Passive acoustic monitoring from profiling floats as a pathway to scalable autonomous observations of global surface wind

Abstract. Wind forcing plays a pivotal role in driving upper-ocean physical and biogeochemical processes, yet direct wind observations remain sparse in many regions of the global ocean. While passive acoustics have been used to estimate wind speed from moored and mobile platforms, their application to profiling floats has been demonstrated only in limited cases. Here we report the first deployment of a biogeochemical profiling float equipped with a passive acoustic sensor explicitly designed for wind retrieval, aimed at detecting wind-driven surface signals from depth. The float was deployed in the northwestern Mediterranean Sea near the DYFAMED (DYnamique des Flux Atmosphériques en MEDiterranée) meteorological buoy from February to April 2025 and operated at parking depths of 500–1000 m. We demonstrate that wind speed can be successfully retrieved from subsurface ambient noise using established acoustic algorithms, with float-derived estimates showing good agreement with collocated surface observations. To evaluate scalability to remote regions, we simulate a remote deployment scenario by refitting the acoustic model of Nystuen et al. (2015) using ERA5 reanalysis as a reference for surface wind. The ERA5-based calibration performs well under moderate winds but exhibits systematic high-wind bias (≥ 10 m s−1). Finally, we apply a residual learning framework to correct these estimates using a limited subset of DYFAMED wind data, simulating conditions where only brief surface observations are available. The corrected wind time series achieved a 38.6 % reduction in RMSE, demonstrating the effectiveness of combining reanalysis with sparse in-situ calibration. This framework improves agreement with in-situ wind observations relative to reanalysis alone, supporting a scalable strategy for float-based wind monitoring in data-sparse ocean regions. Such capability has direct implications for improving estimates of air–sea exchanges, interpreting biogeochemical fluxes, and advancing climate-relevant ocean observing.

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Publication Details

Journal
Ocean science
Published
2026-01-13
DOI
https://doi.org/10.5194/os-22-101-2026
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
0.00

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article

Passive acoustic monitoring from profiling floats as a pathway to scalable autonomous observations of global surface wind

Catherine Schmechtig, Antoine Poteau, Christophe Schaeffer, Louise Delaigue et al.
Ocean science
Oceanographic and Atmospheric Processes
article

Passive acoustic monitoring from profiling floats as a pathway to scalable autonomous observations of global surface wind

Catherine Schmechtig, Antoine Poteau, Christophe Schaeffer, Louise Delaigue, Hervé Claustre, Pierre Cauchy, Sara Pensieri, Arnaud David, Paco Stil, Roberto Bozzano, Dorian Cazau, Anatole Gros-Martial, Edouard Leymarie
article en

Abstract

Abstract. Wind forcing plays a pivotal role in driving upper-ocean physical and biogeochemical processes, yet direct wind observations remain sparse in many regions of the global ocean. While passive acoustics have been used to estimate wind speed from moored and mobile platforms, their application to profiling floats has been demonstrated only in limited cases. Here we report the first deployment of a biogeochemical profiling float equipped with a passive acoustic sensor explicitly designed for wind retrieval, aimed at detecting wind-driven surface signals from depth. The float was deployed in the northwestern Mediterranean Sea near the DYFAMED (DYnamique des Flux Atmosphériques en MEDiterranée) meteorological buoy from February to April 2025 and operated at parking depths of 500–1000 m. We demonstrate that wind speed can be successfully retrieved from subsurface ambient noise using established acoustic algorithms, with float-derived estimates showing good agreement with collocated surface observations. To evaluate scalability to remote regions, we simulate a remote deployment scenario by refitting the acoustic model of Nystuen et al. (2015) using ERA5 reanalysis as a reference for surface wind. The ERA5-based calibration performs well under moderate winds but exhibits systematic high-wind bias (≥ 10 m s−1). Finally, we apply a residual learning framework to correct these estimates using a limited subset of DYFAMED wind data, simulating conditions where only brief surface observations are available. The corrected wind time series achieved a 38.6 % reduction in RMSE, demonstrating the effectiveness of combining reanalysis with sparse in-situ calibration. This framework improves agreement with in-situ wind observations relative to reanalysis alone, supporting a scalable strategy for float-based wind monitoring in data-sparse ocean regions. Such capability has direct implications for improving estimates of air–sea exchanges, interpreting biogeochemical fluxes, and advancing climate-relevant ocean observing.

Ocean scienceVol. 22(1)
Centre National de la Recherche Scientifique (FR), Scripps Institution of Oceanography (US), Université de Bretagne Occidentale (FR), Université du Québec à Rimouski (CA), Sorbonne Université (FR), Centre d'Etudes Biologiques de Chizé (FR), Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT), NKE Instrumentation (France) (FR), Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance (FR), Laboratoire d’Océanographie de Villefranche (FR), Observatoire des sciences de l'Univers Paris-Centre Ecce Terra, Université Paris 1 Panthéon-Sorbonne (FR)
Northwestern University, Institut Français de Recherche pour l'Exploitation de la Mer, European Commission, Agence Nationale de la Recherche, Centre National de la Recherche Scientifique, European Centre for Medium-Range Weather Forecasts
Life below water
Openalex Percentile: Top 90%
Oceanographic and Atmospheric Processes
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