This study presents a novel, cost-effective approach for estimating turbulent kinetic energy dissipation rates (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\epsilon\)</EquationSource> </InlineEquation>) and temperature variance dissipation rates (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({\chi}_{T}\)</EquationSource> </InlineEquation>) using high-resolution data from fast-response temperature sensors. By leveraging a purpose-built MATLAB toolbox (Solo_T), we developed an integrated methodology to process temperature time-series, estimate key turbulence parameters, and evaluate mixing processes. The efficacy of this approach is demonstrated through field deployments in the northern Arabian Gulf, a dynamically forced, shallow marine environment influenced by episodic Shamal winds and internal wave activity. The methodology captures key turbulence features and temporal variability under varying atmospheric and oceanographic conditions. A comprehensive description of the toolbox architecture, spectral processing workflow, and the theoretical formulations underpinning the computations is provided. Comparisons with two benchmark microstructure profilers show strong agreement, with discrepancies in <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\epsilon\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\({\chi}_{T}\)</EquationSource> </InlineEquation> estimates within 18%, and correlation coefficients for <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\epsilon\)</EquationSource> </InlineEquation> ranging between 0.49 and 0.66 across depths, underscoring the accuracy and robustness of the method. This work contributes a reproducible, scalable framework for turbulence studies and expands observational capabilities in coastal, semi-enclosed, and other under sampled aquatic systems, where traditional turbulence profilers are often impractical or cost prohibitive. Graphical Abstract Graphical abstract descriptions: this visual summary serves as a pivotal entry point into the research, offering a concise overview of the study’s core findings and methodologies. Comprising simple, clear visuals—such as diagrams, illustrations, and conceptual overlays—it effectively communicates complex data in an accessible format. Logical flow is essential in guiding the reader through the interconnected components of the study, while consistency in color, layout, and typography ensures clarity and visual coherence. High-quality imagery enhances engagement, allowing readers to grasp essential information rapidly and encouraging them to explore the full manuscript. In this graphical abstract, three integrated visual elements convey the scope and significance of the work. The left panel incorporates code fragments and spectral-analysis concepts from the SOLO_T MATLAB toolbox, representing the computational engine used to extract ε and χₜ from high-resolution thermistor data. The center panel features satellite imagery of the northern Arabian Gulf, grounding the research in its real observational setting and highlighting the dynamic coastal environment where the methodology was validated. The right panel displays deployment schematics of the custom thermistor frame, emphasizing the cost-effective, field-ready instrumentation central to this study. Overlaid streamline patterns symbolically represent turbulence and scalar transport, visually linking instrumentation, field observations, and analytical processing. Together, these elements provide a concise yet comprehensive overview of the methodology and its relevance to advancing turbulence research in aquatic systems.

A Novel Cost-Effective Approach for Collecting Time-Series of Turbulence Properties in Aquatic Systems – Part II

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2026年3月24日

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Fahad Al Senafi,
Ayal Anis,
Sebastiano Piccolroaz,
Tariq Al Rushaid

摘要

This study presents a novel, cost-effective approach for estimating turbulent kinetic energy dissipation rates ( \(\epsilon\) ) and temperature variance dissipation rates ( \({\chi}_{T}\) ) using high-resolution data from fast-response temperature sensors. By leveraging a purpose-built MATLAB toolbox (Solo_T), we developed an integrated methodology to process temperature time-series, estimate key turbulence parameters, and evaluate mixing processes. The efficacy of this approach is demonstrated through field deployments in the northern Arabian Gulf, a dynamically forced, shallow marine environment influenced by episodic Shamal winds and internal wave activity. The methodology captures key turbulence features and temporal variability under varying atmospheric and oceanographic conditions. A comprehensive description of the toolbox architecture, spectral processing workflow, and the theoretical formulations underpinning the computations is provided. Comparisons with two benchmark microstructure profilers show strong agreement, with discrepancies in \(\epsilon\) and \({\chi}_{T}\) estimates within 18%, and correlation coefficients for \(\epsilon\) ranging between 0.49 and 0.66 across depths, underscoring the accuracy and robustness of the method. This work contributes a reproducible, scalable framework for turbulence studies and expands observational capabilities in coastal, semi-enclosed, and other under sampled aquatic systems, where traditional turbulence profilers are often impractical or cost prohibitive.

Graphical Abstract

Graphical abstract descriptions: this visual summary serves as a pivotal entry point into the research, offering a concise overview of the study’s core findings and methodologies. Comprising simple, clear visuals—such as diagrams, illustrations, and conceptual overlays—it effectively communicates complex data in an accessible format. Logical flow is essential in guiding the reader through the interconnected components of the study, while consistency in color, layout, and typography ensures clarity and visual coherence. High-quality imagery enhances engagement, allowing readers to grasp essential information rapidly and encouraging them to explore the full manuscript. In this graphical abstract, three integrated visual elements convey the scope and significance of the work. The left panel incorporates code fragments and spectral-analysis concepts from the SOLO_T MATLAB toolbox, representing the computational engine used to extract ε and χₜ from high-resolution thermistor data. The center panel features satellite imagery of the northern Arabian Gulf, grounding the research in its real observational setting and highlighting the dynamic coastal environment where the methodology was validated. The right panel displays deployment schematics of the custom thermistor frame, emphasizing the cost-effective, field-ready instrumentation central to this study. Overlaid streamline patterns symbolically represent turbulence and scalar transport, visually linking instrumentation, field observations, and analytical processing. Together, these elements provide a concise yet comprehensive overview of the methodology and its relevance to advancing turbulence research in aquatic systems.