FN Archimer Export Format
PT J
TI Learning-Based Emulation of Sea Surface Wind Fields From Numerical Model Outputs and SAR Data
BT 
AF HE GUELTON, Liyun
   FABLET, Ronan
   CHAPRON, Bertrand
   TOURNADRE, Jean
AS 1:1;2:2;3:1;4:1;
FF 1:PDG-ODE-LOS;2:;3:PDG-ODE-LOPS-SIAM;4:PDG-ODE-LOPS-SIAM;
C1 IFREMER, Lab Oceanog Space, F-29280 Plouzane, France.
   Telecom Bretagne, SC, F-29280 Brest, France.
C2 IFREMER, FRANCE
   TELECOM BRETAGNE, FRANCE
SI BREST
SE PDG-ODE-LOS
   PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   copubli-france
IF 2.145
TC 7
UR https://archimer.ifremer.fr/doc/00314/42513/88207.pdf
LA English
DT Article
DE ;Coastal wind;downscaling;high resolution (HR);machine learning;support vector regression (SVR)
AB The availability of sea surface wind conditions with a high-resolution (HR) 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 in both space and time. Synthetic aperture radar (SAR) satellite systems deliver HR sea surface fields, with a spatial resolution below 0.01., but they are also characterized 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 degrees). With a view to leve-raging 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 HR 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.
PY 2015
PD OCT
SO Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing
SN 1939-1404
PU Ieee-inst Electrical Electronics Engineers Inc
VL 8
IS 10
UT 000368904000014
BP 4742
EP 4750
DI 10.1109/JSTARS.2015.2496503
ID 42513
ER

EF