Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations
International audience 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 recon...
Published in: | Proceedings of the 10th International Conference on Climate Informatics |
---|---|
Main Authors: | , , , , |
Other Authors: | , , , , , , , , , , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2020
|
Subjects: | |
Online Access: | https://imt-atlantique.hal.science/hal-02929973 https://imt-atlantique.hal.science/hal-02929973/document https://imt-atlantique.hal.science/hal-02929973/file/ci2020_beauchamp.pdf https://doi.org/10.1145/3429309.3429313 |
id |
ftunivbrest:oai:HAL:hal-02929973v1 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Université de Bretagne Occidentale: HAL |
op_collection_id |
ftunivbrest |
language |
English |
topic |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
description |
International audience 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 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. |
author2 |
Département Signal et Communications (IMT Atlantique - SC) IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Lab-STICC_IMTA_CID_TOMS Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) Ocean Next Collecte Localisation Satellites (CLS) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) |
format |
Conference Object |
author |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand |
author_facet |
Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément 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 |
HAL CCSD |
publishDate |
2020 |
url |
https://imt-atlantique.hal.science/hal-02929973 https://imt-atlantique.hal.science/hal-02929973/document https://imt-atlantique.hal.science/hal-02929973/file/ci2020_beauchamp.pdf https://doi.org/10.1145/3429309.3429313 |
op_coverage |
Oxford, United Kingdom |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
CI 2020 : 10th International Conference on Climate Informatics https://imt-atlantique.hal.science/hal-02929973 CI 2020 : 10th International Conference on Climate Informatics, Sep 2020, Oxford, United Kingdom. ⟨10.1145/3429309.3429313⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1145/3429309.3429313 hal-02929973 https://imt-atlantique.hal.science/hal-02929973 https://imt-atlantique.hal.science/hal-02929973/document https://imt-atlantique.hal.science/hal-02929973/file/ci2020_beauchamp.pdf doi:10.1145/3429309.3429313 |
op_rights |
info:eu-repo/semantics/OpenAccess |
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_ |
1790604446996103168 |
spelling |
ftunivbrest:oai:HAL:hal-02929973v1 2024-02-11T10:06:37+01:00 Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations Beauchamp, Maxime Fablet, Ronan Ubelmann, Clément Ballarotta, Maxime Chapron, Bertrand Département Signal et Communications (IMT Atlantique - SC) IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) Lab-STICC_IMTA_CID_TOMS Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC) École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique) Institut Mines-Télécom Paris (IMT) Ocean Next Collecte Localisation Satellites (CLS) Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER) Oxford, United Kingdom 2020-09-23 https://imt-atlantique.hal.science/hal-02929973 https://imt-atlantique.hal.science/hal-02929973/document https://imt-atlantique.hal.science/hal-02929973/file/ci2020_beauchamp.pdf https://doi.org/10.1145/3429309.3429313 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1145/3429309.3429313 hal-02929973 https://imt-atlantique.hal.science/hal-02929973 https://imt-atlantique.hal.science/hal-02929973/document https://imt-atlantique.hal.science/hal-02929973/file/ci2020_beauchamp.pdf doi:10.1145/3429309.3429313 info:eu-repo/semantics/OpenAccess CI 2020 : 10th International Conference on Climate Informatics https://imt-atlantique.hal.science/hal-02929973 CI 2020 : 10th International Conference on Climate Informatics, Sep 2020, Oxford, United Kingdom. ⟨10.1145/3429309.3429313⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] info:eu-repo/semantics/conferenceObject Conference papers 2020 ftunivbrest https://doi.org/10.1145/3429309.3429313 2024-01-16T23:38:11Z International audience 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 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. Conference Object North Atlantic Université de Bretagne Occidentale: HAL Proceedings of the 10th International Conference on Climate Informatics 22 29 |