Estimating Energy Dissipation Rate from Breaking Waves Using Polarimetric SAR Images

The total energy dissipation rate on the ocean surface, ϵt (W m−2), provides a first-order estimation of the kinetic energy input rate at the ocean–atmosphere interface. Studies on the spatial and temporal distribution of the energy dissipation rate are important for the improvement of climate and wave models. Traditional oceanographic research normally uses remote measurements (airborne and platforms sensors) and in situ data acquisition to estimate ϵt; however, those methods cover small areas over time and are difficult to reproduce especially in the open oceans. Satellite remote sensing has proven the potential to estimate some parameters related to breaking waves on a synoptic scale, including the energy dissipation rate. In this paper, we use polarimetric Synthetic Aperture Radar (SAR) data to estimate ϵt under different wind and sea conditions. The used methodology consisted of decomposing the backscatter SAR return in terms of two contributions: a polarized contribution, associated with the fast response of the local wind (Bragg backscattering), and a non-polarized (NP) contribution, associated with wave breaking (Non-Bragg backscattering). Wind and wave parameters were estimated from the NP contribution and used to calculate ϵt from a parametric model dependent of these parameters. The results were analyzed using wave model outputs (WAVEWATCH III) and previous measurements documented in the literature. For the prevailing wind seas conditions, the ϵt estimated from pol-SAR data showed good agreement with dissipation associated with breaking waves when compared to numerical simulations. Under prevailing swell conditions, the total energy dissipation rate was higher than expected. The methodology adopted proved to be satisfactory to estimate the total energy dissipation rate for light to moderate wind conditions (winds below 10 m s−1), an environmental condition for which the current SAR polarimetric methods do not estimate ϵt properly.

[1]  Klaus Hasselmann,et al.  On the spectral dissipation of ocean waves due to white capping , 1974 .

[2]  Jin Wu,et al.  Wind-Stress coefficients over Sea surface near Neutral Conditions—A Revisit , 1980 .

[3]  W. Pierson,et al.  A proposed spectral form for fully developed wind seas based on the similarity theory of S , 1964 .

[4]  Christopher J. Zappa,et al.  Environmental turbulent mixing controls on air‐water gas exchange in marine and aquatic systems , 2007 .

[5]  Bertrand Chapron,et al.  On Dual Co-Polarized SAR Measurements of the Ocean Surface , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Bertrand Chapron,et al.  The FluxEngine air–sea gas flux toolbox: simplified interface and extensions for in situ analyses and multiple sparingly soluble gases , 2019, Ocean Science.

[7]  Anthony Freeman The effects of noise on polarimetric SAR data , 1993, Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium.

[8]  Bertrand Chapron,et al.  Swell and Wind-Sea Distributions over the Mid-Latitude and Tropical North Atlantic for the Period 2002–2008 , 2012 .

[9]  Alexis Mouche,et al.  Dual-polarization measurements at C-band over the ocean: results from airborne radar observations and comparison with ENVISAT ASAR data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Brian Ward,et al.  Parameterizing air‐sea gas transfer velocity with dissipation , 2017 .

[11]  Bertrand Chapron,et al.  On Quad-Polarized SAR Measurements of the Ocean Surface , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Russel P. Morison,et al.  On the upper ocean turbulent dissipation rate due to microscale breakers and small whitecaps , 2018, Ocean Modelling.

[13]  Adrian H. Callaghan,et al.  On the Relationship between the Energy Dissipation Rate of Surface-Breaking Waves and Oceanic Whitecap Coverage , 2018, Journal of Physical Oceanography.

[14]  Irena Hajnsek,et al.  Inversion of surface parameters from polarimetric SAR , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  Jeffrey L. Hanson,et al.  Wind Sea Growth and Dissipation in the Open Ocean , 1999 .

[16]  J. P. Hansen,et al.  High Range Resolution Radar Measurements of the Speed Distribution of Breaking Events in Wind-Generated Ocean Waves: Surface Impulse and Wave Energy Dissipation Rates , 2001 .

[17]  Hendrik L. Tolman,et al.  Source Terms in a Third-Generation Wind Wave Model , 1996 .

[18]  William J. Plant,et al.  An analysis of the effects of swell and surface roughness spectra on microwave backscatter from the ocean , 2010 .

[19]  M. Banner,et al.  Modeling Wave-Enhanced Turbulence in the Ocean Surface Layer , 1994 .

[20]  Jin Wu Variations of whitecap coverage with wind stress and water temperature , 1988 .

[21]  Yijun He,et al.  Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model , 2011 .

[22]  Edward C. Monahan,et al.  Whitecaps and the passive remote sensing of the ocean surface , 1986 .

[23]  W. Plant A two-scale model of short wind-generated waves and scatterometry , 1986 .

[24]  J. Crease The Dynamics of the Upper Ocean , 1967 .

[25]  Dariusz Stramski,et al.  Bubble entrainment by breaking waves and their influence on optical scattering in the upper ocean , 2001 .

[26]  Benjamin Holt,et al.  Polarimetric Analysis of Backscatter From the Deepwater Horizon Oil Spill Using L-Band Synthetic Aperture Radar , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Albert J. Williams,et al.  Estimates of Kinetic Energy Dissipation under Breaking Waves , 1996 .

[28]  Mark A. Sletten,et al.  Energy dissipation of wind‐generated waves and whitecap coverage , 2008 .

[29]  W. Kendall Melville,et al.  Distribution of breaking waves at the ocean surface , 2002, Nature.

[30]  E. Rogers,et al.  Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation , 2009, 0907.4240.

[31]  O. M. Phillips,et al.  Radar Returns from the Sea Surface—Bragg Scattering and Breaking Waves , 1988 .

[32]  Peter Sutherland,et al.  Field Measurements of Surface and Near-Surface Turbulence in the Presence of Breaking Waves , 2015 .

[33]  하태민 Dynamics and Modelling of Ocean Waves , 2016 .

[34]  A. Mouche,et al.  Radar scattering of the ocean surface and sea-roughness properties : A combined analysis from dual-polarizations airborne radar observations and models in C band , 2006 .

[35]  Richard Manasseh,et al.  Passive Acoustic Determination of Wave-Breaking Events and Their Severity across the Spectrum , 2006 .

[36]  Alexander V. Babanin,et al.  Breaking Probability for Dominant Waves on the Sea Surface , 2000 .

[37]  S. Durden,et al.  A physical radar cross-section model for a wind-driven sea with swell , 1985, IEEE Journal of Oceanic Engineering.

[38]  K. Katsaros,et al.  A Unified Directional Spectrum for Long and Short Wind-Driven Waves , 1997 .

[39]  Jakov V. Toporkov,et al.  Breaking wave contribution to low grazing angle radar backscatter from the ocean surface , 2008 .

[40]  P. Hwang,et al.  Surface roughness and breaking wave properties retrieved from polarimetric microwave radar backscattering , 2015 .

[41]  B. Chapron,et al.  A semiempirical model of the normalized radar cross‐section of the sea surface 1. Background model , 2003 .

[42]  K. THE ROLE OF SURFACE-WAVE BREAKING IN AIR-SEA INTERACTION , 2007 .

[43]  O. Phillips Spectral and statistical properties of the equilibrium range in wind-generated gravity waves , 1985, Journal of Fluid Mechanics.

[44]  W. Kendall Melville,et al.  Correlations between Ambient Noise and the Ocean Surface Wave Field , 1995 .

[45]  Ferris Webster,et al.  Whitecap coverage from satellite measurements: A first step toward modeling the variability of oceanic whitecaps , 2006 .

[46]  Camilla Brekke,et al.  The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Maurizio Migliaccio,et al.  The Two-Scale BPM Scattering Model for Sea Biogenic Slicks Contrast , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[48]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[49]  E. Rogers,et al.  Semi-empirical dissipation source functions for ocean waves: Part I, definition, calibration and validation. Fabrice ArdhuinJean-Francois Filipot and Rudy Magne Service Hydrographique et Oceanographique de la Marine, Brest, France , 2010 .

[50]  Changlong Guan,et al.  The whitecap coverage model from breaking dissipation parametrizations of wind waves , 2007 .

[51]  J. Johannessen,et al.  Quad‐polarization SAR features of ocean currents , 2014 .

[52]  N. Reul,et al.  Importance of the sea surface curvature to interpret the normalized radar cross section , 2007 .

[53]  C. J. Zappa,et al.  Infrared remote sensing of breaking waves , 1997, Nature.

[54]  William Perrie,et al.  Depolarized radar return for breaking wave measurement and hurricane wind retrieval , 2010 .

[55]  Xiaofeng Li,et al.  A Systematic Comparison of the Effect of Polarization Ratio Models on Sea Surface Wind Retrieval From C-Band Synthetic Aperture Radar , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[56]  Maurizio Migliaccio,et al.  X-Band Two-Scale Sea Surface Scattering Model to Predict the Contrast due to an Oil slick , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.