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Improved wind retrieval scheme for the generation of SAR level 2 wind products

Vincent Kerbaol(1) and Harald Johnsen(2)

(1) BOOST Technologies, 135, rue Claude Chappe, 29280 Plouzané, France
(2) Norut-IT, Postboks 6434,Forskningsparken, 9294 Tromsø, Norway

Abstract

High resolution properties of SAR imagery has truly contributed to detect, identify and analyse large scale and mesoscale atmospheric phenomena (atmospheric fronts, lows, hurricane, etc) as well as very local meteorological events (rain, breeze, relief effect, etc). In parallel, the ability to derive high resolution wind fields from SAR images using scatterometry approach has also opened new perspectives in applications where the knowledge of the fine scale spatial distribution of wind field is required (offshore wind farming, coastal sail racing competitions, etc). Research studies are also underway on stratifications and measurements corrections as well as turbulence characterization to further exploit SAR capabilities. Recently, research studies to define, develop and validate SAR Ocean Wind, Waves and Currents level 2 products prototypes have been initiated and supported by ESA. Preliminary characteristics of the improved wind retrieval scheme are presented here. As a major improvement, a Maximum A Posteriori (MAP) wind vector estimator is proposed. This estimator, a sub-optimal solution of which was early introduced by Portabella et al., is optimal in a statistical (Bayesian) sense as it fully exploits the a priori knowledge on local wind field (such as obtained from numerical weather prediction –NWP- model or inferred from the SAR image itself). This estimator also includes the classical wind inversion scheme as a particular case. Furthermore, an FFT-based estimation method of the wind direction is proposed to complement the statistical a priori information provided by the NWP model. The wind direction is estimated from SAR signature of wind rows. Confidence level of direction estimates is characterized by a Peak Side Lobe Ratio (PSLR). Issues related to the polarization are also discussed. In particular, the choice of an appropriate wind dependent polarization ratio is proposed and the application to ENVISAT ASAR AP mode of the Bayesian wind estimator is investigated.

 

 

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