Learning-based Emulation of Sea Surface Wind Fields from Numerical Model Outputs and SAR Data - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Année : 2015

Learning-based Emulation of Sea Surface Wind Fields from Numerical Model Outputs and SAR Data

Résumé

The availability of sea surface wind conditions with a high-resolution space-time sampling is a critical issue for a wide range of applications. Currently, no observation systems nor model forecasts provide relevant information with a high sampling rate both in space and time. Synthetic Aperture Radar (SAR) satellite systems deliver high-resolution sea surface fields, with a spatial resolution below 0.01◦, but they are also char- acterized by a large revisit time up 7-to-10 days for temperate zones. Meanwhile, operational model predictions typically involve a high temporal resolution (e.g. every 6 h), but also a low spatial resolution (0.5◦). With a view to leveraging both data sources, we investigate statistical downscaling schemes. In this study, a new model based on a machine learning method, namely Support Vector Regression (SVR), is built to reconstruct high-resolution sea surface wind fields from low-resolution operational model forecasts. The considered case study off Norway demonstrates the relevance of the proposed SVR model. It outperforms state- of-the-art approaches (namely, linear, analog and Empirical Orthogonal Function (EOF) downscaling models) in terms of mean square error. It also realistically reproduces complex space- time variabilities of the observed SAR wind fields. We further discuss the SVR model as a generalization of the popular linear and analog models.
Fichier principal
Vignette du fichier
liyun-He-JSTARS-final-submission.pdf (6.03 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01581500 , version 1 (04-09-2017)

Identifiants

Citer

Liyun He-Guelton, Ronan Fablet, Bertrand Chapron, Jean Tournadre. Learning-based Emulation of Sea Surface Wind Fields from Numerical Model Outputs and SAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8 (10), pp.4742-4750. ⟨10.1109/JSTARS.2015.2496503⟩. ⟨hal-01581500⟩
242 Consultations
153 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More