Inferring Marine Atmospheric Boundary Layer Properties from Spectral Characteristics of Satellite-Borne SAR Imagery

Abstract

For the commonly observed range of air?sea temperature difference and surface wind speed, the static stability of the atmospheric surface layer can have a significant effect on the mean surface stress and its turbulence-scale horizontal variability. While traditional satellite-borne scatterometers have insufficient horizontal resolution to map this turbulence-scale horizontal variability, satellite-borne synthetic aperture radars (SAR) can. This paper explores the potential for applying existing boundary layer similarity theories to these SAR-derived maps of turbulence-scale horizontal variability in air?sea stress. Two potential approaches for deriving boundary layer turbulence and stability statistics from SAR backscatter imagery are considered. The first approach employs Monin?Obukhov similarity theory, mixed layer similarity theory, and a SAR-based estimate of the atmospheric boundary layer depth to relate the ratio of the mean and standard deviation of the SAR-derived wind speed field to the stability of the atmospheric surface layer and the convective scale velocity of the atmospheric mixed layer. The second approach addresses these same goals by application of mixed layer similarity theory for the inertial subrange of the SAR-derived wind speed spectra. In both approaches, the resulting quantitative estimates of Monin?Obukhov and mixed layer scaling parameters are then used to make a stability correction to the SAR-derived wind speed and to estimate the surface buoyancy flux. The impact of operational and theoretical constraints on the practical utility of these two approaches is considered in depth. Calibration and resolution issues are found to impose wind speed limits on the approaches? applicability. The sensitivity of the two approaches? results to uncertainty in their nondimensional parameters is also discussed.