FN Archimer Export Format
PT J
TI Statistical emulation of high-resolution sar wind fields from low-resolution model predictions
BT 
AF HE, Liyun
   CHAPRON, Bertrand
   TOURNADRE, Jean
   FABLET, Ronan
AS 1:1;2:1;3:1;4:2;
FF 1:;2:PDG-ODE-LOS;3:PDG-ODE-LOS;4:;
C1 IFREMER, Lab Oceanog Spatiale, Brest, France.
   Telecom Bretagne, Departement SC Brest, FRANCE
C2 IFREMER, FRANCE
   TELECOM BRETAGNE, FRANCE
SI BREST
SE PDG-ODE-LOS
IN WOS Ifremer jusqu'en 2018
   copubli-france
TC 1
UR https://archimer.ifremer.fr/doc/00256/36733/88194.pdf
LA English
DT Article
DE ;Statistical downscaling;High resolution;Support Vector Regression (SVR);SAR coastal wind
AB This paper addresses the reconstruction of high-resolution (HR) sea surface wind fields (typically, at a spatial resolution of 1 k m). The availability of such HR fields is critical for numerous issues, e.g. coastal management, offshore structures, oil spill disaster tracking, etc. Satellites, especially from Synthetic Aperture Radar (SAR) systems, can monitor the ocean surface at a spatial resolution of a few meters. SAR wind fields are operationally produced with spatial resolutions of less than 1 k m [1, 2]. However, satellite SAR systems involve a highly irregular sampling of the ocean surface and, for a given region, SAR wind fields may be delivered with a low temporal resolution, typically every 7-to-10 days for temperate zones. By contrast, model predictions, such as European Center for Medium-range Weather Forecast (ECMWF) wind fields, are typically delivered with a high temporal resolution (e.g. every 3 h or 6 h), but with a low spatial resolution (similar to 50 km x 50 km). The question of the combination of numerical model predictions and SAR wind fields naturally arises to deliver HR wind fields at sea surface anywhere and anytime. Here, we state this issue as the statistical learning of transfer functions between low-resolution (LR) model predictions and the associated HR SAR fields. We investigate the extent to which such regression functions can be learnt from a set of co-located HR and LR fields. Both local and non-local schemes as well as linear and non-linear regression methods are considered. As a case-study, we carry out numerical experiments for a coastal area off Norway, which involves complex LR-to-HR situations.
PY 2014
SO IEEE International Symposium on Geoscience and Remote Sensing IGARSS
SN 2153-6996
PU IEEE
UT 000349688105121
BP 3914
EP 3917
DI 10.1109/IGARSS.2014.6947340
ID 36733
ER

EF