Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations
Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolatio...
Published in: | Proceedings of the 10th International Conference on Climate Informatics |
---|---|
Main Authors: | , , , , |
Format: | Text |
Language: | English |
Published: |
CI2020: Proceedings of the 10th International Conference on Climate InformaticsSeptember 2020. ISBN: 978-1-4503-8848-1. pp. 22–29
2020
|
Subjects: | |
Online Access: | https://doi.org/10.1145/3429309.3429313 https://archimer.ifremer.fr/doc/00739/85150/90134.pdf https://archimer.ifremer.fr/doc/00739/85150/90135.pdf https://archimer.ifremer.fr/doc/00739/85150/ |
id |
fttriple:oai:gotriple.eu:10670/1.zeuxbz |
---|---|
record_format |
openpolar |
spelling |
fttriple:oai:gotriple.eu:10670/1.zeuxbz 2023-05-15T17:34:22+02:00 Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations Beauchamp, Maxime Fablet, Ronan Ubelmann, Clement Ballarotta, Maxime Chapron, Bertrand 2020-01-01 https://doi.org/10.1145/3429309.3429313 https://archimer.ifremer.fr/doc/00739/85150/90134.pdf https://archimer.ifremer.fr/doc/00739/85150/90135.pdf https://archimer.ifremer.fr/doc/00739/85150/ en eng CI2020: Proceedings of the 10th International Conference on Climate InformaticsSeptember 2020. ISBN: 978-1-4503-8848-1. pp. 22–29 doi:10.1145/3429309.3429313 10670/1.zeuxbz https://archimer.ifremer.fr/doc/00739/85150/90134.pdf https://archimer.ifremer.fr/doc/00739/85150/90135.pdf https://archimer.ifremer.fr/doc/00739/85150/ other Archimer, archive institutionnelle de l'Ifremer geo info Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2020 fttriple https://doi.org/10.1145/3429309.3429313 2023-01-22T18:47:40Z Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on a small region, part of the GULFSTREAM and mainly driven by energetic mesoscale dynamics. Based on an Observation System Simulation Experiment (OSSE), we will use the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new NN-based end-to-end learning framework for the representation of spatio-temporal irregulary-sampled data. We evaluate how some of these methods are a significant improvements, particularly by catching up the small scales ranging up to 30-40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and (agreggated) along-track nadir observations. Text North Atlantic Unknown Proceedings of the 10th International Conference on Climate Informatics 22 29 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
geo info |
spellingShingle |
geo info Beauchamp, Maxime Fablet, Ronan Ubelmann, Clement Ballarotta, Maxime Chapron, Bertrand Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
topic_facet |
geo info |
description |
Over the last years, a very active field of research aims at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on a small region, part of the GULFSTREAM and mainly driven by energetic mesoscale dynamics. Based on an Observation System Simulation Experiment (OSSE), we will use the NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new NN-based end-to-end learning framework for the representation of spatio-temporal irregulary-sampled data. We evaluate how some of these methods are a significant improvements, particularly by catching up the small scales ranging up to 30-40km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and (agreggated) along-track nadir observations. |
format |
Text |
author |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clement Ballarotta, Maxime Chapron, Bertrand |
author_facet |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clement Ballarotta, Maxime Chapron, Bertrand |
author_sort |
Beauchamp, Maxime |
title |
Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
title_short |
Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
title_full |
Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
title_fullStr |
Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
title_full_unstemmed |
Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
title_sort |
data-driven and learning-based interpolations of along-track nadir and wide-swath swot altimetry observations |
publisher |
CI2020: Proceedings of the 10th International Conference on Climate InformaticsSeptember 2020. ISBN: 978-1-4503-8848-1. pp. 22–29 |
publishDate |
2020 |
url |
https://doi.org/10.1145/3429309.3429313 https://archimer.ifremer.fr/doc/00739/85150/90134.pdf https://archimer.ifremer.fr/doc/00739/85150/90135.pdf https://archimer.ifremer.fr/doc/00739/85150/ |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Archimer, archive institutionnelle de l'Ifremer |
op_relation |
doi:10.1145/3429309.3429313 10670/1.zeuxbz https://archimer.ifremer.fr/doc/00739/85150/90134.pdf https://archimer.ifremer.fr/doc/00739/85150/90135.pdf https://archimer.ifremer.fr/doc/00739/85150/ |
op_rights |
other |
op_doi |
https://doi.org/10.1145/3429309.3429313 |
container_title |
Proceedings of the 10th International Conference on Climate Informatics |
container_start_page |
22 |
op_container_end_page |
29 |
_version_ |
1766133181831970816 |