TOWARD THE TRUE NEAR-SURFACE WIND-SPEED - ERROR MODELING AND CALIBRATION USING TRIPLE COLLOCATION

Wind is a very important geophysical variable to accurately measure. H owever, a statistical phenomenon important for the validation or calib ration oi winds is the small dynamic range relative to the typical mea surement uncertainty, i.e., the generally small signal-to-noise ratio. In such cases, pseudobiases may occur when standard validation or cal ibration methods are applied, such as regression or bin-average analys es. Moreover, nonlinear transformation of random error, for instance, between wind components and speed and direction, may give rise to subs tantial pseudobiases. In fact, validation or calibration can only be d one properly when the full error characteristics of the data are known . In practice, the problem is that prior knowledge on the error charac teristics is seldom available. In this paper we show that simultaneous error modeling and calibration can be achieved bq using triple colloc ations. This is a fundamental finding that is generally relevant to al l geophysical validation. To illustrate the statistical analysis using triple collocations, in situ, ERS scatterometer, and forecast model w inds are used. Wind component error analysis is shown to be more conve nient than wind speed and direction error analysis. The anemometer win ds from the National Oceanic and Atmospheric Administration (NOAA) buo ys are shown to have the largest error variance, followed by the scatt erometer and the National Centers for Environmental Prediction (NCEP) forecast model winds proved the most accurate. When using the in situ winds as a reference, the scatterometer wind components are biased low by similar to 4%. The NCEP forecast model winds are found to be biase d high by similar to 6%. After applying a higher-order calibration pro cedure an improved ERS scatterometer wind retrieval is proposed. The s ystematic and random error analysis is relevant for the use of near-su rface winds to compute fluxes of momentum, humidity, or heat or to dri ve ocean wave or circulation models.