Estimating Sea Surface Local Wind Variability from ASCAT-derived Information

Abstract

Local variability of sea surface wind has a significant impact on the mesoscale air-sea interactions and the wind-induced oceanic response, such as temperature variability and circulation patterns. Recent advances in the wind quality control of Advanced Scatterometer (ASCAT) show that wind variability within a wind vector cell can be characterized using certain quality indicators derived from ASCAT data, such as the inversion residual (namely the maximum likelihood estimator, MLE) and the singularity exponent (SE) derived from singularity analysis. This study is aimed at quantifying the ASCAT subcell wind variability over the global ocean surface. It is assumed that the spatial variability is proportional to the variance associated with time-series of collocated moored buoys winds. As such, 10-min sampled buoy winds are used to examine the subcell wind variability following Taylor’s hypothesis, which allows for a temporal dimension to be converted into a spatial dimension, and vice versa. The time window (centered on the buoy measurement collocated with ASCAT acquisition) used for calculating the mean buoy winds and the subcell spatial variability is set equal to 25 km. Then the sensitivity of ASCAT quality indicators to the subcell wind variability is evaluated. The results indicate that SE is more sensitive than MLE in characterizing the wind variability, but they are rather complementary in flagging the most variable winds. Consequently, an empirical model is derived to relate the subcell wind variability to the ASCAT MLE and/or SE values. Although the overall procedure is based on the one-dimensional temporal analysis and the empirical model cannot fully represent the two-dimensional spatial variability as depicted by the scatterometer, it is probably the first attempt to assign a subcell wind variability value for each wind vector cell within the ASCAT swath. The empirical method presented here is effective, straightforward, and could be applied to other scatterometer systems. The next step is therefore generate global wind variability maps which can be used in a wide variety of scientific and operational applications