FN Archimer Export Format
PT J
TI Automated global classification of surface layer stratification using high‐resolution sea surface roughness measurements by satellite synthetic aperture radar
BT 
AF Stopa, Justin E.
   Wang, Chen
   Vandemark, Doug
   Foster, Ralph
   Mouche, Alexis
   Chapron, Bertrand
AS 1:1;2:2;3:3;4:4;5:5;6:5;
FF 1:;2:;3:;4:;5:PDG-ODE-LOPS-SIAM;6:PDG-ODE-LOPS-SIAM;
C1 Department of Ocean Resources and Engineering School of Ocean and Earth Science and Technology, University of Hawai´i at Mānoa Honolulu Hawaii, USA
   School of Marine Sciences Nanjing University of Information Science and Technology Nanjing, China
   Ocean Processes Analysis Laboratory University of New Hampshire Durham NH, USA
   Applied Physics Laboratory University of Washington Seattle WA, USA
   Univ. Brest CNRS IRD, Ifremer Laboratoire d’Océanographie Physique et Spatiale (LOPS) IUEM 29280 Brest ,France
C2 UNIV HAWAII MANOA, USA
   UNIV NANJING, CHINA
   UNIV NEW HAMPSHIRE, USA
   UNIV WASHINGTON, USA
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   copubli-int-hors-europe
   copubli-sud
IF 5.2
TC 2
UR https://archimer.ifremer.fr/doc/00776/88813/94454.pdf
   https://archimer.ifremer.fr/doc/00776/88813/94455.pdf
LA English
DT Article
DE ;marine atmospheric boundary layer;air-sea fluxes;boundary layer dynamics;turbulent coherent structures;synthetic aperture radar;deep learning for remote sensing
AB 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. 
PY 2022
PD JUL
SO Geophysical Research Letters
SN 0094-8276
PU American Geophysical Union (AGU)
VL 49
IS 12
UT 000813574400001
DI 10.1029/2022GL098686
ID 88813
ER

EF