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Adv."],"published-print":{"date-parts":[[2026,5,29]]},"abstract":"<jats:p>\n                    The ocean supplies energy for tropical cyclones (TCs) and slows their winds through surface friction, which exerts a force on the ocean termed wind stress. The drag coefficient (\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>d<\/jats:sub>\n                    ) is the key parameter that converts wind speed to wind stress and is currently estimated in forecast models from incomplete data collected in low-to-moderate ocean winds. Here, we use measurements from 11 Atlantic hurricanes to quantify\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>d<\/jats:sub>\n                    in winds up to 44 meters per second and surface waves up to 14 meters. It is found that\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>d<\/jats:sub>\n                    levels off, as wind speed surpasses 30 meters per second but does not decrease appreciably as suggested by previous indirect methods. Interaction of the wind and wave fields causes\n                    <jats:italic toggle=\"yes\">C<\/jats:italic>\n                    <jats:sub>d<\/jats:sub>\n                    to be 20\u00a0\u00b1\u00a02% higher on the motion-left side of a storm, where wind and waves are misaligned, than on the right. These results quantify directly a fundamental TC air-sea interaction parameter and demonstrate the importance of distinct TC quadrant-specific wind-wave interactions.\n                  <\/jats:p>","DOI":"10.1126\/sciadv.aec7422","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:02:22Z","timestamp":1779904942000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":4,"title":["Hurricane air-sea drag saturation and sea-state dependence revealed by surface drones"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0050-042X","authenticated-orcid":true,"given":"Gregory R.","family":"Foltz","sequence":"first","affiliation":[{"name":"NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of 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Zhang, A. Chiodi, C. Zhang, G. R. Foltz, M. Cronin, C. W. Mordy, J. Cross, E. Cokelet, J. Zhang, C. Meinig, N. Lawrence-Slavas, P. Stabeno, R. Jenkins, Observing extreme ocean and weather events using innovative saildrone uncrewed surface vehicles. 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K. Biswas S. Abarca L. Bernardet I. Ginis E. Grell M. Iacono E. Kalina B. Liu Q. Liu T. Marchok A. Mehra K. Newman J. Sippel V. Tallapragada B. Thomas W. Wang H. Winterbottom Z. Zhang \u201cHurricane Weather Research and Forecasting (HWRF) model: 2018 scientific documentation\u201d (DTC 2018); https:\/\/dtcenter.org\/sites\/default\/files\/community-code\/hwrf\/docs\/scientific_documents\/HWRFv4.0a_ScientificDoc.pdf."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.3389\/feart.2024.1379069"},{"key":"e_1_3_2_36_2","first-page":"2339","article-title":"Potential low bias in high-wind drag coefficient inferred from dropsonde data in hurricanes","volume":"78","author":"Richter D. H.","year":"2021","unstructured":"D. H. Richter, C. Wainwright, D. P. Stern, G. H. Bryan, D. Chavas, Potential low bias in high-wind drag coefficient inferred from dropsonde data in hurricanes. J. Atmos. Sci. 78, 2339\u20132352 (2021).","journal-title":"J. Atmos. 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Zhang \u201cHurricane Weather Research and Forecasting (HWRF) Model: 2011 scientific documentation \u201d (NOAA\/NCAR\/Development Testbed Center 2011)."},{"key":"e_1_3_2_53_2","first-page":"1264969","volume":"11","author":"Hazelton A.","year":"2023","unstructured":"A. Hazelton, G. J. Alaka Jr., L. Gramer, W. Ramstrom, S. Ditchek, X. Chen, B. Liu, Z. Zhang, L. Zhu, W. Wang, B. Thomas, J. Shin, C.-K. Wang, H.-S. Kim, X. Zhang, A. Mehra, F. Marks, 2022 real-time hurricane forecasts from an experimental version of the Hurricane Analysis and Forecast System (HAFSV0.3S), Front. Earth Sci. 11, 1264969 (2023).","journal-title":"2022 real-time hurricane forecasts from an experimental version of the Hurricane Analysis and Forecast System (HAFSV0.3S)"},{"key":"e_1_3_2_54_2","unstructured":"G. R. Foltz Data and scripts to accompany: Hurricane air-sea drag saturation and sea-state dependence revealed by surface drones Zenodo (2026); 10.5281\/zenodo.19632118."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1175\/JAS-D-11-0312.1"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1029\/2012GL053988"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1080\/21664250.2023.2244751"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10236-020-01360-w"},{"key":"e_1_3_2_59_2","first-page":"1","article-title":"Drag coefficients derived from ocean current and temperature profiles at high wind speeds","volume":"70","author":"Zou Z.","year":"2022","unstructured":"Z. Zou, D. Zhao, J. Tian, B. Liu, J. Huang, Drag coefficients derived from ocean current and temperature profiles at high wind speeds. 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