Day 4

Detailed paper information

Back to list

  1. Samantha Furtney University of Miami - Rosenstiel School of Marine and Atmospheric Science Speaker
  2. Roland Romeiser University of Miami RSMAS
  3. Hans Graber University of Miami - Rosenstiel School of Marine and Atmospheric Science
Form of presentation Poster
  • A8. Ocean
    • A8.14 Remote-sensing of Ocean Waves and their Applications
Abstract text The University of Miami’s Center for Southeastern Tropical Advanced Remote Sensing acquired over 100 Synthetic Aperture Radar (SAR) images of the California Monterey Bay region for the ongoing Coastal Land-Air-Sea-Interaction Project. Approximately 30 of these images include signatures of nonlinear internal waves (NIW). Eight Air Sea Interaction Spar (ASIS) buoys deployed in the region of interest provide field measurements within the SAR image swath. Surface roughness is most commonly thought of as a result of the wind blowing over the ocean surface. SAR senses the short-scale ocean surface roughness by means of Bragg scattering. Although internal waves are subsurface waves, they are visible in SAR data because they modulate the surface currents resulting in increased roughness associated with the leading edge of the internal wave and decreased roughness associated with the trailing edge of the internal wave. Changes in surface roughness alter the drag coefficient which is a key parameter for detecting wind stress. It has been speculated that NIWs can drive wind velocity and stress variance relative to the mean atmospheric flow, suggesting a surface roughness—wind feedback mechanism exists.

Using the SAR images to confirm the presence of NIWs, we estimate the likely time of arrival at an ASIS buoy site if not already intersecting with a buoy at the time of the image acquisition. The ultrasonic anemometer mounted on ASIS provides the three components of velocity needed to derive the turbulent fluctuations of wind velocity, along with the product term u’w’. The Morlet wavelet transform is used to decompose the signal into both frequency and time domain to study the evolution of features. The length scales corresponding to a particular frequency band of enhanced energy patterns found in wavelet plots are compared to SAR measured NIW wavelengths. We take the covariance between u’ and w’ and integrate over frequency to see if this proposed NIW-induced change in wind stress occurs over the same particular frequencies. To assess the contribution of NIWs to the total air-sea flux, we take the cumulative cospectral sum of u’ and w’ (components of momentum flux). A neighboring ASIS buoy not in the path of an NIW is used to represent the background atmospheric flow. We will present early results and discuss the implications NIWs have on the momentum flux and if they should be considered when studying fine-scale ocean-atmosphere interactions.