JOURNAL ARTICLE
Influence of lee waves on ocean acoustic simulations near Atlantis II Seamount.
Published In: Journal of the Acoustical Society of America, 2025, v. 157, n. 4. P. 3057 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: McKinley, Matthew; Sun, Daoxun; O'Donnell, Brian; Bracco, Annalisa; Sabra, Karim G. 3 of 3
Abstract
This article investigates the impact of topographically generated lee waves on underwater acoustic propagation near the Atlantis II seamount in the New England Seamount area using a high-resolution regional ocean model called the Coastal and Regional Ocean Community (CROCO) model. The study identifies two types of lee waves—vertical lee waves forming above the seamount and tilted lee waves generated by smaller-scale bathymetry around its base—and demonstrates how these waves modulate sound speed profiles, affecting both the deep sound channel and the sonic layer acoustic duct (SLAD). Acoustic simulations using ray tracing and coupled normal mode analysis reveal that lee waves cause significant spatial and temporal variations in transmission loss, particularly by lifting the sonic layer depth and blocking sound energy within the SLAD at frequencies between 1000 and 2000 Hz. The findings emphasize the necessity of submesoscale-permitting ocean models to capture lee wave dynamics and their influence on acoustic propagation, with implications for improving acoustic prediction accuracy in civilian and defense applications.
Additional Information
- Source:Journal of the Acoustical Society of America. 2025/04, Vol. 157, Issue 4, p3057
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2025
- ISSN:0001-4966
- DOI:10.1121/10.0036460
- Accession Number:184884005
- Copyright Statement:Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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