Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar
Abstract 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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2022GL098686 |
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| author | Justin E. Stopa Chen Wang Doug Vandemark Ralph Foster Alexis Mouche Bertrand Chapron |
| author_facet | Justin E. Stopa Chen Wang Doug Vandemark Ralph Foster Alexis Mouche Bertrand Chapron |
| author_sort | Justin E. Stopa |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-465f8e8701274894815c919478c298cf |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-465f8e8701274894815c919478c298cf2025-01-22T14:38:16ZengWileyGeophysical Research Letters0094-82761944-80072022-06-014912n/an/a10.1029/2022GL098686Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture RadarJustin E. Stopa0Chen Wang1Doug Vandemark2Ralph Foster3Alexis Mouche4Bertrand Chapron5Department of Ocean Resources and Engineering School of Ocean and Earth Science and Technology University of Hawai'i at Mānoa Honolulu HI USASchool of Marine Sciences Nanjing University of Information Science and Technology Nanjing ChinaOcean Processes Analysis Laboratory University of New Hampshire Durham NH USAApplied Physics Laboratory University of Washington Seattle WA USAUniversity Brest, CNRS, IRD, Ifremer, Laboratoire d’Océanographie Physique et Spatiale (LOPS) IUEM Brest FranceUniversity Brest, CNRS, IRD, Ifremer, Laboratoire d’Océanographie Physique et Spatiale (LOPS) IUEM Brest FranceAbstract 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.https://doi.org/10.1029/2022GL098686marine atmospheric boundary layerair‐sea fluxesboundary layer dynamicsturbulent coherent structuressynthetic aperture radardeep learning for remote sensing |
| spellingShingle | Justin E. Stopa Chen Wang Doug Vandemark Ralph Foster Alexis Mouche Bertrand Chapron Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar Geophysical Research Letters marine atmospheric boundary layer air‐sea fluxes boundary layer dynamics turbulent coherent structures synthetic aperture radar deep learning for remote sensing |
| title | Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar |
| title_full | Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar |
| title_fullStr | Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar |
| title_full_unstemmed | Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar |
| title_short | Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar |
| title_sort | automated global classification of surface layer stratification using high resolution sea surface roughness measurements by satellite synthetic aperture radar |
| topic | marine atmospheric boundary layer air‐sea fluxes boundary layer dynamics turbulent coherent structures synthetic aperture radar deep learning for remote sensing |
| url | https://doi.org/10.1029/2022GL098686 |
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