west and north coast of the United Kingdom for coastal sea level prediction. The temporal correlation coefficient between altimetric NSLs and tide gauge data reaches a maximum higher than 90% for each gauge. The results show that the multivariate regression approach can efficiently integrate the two types of data in the coastal waters of the area. The Multivariate Regression Model is established by integrating the along-track NSL from the joint TOPEX/Jason-1/Jason-2 altimeters with that from eleven tide gauges. The model results give a maximum hindcast skill of 0.95, which means maximum 95% of NSL variance can be explained by the model. The minimum Root Mean Square Error (RMSe) between altimetric observations and model predictions is 4.99cm in the area. The validation of the model using Envisat satellite altimetric data gives a maximum temporal correlation coefficient of 0.96 and a minimum RMSe of 4.39cm between altimetric observations and model predictions, respectively. The model is furthermore used to predict high frequency NSL variation (i.e., every 15min) during a storm surge event at an independent tide gauge station at the Northeast of the UK (Aberdeen).
Original languageEnglish
JournalAdvances in Space Research
Publication date2012
Volume50
Issue8
Pages1099-1106
ISSN0273-1177
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 1

Keywords

  • Multivariate Regression Model, The response method, Non-Tidal Sea Level, Tidal correction
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