An Improved Singularity Analysis for ASCAT Wind Quality Control: Application to Low Winds

Abstract

Singularity analysis has proven to be a complementary tool to the Advanced Scatterometer (ASCAT) inversion residual (or maximum likelihood estimator) in terms of wind quality control (QC). In this paper, a new implementation scheme of singularity exponent (SE) is developed for ASCAT data analysis. It combines the wavelet projections of the gradient measurements of multiple parameters into the analysis, ensuring that the analyzed parameters contribute equally to the final singularity map. Therefore, the underlying geophysical phenomena in the different ASCAT-derived parameters can be effectively revealed simultaneously on a unique map of SEs. The validation using both buoy winds and European Centre for Medium-Range Weather Forecasting forecast wind output shows that the newly derived SE significantly improves the current ASCAT wind QC. In particular, poor-quality ASCAT measurements at low-wind and high-variability conditions (w <; 4 m/s) can be effectively screened using the new SE.