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

verfasst von: 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.
Organisationseinheit(en): Institut für Meteorologie und Klimatologie
Externe Organisation(en): 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-Zentrum Potsdam Deutsches GeoForschungsZentrum (GFZ)
Typ: Artikel
Journal: Advances in Geophysics
Band: 65
Seiten: 125-177
Anzahl der Seiten: 53
ISSN: 0065-2687
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geophysik
Elektronische Version(en): https://doi.org/10.1016/bs.agph.2024.06.001 (Zugang: Geschlossen)

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",
}