Wind wave analysis in depth limited water using OCEANLYZ, A MATLAB toolbox. (September 2017)

• Record Type: Journal Article
• Authors: Karimpour, Arash; Chen, Qin
• Is Part Of: Computers & geosciences. Volume 106(2017)
• Page Start: 181
• Page End: 189
• Publication Date: 2017-09
• Subjects: Measured wind wave analysis -- Frequency and time domains wave data analysis -- OCEANLYZ -- MATLAB toolbox; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.06.010
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: There are a number of well established methods in the literature describing how to assess and analyze measured wind wave data. However, obtaining reliable results from these methods requires adequate knowledge on their behavior, strengths and weaknesses. A proper implementation of these methods requires a series of procedures including a pretreatment of the raw measurements, and adjustment and refinement of the processed data to provide quality assurance of the outcomes, otherwise it can lead to untrustworthy results. This paper discusses potential issues in these procedures, explains what parameters are influential for the outcomes and suggests practical solutions to avoid and minimize the errors in the wave results. The procedure of converting the water pressure data into the water surface elevation data, treating the high frequency data with a low signal-to-noise ratio, partitioning swell energy from wind sea, and estimating the peak wave frequency from the weighted integral of the wave power spectrum are described. Conversion and recovery of the data acquired by a pressure transducer, particularly in depth-limited water like estuaries and lakes, are explained in detail. To provide researchers with tools for a reliable estimation of wind wave parameters, the Ocean Wave Analyzing toolbox, OCEANLYZ, is introduced. The toolbox contains a number of MATLAB functions for estimation of the wave properties in time and frequency domains. The toolbox has been developed and examined during a number of the field study projects in Louisiana's estuaries. Highlights: Insights on parameters influencing a quality of wave data analysis are provided. Practical guides to minimize potential errors in wave data analysis are suggested. Algorithms for partitioning a measured bimodal spectrum are tested and explained. OCEANLYZ toolbox for time and frequency domains wave data analysis is introduced.