Automated Global Classification of Surface Layer Stratification Using High-Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar

Justin E. Stopa , Chen Wang , Doug Vandemark , Ralph Foster , Alexis Mouche (1) , Bertrand Chapron (1)

1 LOPS - Laboratoire d'Océanographie Physique et Spatiale

A three-state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel-1 synthetic aperture radar wave mode images at incidence angle ≈36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri < −0.012, near-neutral: −0.012 < Ri < +0.001, and stable: Ri > +0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016 to 2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite-based approach for detecting air-sea stratification has implications for weather modeling and air-sea flux products.

Mots clés: marine atmospheric boundary layer; air-sea fluxes; boundary layer dynamics; turbulent coherent structures; synthetic aperture radar; deep learning for remote sensing

Domaines: Planète et Univers [physics]

Justin E. Stopa, Chen Wang, Doug Vandemark, Ralph Foster, Alexis Mouche, et al.. Automated Global Classification of Surface Layer Stratification Using High-Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar. Geophysical Research Letters, 2022, 49, ⟨10.1029/2022GL098686⟩. ⟨insu-03779398⟩