Sea level predictions from tide gauges and satellite altimetry around the coast of Northern Australia

Xiaoli Deng (The University of Newcastle, Australia)


Zahra Gharineiat (The University of Newcastle, AUstralia); Fukai Peng (The University of Newcastle, Australia)

Event: 2016 Ocean Surface Topography Science Team Meeting

Session: Science II: From large-scale oceanography to coastal and shelf processes

Presentation type: Type Poster

Extreme sea levels caused by tropical cyclones are considered as the most devastating weather events that affect the coast of Northern Australia. In this paper in order to monitor sea level extremes, models of the multivariate regression (MR) and the Multi-adaptive regression splines (MARS) have been used to predict sea-level variations using 21 years of data from multi-satellite radar altimetry missions including TOPEX/Poseidon, Jason-1 and Jason-2, and 14 tide gauges. It is found that both datasets are statistically correlated with pointwise correlation coefficients >0.6 in most of the study area, which has been confirmed by the student’s t-test results. The storm-surge propagation was investigated, which has been found to follow the same direction as the Kelvin waves. The results reveal that both models achieve averaged quality measures of the hindcast skill (~0.62) and root mean square error (~7 cm), with a better performance from the MARS model. The predicted sea surface heights (SSHs) have been validated against independent in-situ data from three tide gauges during periods of six tropical cyclones. Comparison results indicate that the high SSH peaks predicted by the model taken into account of both altimetry and tide-gauge data agree well with those observed at validating gauges. Changes in extreme sea levels are analysed. The concepts of modelled sea levels and extreme sea levels are illustrated in this paper with the results for the area subjected to intense tropical cyclone events around northern Australian coasts.

Poster show times:

RoomStart DateEnd Date
Grande Halle Thu, Nov 03 2016,11:00 Thu, Nov 03 2016,18:00
Xiaoli Deng
The University of Newcastle