| 14 Oct 2025
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 acoustic techniques have been used to estimate wind speed from moored and mobile platforms, their application to profiling floats has been demonstrated only in limited cases and remains largely unexplored. Here, we report on the first deployment of a profiling float equipped with a passive acoustic sensor, aimed at detecting wind-driven surface signals from depth. The float was deployed in the northwestern Mediterranean Sea near the DYFAMED meteorological buoy from February to April 2025, operating 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 from the DYFAMED buoy. To evaluate the potential for broader application, we simulate a remote deployment scenario by refitting the acoustic model of Nystuen et al. (2015) using ERA5 reanalysis as a proxy for surface wind. Refitting the model to ERA5 data demonstrates that the float–acoustic–wind relationship is generalizable in moderate conditions, but high-wind regimes remain systematically biased—especially above 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—such as those from a ship during float deployment—are available. The corrected wind time series achieved a 37 % reduction in RMSE and improved the coefficient of determination (R2) from 0.85 to 0.91, demonstrating the effectiveness of combining reanalysis with sparse in-situ fitting. This framework enables the retrieval of fine-scale wind variability not captured by reanalysis alone, supporting a scalable strategy for float-based wind monitoring in data-sparse ocean regions—with important implications for quantifying air–sea exchanges, improving biogeochemical flux estimates, and advancing global climate observations.
Competing interests: NKE instrumentation is a private company which commercialized the acoustic float, in which AD and CS are employed. The acoustic float is based on the PROVOR CTS5 platform and on an acoustic sensor developed and commercialized by NKE instrumentation with a partnership agreement with LOV. All other co-authors declare no competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
The manuscript represents an outstanding contribution to scientific progress by demonstrating a scalable, autonomous system for wind speed estimation using profiling floats and passive acoustic monitoring (PAM).
The core novelty is the successful deployment and retrieval of wind data from deep parking depths (500–1000 m) and the development of a residual learning framework. This framework is critical because it addresses the core limitation of acoustic wind retrieval in remote regions: the lack of local calibration data. By combining global reanalysis (ERA5) with sparse in-situ observations to correct systematic biases, the authors achieved a major quantitative improvement: a 37% reduction in RMSE and an increase in R^2 from 0.85 to 0.91. This work provides a practical path to integrate wind forcing observations with the BGC-Argo float array.
The scientific approach and applied methods are valid and well-justified. The use of established empirical models (like Nystuen et al., 2015) and the innovative application of the XGBoost algorithm for residual learning are appropriate for handling the non-linear relationship between acoustics and wind. The necessary preprocessing steps, such as depth correction and noise mitigation, are included.
The discussion is appropriate and balanced, explicitly acknowledging the systematic bias of the ERA5-fitted model in high-wind regimes (>10 m/s) and the need for the residual correction. It accurately situates the findings within the framework of prior moored and mobile PAM research.
The authors present the scientific results and conclusions in a clear, concise, and well-structured manner. The manuscript adheres to a standard, logical flow, and the technical language is precise. The figures are of high quality and clearly show the main findings, especially when comparing the unoptimized, ERA5-fitted, and ML-corrected time series (Figure 8) and the scatter plots (Figure 6 Tables clearly present all necessary methodological details, including frequency band integration times and model requirements.
Given the groundbreaking nature of the results and the high overall quality, the manuscript should be accepted subject to minor revisions.
The "minor revisions" category is suggested to meet the need for a clearer explanation of the current validation strategy. Suggested minor revisions focus on enhancing the discussion (Section 3.3.1, paragraph 5) and the conclusions by explicitly stating that while the framework works, the performance metrics may represent the upper bound of expected accuracy due to using the same short-duration deployment for both training and validation. Expand the need for future work to validate the model's generalizability using spatially or temporally distinct training-validation splits to confirm the framework's robustness for global, remote deployment.