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 2016-2019 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 modelling and air-sea flux products.

Key Points

Common atmospheric boundary layer coherent structures are classified using high-resolution global satellite sea surface radar roughness data

Coherent structures partition into separate unstable, near-neutral and stable stratification regimes

3-regime coherent structure and stratification classification can aid air-sea flux estimation and atmosphere boundary layer parameterization

Plain Language Summary

The air-sea fluxes of momentum, heat, and water vapor are crucial climate data records because they represent lower boundary conditions on the atmospheric circulation and upper boundary conditions of ocean waves and currents. Global measurements of these fluxes using conventional fixed-station systems are impractical making satellite observations attractive. It is very difficult to measure two crucial parameters: the temperature and humidity of the air near the sea surface. The study’s motivation is that external information about the air-sea stratification should improve the retrieval of these parameters. We demonstrate a global capability to classify air-sea stratification based on variations in the sea surface texture of high-resolution radar images. This is possible because the turbulent atmospheric boundary layer develops distinct circulations in different stratification regimes. Perturbation surface winds induced by these coherent structures induce a modulation of the ocean surface roughness that is resolved by orbiting radars. An automatic machine learning algorithm detects the characteristic structures, which we correlated to a standard global data assimilation surface analysis. An important research outcome is the largest all-weather data set of boundary layer coherent structures and the conditions under which they exist. Examination of these data will advance our understanding of fundamental boundary layer processes.