Towards a long-term sea level ground-truth dataset in Antarctica. Validation of Copernicus Sentinel-3B altimetry data

Time series of sea level data in the polar regions are a challenging task due to the presence of sea ice and severe weather conditions during most of the year. Long-term sea level measurements are crucial for sea level trend analysis under a climate change scenario, and for establishing the referenc...

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Bibliographic Details
Main Authors: Luengo-S, O., Gómez-Enri, J., Bruno Mejías, M., Berrocoso Domínguez, M.
Format: Conference Object
Language:English
Published: 2023
Subjects:
Online Access:https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018452
Description
Summary:Time series of sea level data in the polar regions are a challenging task due to the presence of sea ice and severe weather conditions during most of the year. Long-term sea level measurements are crucial for sea level trend analysis under a climate change scenario, and for establishing the reference level for many applications. These records are obtained using in-situ tide gauges and, since the 1990s, satellite altimetry. The main aim of this work is the validation of Copernicus Sentinel-3B SAR altimetry data using a ground-truth station (pressure sensor) located in Livingston Island (South Shetland archipelago, about 100 km away from the Antarctic Peninsula). Time series of sea level span 4 years: from November 2018 to December 2022. Altimetry data are from the repository available in the European Space Agency Earth Console Parallel Processing Service (P-PRO). They are obtained in the crossover of two tracks (ascending orbit #0374 and descending #0109) near the tide gauge station (about 15 km). The in-situ station is deployed off the coast of Johnson Cove, on a small cove at the foot of a glacier in Livingston Island (62°39'38.70"S; 60°22'11.62"W). The time series of sea surface heights (not corrected by tidal effects) are referred to the WGS84 ellipsoid, allowing an absolute comparison of the records. Preliminary qualitative analysis shows a very good level of agreement between both datasets.