Learning Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations - Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques Access content directly
Journal Articles James, Journal of Advancing in Modeling Earth Syst Year : 2024

Learning Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations

Abstract

Satellite-based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space-borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced using linear Optimal Interpolations (OI) such as the widely-used Data Unification and Altimeter Combination System (\textsc{duacs}). On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection.We propose a new multi-variate Observing System Simulation Experiment (OSSE) emulating 20 years of SSH and SST satellite observations.We train an Attention-Based Encoder-Decoder deep learning network (\textsc{abed}) on this data, comparing two settings: one with access to ground truth during training and one without.On our OSSE, we compare \textsc{abed} reconstructions when trained using either supervised or unsupervised loss functions, with or without SST information. We evaluate the SSH interpolations in terms of eddy detection. We also introduce a new way to transfer the learning from simulation to observations: supervised pre-training on our OSSE followed by unsupervised fine-tuning on satellite data.Based on real SSH observations from the Ocean Data Challenge 2021, we find that this learning strategy, combined with the use of SST, decreases the root mean squared error by 24\% compared to OI.
Fichier principal
Vignette du fichier
revision2.pdf (2.62 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04568666 , version 1 (05-05-2024)

Licence

Identifiers

  • HAL Id : hal-04568666 , version 1

Cite

Théo Archambault, Arthur Filoche, Anastase Alexandre Charantonis, Dominique Béréziat, Sylvie Thiria. Learning Sea Surface Height Interpolation from Multi-variate Simulated Satellite Observations. James, Journal of Advancing in Modeling Earth Syst, inPress. ⟨hal-04568666⟩
0 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More