Numerical evaluation of internal tide characteristics extracted from mobile float observations: A case study near the Luzon Strait

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN
A. Cao, Zheng Guo, Shuya Wang, Xinyu Guo, Jinbao Song
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引用次数: 0

Abstract

With the development of ocean observation technology, data from specially-designed mobile profiling floats have been used to study the internal tides (ITs). However, the accuracy of IT characteristics extracted from such observations has not been fully evaluated. Based on numerical simulations of ITs and background circulation with hundreds of free-moving floats near the Luzon Strait, this study examines the IT characteristics extracted from the float observations based on statistics. For the case in which only the M2 constituent is considered, the lowest error level of extracted M2 temperature fluctuation amplitudes (TFAs) is 40−50%, which appears at 200−1500 m depth. Increasing the sampling frequency of the float from daily to hourly does not decrease the lowest error level. The quasi-daily sampling and other tidal constituents also have an impact on the extracted M2 TFAs and increase their errors. The different patterns of background currents mainly influence the errors of extracted M2 TFAs in the upper 200 m. The relation between TFA and vertical displacement of ITs and the two error sources of the TFA extracted from float observations are discussed in this study.
从流动浮标观测中提取的内部潮汐特征的数值评估:以吕宋海峡附近为例
随着海洋观测技术的发展,来自专门设计的移动剖面浮标的数据已被用于研究内部潮汐。然而,从这些观测中提取的信息技术特征的准确性尚未得到充分评估。基于对吕宋海峡附近数百个自由移动浮筒的ITs和背景环流的数值模拟,本研究基于统计数据检验了从浮筒观测中提取的IT特征。对于只考虑M2成分的情况,提取的M2温度波动幅度(TFA)的最低误差水平为40−50%,出现在200−1500 m深度。将浮动的采样频率从每天增加到每小时并不会降低最低误差水平。准每日采样和其他潮汐成分也会对提取的M2 TFA产生影响,并增加其误差。背景电流的不同模式主要影响上层200 m提取的M2 TFA的误差。本文讨论了TFA与ITs垂直位移之间的关系以及从浮子观测中提取的TFA的两个误差源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
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.
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