Dynamical coupling of wind and ocean waves through wave-induced air flow

Understanding the physical mechanisms behind the generation of ocean waves by wind has been a longstanding challenge. Previous studies have assumed that ocean waves induce fluctuations in velocity and pressure of the overlying air that are synchronized with the waves, and numerical models have supported this assumption. In a complex feedback, these fluctuations provide the energy for wave generation. The spatial and temporal structure of the wave-induced airflow therefore holds the key to the physics of wind-wave coupling, but detailed observations have proved difficult. Here we present an analysis of wind velocities and ocean surface elevations observed over the open ocean. We use a linear filter to identify the wave-induced air flow from the measurements and find that its structure is in agreement with 'critical-layer' theory. Considering that the wave-induced momentum flux is then controlled by the wave spectrum and that it varies considerably in vertical direction, a simple parameterization of the total air-sea momentum flux is unlikely to exist.

Statistical-uncertainty-based adaptive filtering of lidar signals

An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometeorological humidity data were used to calibrate the ratio of the lidar gains of the H(2)O and the N(2) photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem.

Estimation of the refractive-index temperature structure parameter over the ocean

The semiempirical equation of Wyngaard, Izumi, and Collins is combined with the bulk aerodynamic formulas to provide equations for the estimation of C(T)(2) (the refractive-index temperature structure parameter) in the atmospheric boundary layer above the sea. The equations relate C(T)(2) to easily measured quantities of average wind speed and air-sea temperature and moisture differences. Comparison with limited C(T)(2) data obtained in the marine boundary layer suggests that the estimation equations are satisfactory.