Ocean surface Stokes drift from scatterometer observations

Abstract

Stokes drift, a mean Lagrangian motion induced by ocean surface gravity waves, plays an important role in modifying the upper ocean circulation. In this study, two empirical models are developed to estimate ocean surface Stokes drift Uss, based on a multilayer-perceptron Back-Propagation Neural Network (BP NN) algorithm, using buoy data collocated with European Remote Sensing (ERS) data, and using (1) bulk wave parameters and (2) spectral wave density. Both BP NN models perform reasonably well, with correlation coefficients of 0.92 between the retrieved Uss and buoy measurements, for the former bulk-parameter-based NN model, compared to 0.87, for the latter spectral wave-density-based NN model. Uss values were estimated over the global ocean during 27–29 August 1998. We found Uss in the range from 2 to 36 cm s−1 with an average of 9 cm s−1, which is in good agreement with previous observations. With the retrieved Uss, we calculated the Langmuir number Lat and the scaled Langmuir vertical turbulent velocity. The distribution of Lat (generally in the range 0.25–0.58) provides a measure of the dominance of Langmuir turbulence over kinetic energy turbulence in regions with high Uss, such as the westerlies in the Southern Ocean.