2. ERS AND NSCAT SCATTEROMETER WIND DATA

The following items are presented in this section :

• 2.1  Introduction
• 2.2  Scatterometer data
  • 2.2.1  ERS Scatterometer off-line products
  • 2.2.2  NSCAT off-line products
  • 2.2.3 Accuracy of scatterometer winds
• 2.3  Wind stress

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2.1  Introduction

Scatterometer instruments on board satellites can routinely provide an estimation of the surface wind vector with high spatial and temporal resolution over all ocean basins. Although the exact mechanisms responsible for the measured backscatter power under realistic oceanic conditions are not fully understood, theoretical analysis, controlled laboratory and field experiment, and measurements from spaceborne radars all confirm that backscatter over the oceans power at moderate incidence angles is substantially dependent on near-surface wind characteristics (speed and direction with respect to the radar viewing geometry). At the present time, the microwave scatterometer is the only satellite sensor that observes wind in terms of wind speed and wind direction.

To date, the most successful inversions of scatterometer measurements rely on empirically derived algorithms. An empirical relationship is typically given by the following harmonic formula :

         (1)

Where k is the degree of ⁰ representation that uses cosines as orthogonal basis (number of harmonics), , the scatterometer wavelength, P, the polarization, , the radar incidence angle, U the wind speed for neutral stability and is the angle between wind direction and radar azimuth. A_j(,P,,U) are the model coefficients to be determined through regression analysis.

Surface wind speed and direction at a given height are retrieved through the minimization, in U and c space, of the Maximum Likelihood Estimator (MLE) function defined by

      (2)

Where ⁰ and _m° are the measured and estimated, from (1), backscatter coefficients, respectively. Var(_m° ) stands for ⁰ variance estimation (Pierson, 1989). N is the number of measured ⁰ used in the wind vector estimation. This approach yields up to four solutions and an ambiguity removal procedure is needed in order to estimate the most probable wind vector (Quilfen et al, 1991), (NASA, 1997).

A main task for a scatterometer investigator is the calibration of the sensor data. The calibration involves both the determination of the empirical model (1) and the development of the surface wind retrieval algorithm. A second task consists in validating the accuracy of backscatter coefficients and wind estimates and their comparison with other sources of data (e.g Schroeder et al, 1982; Bentamy et al, 1994 ; Quilfen et al, 1995 ; Graber et al, 1996).

For the first time and over a period of 10 months (September 1996 - June 1997), two scatterometers were available and provided surface wind estimates with different instrumental configurations. The first one is on board the European Remote Sensing satellite 2 (ERS-2) and the second is the NASA scatterometer (NSCAT) on board the Advanced Earth Observing System (ADEOS). The use of both wind estimates should potentially lead to a more refined wind field analysis calculated from satellite data.

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2.2  Scatterometer data

2.2.1  ERS Scatterometer off-line products

The European Remote Sensing Satellites, ERS-1 & 2, make a substantial contribution to the scientific study of the oceans. The estimations of surface parameters were performed using three microwave instruments : Altimeter, Scatterometer and Synthetic Aperture Radar (SAR) wave mode (Figure 1).

The ERS scatterometer (Figure 1) is an active microwave instrument operating at 5.4GHz (C band) that produces wind vectors (wind speed and direction) at 50 km resolution with a separation of 25 km across a 500 km swath. Incidence angles for the three antennae range from 17° to 46° for the mid beam and 25° to 57° for both the fore- and aft-beams. The scatterometer surface winds are processed and distributed by the Institut Français de Recherche pour l’Exploitation de la MER (IFREMER) using off-line algorithms (Bentamy et al, 1994 ; Quilfen 1995). These ERS-2 winds are called WNF (WiNd Field). The calibration and the validation of the algorithm were performed with dedicated buoy data during the RENE91 experiment, with the National Oceanic Atmospheric Administration (NOAA) National Data Center (NDBC) buoys and the Tropical Ocean Global Atmosphere (TOGA) Tropical Atmosphere Ocean (TAO) buoys. The accuracy of the wind speed and direction derived from the IFREMER algorithm is about 1m/s and 14° (Quilfen, 1995). The validation of the off-line wind products indicated that, at low wind speeds, data are less accurate in wind speed determination and the wind direction (Graber et al, 1996).

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