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

Publication: Research - peer-review › Journal article – Annual report year: 2012

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**Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction.** / Cheng, Yongcun; Andersen, Ole Baltazar; Knudsen, Per.

Publication: Research - peer-review › Journal article – Annual report year: 2012

### Harvard

*Advances in Space Research*, vol 50, no. 8, pp. 1099-1106., 10.1016/j.asr.2011.11.016

### APA

*Advances in Space Research*,

*50*(8), 1099-1106. 10.1016/j.asr.2011.11.016

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### MLA

*Advances in Space Research*. 2012, 50(8). 1099-1106. Available: 10.1016/j.asr.2011.11.016

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TY - JOUR

T1 - Integrating Non-Tidal Sea Level data from altimetry and tide gauges for coastal sea level prediction

A1 - Cheng,Yongcun

A1 - Andersen,Ole Baltazar

A1 - Knudsen,Per

AU - Cheng,Yongcun

AU - Andersen,Ole Baltazar

AU - Knudsen,Per

PB - Pergamon

PY - 2012

Y1 - 2012

N2 - 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).

AB - 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).

KW - Multivariate Regression Model

KW - The response method

KW - Non-Tidal Sea Level

KW - Tidal correction

U2 - 10.1016/j.asr.2011.11.016

DO - 10.1016/j.asr.2011.11.016

JO - Advances in Space Research

JF - Advances in Space Research

SN - 0273-1177

IS - 8

VL - 50

SP - 1099

EP - 1106

ER -