{"title":"Estimating profiles of dissipation rate in the upper ocean using acoustic Doppler measurements made from surface following platforms","authors":"Kristin Zeiden, Jim Thomson, James Girton","doi":"10.1175/jtech-d-23-0027.1","DOIUrl":null,"url":null,"abstract":"Abstract High resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profiles ϵ ( ɀ ) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods (Thomson 2012) to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are pre-filtered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model (Scannell et al. 2017). However, we find the analytic approach less able to resolve the strong vertical gradients in ϵ ( ɀ ) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of non-turbulent shear, and does not add any additional degrees of freedom in the least-squares fit to the model of the structure function. In comparison to the analytic method, ϵ ( ɀ ) estimates obtained via empirical filtering have substantially reduced noise and clearer dependence on near-surface wind speed.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"16 1 1","pages":"0"},"PeriodicalIF":1.9000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1175/jtech-d-23-0027.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract High resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profiles ϵ ( ɀ ) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods (Thomson 2012) to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are pre-filtered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model (Scannell et al. 2017). However, we find the analytic approach less able to resolve the strong vertical gradients in ϵ ( ɀ ) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of non-turbulent shear, and does not add any additional degrees of freedom in the least-squares fit to the model of the structure function. In comparison to the analytic method, ϵ ( ɀ ) estimates obtained via empirical filtering have substantially reduced noise and clearer dependence on near-surface wind speed.
期刊介绍:
The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.