Space and Earth observations to quantify present-day sea-level change

authored by: Xiaoxing He, Jean Philippe Montillet, Gaël Kermarrec, C. K. Shum, Rui Fernandes, Jiahui Huang, Shengdao Wang, Xiwen Sun, Yu Zhang, Harald Schuh
Abstract: This chapter presents the contemporary technologies (e.g., tide-gauges, satellite altimetry) and some methodologies to process observations in order to estimate the sea level at a regional or global scale. We discuss the common biases (e.g., vertical land motion, ocean currents, instrumental noise) and how to address them. Highlighting the collaborative efforts of various global agencies, we emphasize a range of routinely updated data products aimed at facilitating sea level monitoring. We underscore a contemporary approach: the integration of machine learning and deep learning algorithms to handle big datasets. These tools promise to be potent instruments for analysing complex patterns, correlations, and nonlinear relationships that traditional models may struggle to capture effectively. Our aspiration is for the ongoing and future evolution of the applications based on these algorithms to furnish invaluable insights into regional variations, extreme events, and long-term trends of sea level change, aiding multi-decadal planning and bolstering resilience strategies crucial for policymakers.
Organisation(s): Institute of Meteorology and Climatology
External Organisation(s): Jiangxi University of Science and Technology
University of Beira Interior
Physikalisch-Meteorologisches Observatorium World Radiation Center (PMOD/WRC)
The Ohio State University
East China Institute of Technology
Helmholtz Centre Potsdam - German Research Centre for Geosciences (GFZ)
Type: Article
Journal: Advances in Geophysics
Volume: 65
Pages: 125-177
No. of pages: 53
ISSN: 0065-2687
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geophysics
Electronic version(s): https://doi.org/10.1016/bs.agph.2024.06.001 (Access: Closed)

BibTeX

@article{6a92a392acc940ec9354d38298b7b7d1,
title = "Space and Earth observations to quantify present-day sea-level change",
abstract = "This chapter presents the contemporary technologies (e.g., tide-gauges, satellite altimetry) and some methodologies to process observations in order to estimate the sea level at a regional or global scale. We discuss the common biases (e.g., vertical land motion, ocean currents, instrumental noise) and how to address them. Highlighting the collaborative efforts of various global agencies, we emphasize a range of routinely updated data products aimed at facilitating sea level monitoring. We underscore a contemporary approach: the integration of machine learning and deep learning algorithms to handle big datasets. These tools promise to be potent instruments for analysing complex patterns, correlations, and nonlinear relationships that traditional models may struggle to capture effectively. Our aspiration is for the ongoing and future evolution of the applications based on these algorithms to furnish invaluable insights into regional variations, extreme events, and long-term trends of sea level change, aiding multi-decadal planning and bolstering resilience strategies crucial for policymakers.",
keywords = "Global Navigation Satellite Systems, Satellite altimetry, Sea level, Tide gauge",
author = "Xiaoxing He and Montillet, {Jean Philippe} and Ga{\"e}l Kermarrec and Shum, {C. K.} and Rui Fernandes and Jiahui Huang and Shengdao Wang and Xiwen Sun and Yu Zhang and Harald Schuh",
note = "Publisher Copyright: {\textcopyright} 2024",
year = "2024",
doi = "10.1016/bs.agph.2024.06.001",
language = "English",
volume = "65",
pages = "125--177",
journal = "Advances in Geophysics",
issn = "0065-2687",
publisher = "Academic Press Inc.",
number = "1",
}