Statistical emulation of high-resolution sar wind fields from low-resolution model predictions

Type Article
Date 2014
Language English
Author(s) He Liyun1, Chapron Bertrand1, Tournadre Jean1, Fablet Ronan2
Affiliation(s) 1 : IFREMER, Lab Oceanog Spatiale, Brest, France.
2 : Telecom Bretagne, Departement SC Brest, FRANCE
Meeting IGARSS 2014 - IEEE International Geoscience and Remote Sensing Symposium,13-18 July, Québec, Canada
Source IEEE International Symposium on Geoscience and Remote Sensing IGARSS (2153-6996) (IEEE), 2014 , P. 3914-3917
DOI 10.1109/IGARSS.2014.6947340
WOS© Times Cited 1
Keyword(s) Statistical downscaling, High resolution, Support Vector Regression (SVR), SAR coastal wind
Abstract 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.
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