Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Febvre, Quentin"'
Autor:
Johnson, J. Emmanuel, Febvre, Quentin, Gorbunova, Anastasia, Metref, Sammy, Ballarotta, Maxime, Sommer, Julien Le, Fablet, Ronan
The ocean profoundly influences human activities and plays a critical role in climate regulation. Our understanding has improved over the last decades with the advent of satellite remote sensing data, allowing us to capture essential quantities over
Externí odkaz:
http://arxiv.org/abs/2309.15599
Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL) schemes have emerged as appealing solutions to address space-time inte
Externí odkaz:
http://arxiv.org/abs/2309.14350
Sea surface height (SSH) is a key geophysical parameter for monitoring and studying meso-scale surface ocean dynamics. For several decades, the mapping of SSH products at regional and global scales has relied on nadir satellite altimeters, which prov
Externí odkaz:
http://arxiv.org/abs/2302.04497
The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregular
Externí odkaz:
http://arxiv.org/abs/2211.07209
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and Water Topography) altimeter mission. Operational systems however gene
Externí odkaz:
http://arxiv.org/abs/2211.05904
Due to the irregular space-time sampling of sea surface observations, the reconstruction of sea surface dynamics is a challenging inverse problem. While satellite altimetry provides a direct observation of the sea surface height (SSH), which relates
Externí odkaz:
http://arxiv.org/abs/2207.01372
Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically resolve scales smaller than 100km. With their new sensors, upcoming wid
Externí odkaz:
http://arxiv.org/abs/2110.03405
Training Convolutional Neural Networks (CNN) is a resource intensive task that requires specialized hardware for efficient computation. One of the most limiting bottleneck of CNN training is the memory cost associated with storing the activation valu
Externí odkaz:
http://arxiv.org/abs/1910.11127
Akademický článek
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Autor:
Beauchamp, Maxime1 (AUTHOR) maxime.beauchamp@imt-atlantique.fr, Febvre, Quentin1 (AUTHOR), Georgenthum, Hugo1 (AUTHOR), Fablet, Ronan1 (AUTHOR)
Publikováno v:
Geoscientific Model Development. 2023, Vol. 16 Issue 8, p2119-2147. 29p.