A method to de-alias the scatterometer wind field: a real world application

Abstract

By the 1990’s several scatterometers are going to fly on board satellites dedicated to earth and ocean observation. The Europeen ERS.l AMI (Advanced Microwave Imager) is planned to be launched in 1991 and the US N.SCAT (Navy SCATterometer) is supposed to be launched in 1992. These scatterometers will provide wind vectors on a grid mesh of 50*50 km with a time and space coverage which will be dramatically improved with respect to conventionnal means of observations (see figure 1). For the first time oceanographers can expect to obtain an adequate description of the forcing of ocean circulation which depends on the wind-stress vector for the surface layers and on the wind-stress curl for the large scale and deep motions. These scatterometers will measure the wind with three antennas oriented in different directions. As the radar backscatter σ_O varies harmonically with the horizontal angle χ between the wind and the antenna, with maxima backscatter in the upwind and downwind directions and minima at the crosswind angle, it is possible to compute the wind direction [1]. In the absence of noise the determination of the direction of the wind would be unique. As the backscatter signal is noisy ambiguities can arise in its determination. The functional relationship between σ₀ and the azimuth angle χ is nearly σ_O = cos (2χ), which results in two major ambiguities which are approximately 180° apart, and two others which are at 90°. Several techniques have been proposed to remove these ambiguities[2]. In the present paper we propose an alternative approach based on concepts developed in the frame of neural networks. The method we use can be related to image processing and two dimensional filtering. Its conceptuar framework is to determine two optimal filters to dealias the wind direction.