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 U_ss, 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 U_ss and buoy measurements, for the former bulk-parameter-based NN model, compared to 0.87, for the latter spectral wave-density-based NN model. U_ss values were estimated over the global ocean during 27–29 August 1998. We found U_ss 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 U_ss, we calculated the Langmuir number L_at and the scaled Langmuir vertical turbulent velocity. The distribution of L_at (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 U_ss, such as the westerlies in the Southern Ocean.

Acknowledgements

Thanks for the useful suggestions from Jerry Smith from Scripps Institute of Oceanography. This paper was supported by the Project from National Natural Science Foundation of China (no. 41176159), the Canadian Space Agency GRIP projects ‘Spaceborne Ocean Intelligence Network–SOIN’ and ‘RADARSAT-2 SAR wind retrievals without using external wind direction information’, and PERD (Panel on Energy Research and Development).