Characterizing Global Sea Surface Local Wind Variability From ASCAT Data

Recent advances in the sea surface wind quality control of the Advanced Scatterometer (ASCAT) show that spatial wind variability within a resolution cell of 25 \times25 km, namely, the subcell wind variability, is highly correlated with the ASCAT quality indicators, such as the wind inversion residual (maximum likelihood estimator, MLE) and the singularity exponent (SE) derived from singularity analysis. This opens up opportunities for quantifying the instantaneous spatial wind variability over the global sea surface. In this article, it is assumed that the spatial wind variability is linearly proportional to the temporal variation of buoy sea surface winds time series following Taylor's hypothesis. As such, the moored buoy winds with 10-min sampling are used to examine the subcell wind variability. Then the sensitivity of ASCAT quality indicators to the subcell wind variability is evaluated. The results indicate that although SE is more sensitive than MLE in characterizing the wind variability, they are mainly complementary in flagging the most variable winds. Consequently, an empirical model is derived to relate the buoy wind vector variability to the ASCAT MLE and/or SE values. Although the overall procedure is based on the 1-D temporal analysis and such empirical model cannot fully represent the 2-D spatial variability, it leads for the first time to the development of an ASCAT-derived local wind variability product. The empirical method presented here is straightforward and can be applied to other scatterometer systems.