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    "POSEIDON-2",
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      "citation": "Benveniste JÃ©rÃ´me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean FranÃ§ois, LÃ©ger Fabien, NiÃ±o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002â\u0080\u00932018. Scientific Data, 7 (1)",
      "doi": "10.1038/s41597-020-00694-w"
    },
    {
      "citation": "Sea level along the worldâ\u0080\u0099s coastlines can be measured by a network of virtual altimetry stations. Cazenave A., Gouzenes Y., Birol F., Leger F., Passaro M., M. Calafat F., Shaw A., Nino F., Legeais J.-F., Oelsmann J., Restano M. and Benveniste J.,. Commun Earth Environ 3, 117 (2022).",
      "doi": "10.1038/s43247-022-00448-z"
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  "description": "This dataset contains  a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic,  Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia,  Australia and North and South America.  Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. This dataset has been derived from a new version of the ESA SL_cci+  dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called â\u0080\u0098retrackingâ\u0080\u0099) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.This large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. The main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.The product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.This dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856).  The dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, LegÃ©r Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, NiÃ±o Fernando, Legeais Jean FranÃ§ois, Oelsmann Julius, Benveniste JÃ©rÃ´me (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE.  https://doi.org/10.17882/74354In addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication : - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean FranÃ§ois, Oelsmann Julius, Restano Marco, Benveniste JÃ©rÃ´me (2022). Sea level along the worldâ\u0080\u0099s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z -  Benveniste JÃ©rÃ´me, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean FranÃ§ois, LÃ©ger Fabien, NiÃ±o Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002â\u0080\u00932018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w",
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