SCATTEROMETER DATA INTERPRETATION - ESTIMATION AND VALIDATION OF THE TRANSFER-FUNCTION CMOD4

In this paper we estimate the 18 coefficients of the CMOD4 sigma(0)-to -wind transfer function using a maximum likelihood estimation (MLE) me thod in order to improve the-prelaunch function. We show that a MLE me thod has to be used with caution when dealing with a nonlinear relatio nship or with measurement errors that depend on the measured values. I n the transfer function estimation it is crucial to use the components of the wind, rather than wind speed and direction, to use sigma(0) in logarithmic units rather than physical ones, and to use well-sampled input data. In Stoffelen and Anderson [1997a] we showed that the tripl ets of measured backscatter are very coherent and, when plotted in a t hree-dimensional measurement space, they lie on a well-defined conical surface. Here we propose a strategy for validation of a transfer func tion, the first step of which is to test the ability of a transfer fun ction to fit this conical surface. We derive an objective measure to c ompute the average fit of the transfer function surface to the distrib ution of measured sigma(0) triplets. The transfer function CMOD4, deri ved in the first part of this paper, is shown to fit the cone surface to within the observed scatter normal to the cone, i.e., within roughl y 0.2 dB, equivalent to a root-mean-square wind vector error of simila r to 0.5 m s(-1). The second step in the validation strategy is the ve rification of retrieved scatterometer winds at each position on the co ne surface. Scatterometer winds computed from CMOD4 compare better to the European Centre for Medium-Range Weather Forecasts model winds tha n real-time conventional surface wind data (ship, buoy, or island repo rts) with the root-mean-square wind vector difference typically 3.0 m s(-1). This surprising result can be explained by the so-called repres entativeness error. We further s show that no Significant spatial wind error correlation is present in scatterometer data and therefore conc lude that the ERS1 scatterometer provides winds useful for weather for ecasting and climate studies.