15 citations · 19 across the 5 of their papers we have counts for
5 papers
Training neural mapping schemes for satellite altimetry with simulation data
Quentin Febvre, Julien Le Sommer, Clément Ubelmann +1
Satellite altimetry combined with data assimilation and optimal interpolation schemes have deeply renewed our ability to monitor sea surface dynamics. Recently, deep learning (DL)…
Scale-aware neural calibration for wide swath altimetry observations
Quentin Febvre, Clément Ubelmann, Julien Le Sommer +1
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…
A DNN Framework for Learning Lagrangian Drift With Uncertainty
Joseph Jenkins, Adeline Paiement, Yann Ourmières +4
Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome…
Joint calibration and mapping of satellite altimetry data using trainable variational models
Quentin Febvre, Ronan Fablet, Julien Le Sommer +1
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 r…
Wind, wave and current interactions appear key for quantifying cross-shelf transport and carbon export; new knowledge and the potential of SKIM to enable monitoring
Jamie D. Shutler, Thomas Holding, Clement Ubelmann +6
The highly heterogeneous and biologically active continental shelf-seas are important components of the oceanic carbon sink. Carbon rich water from shelf-seas is exported at depth…