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An examination of coherence of directional wave time series from bottom-mounted pressure and current sensors

机译:从底安装压力和电流传感器的定向波时间序列相干的检查

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This paper examines the coherence between kinematic variables from different types of bottom mounted instruments used for coastal directional wave measurements. Long term field data from PPP, PEMCM and PADV for several locations and wave conditions are analyzed for wave direction and coherence. Directional wave measurements analyzed from arrays of sensors assume that the time series of wave kinematics are statistically homogenous. Sensor time series should differ only in amplitude and phase. For real-world field measurements sensor data contains noise components and systematic errors and biases. Coherence between sensor pairs is a common method of quantifying the noise and error. Our results show very high coherence between individual pressure time series in short base-line pressure arrays. Slope components computed from the pressure array are used for directional analysis. We find slope coherence less than the scalar pressures but generally high. Coherence between U and V components of horizontal velocity measured by current meters should be very high because they are cartesian components of the current vector. Our data show this is generally true for common E-M current meters. For acoustic doppler (PADV) velocity meters the coherence was significantly lower. Coherence is potentially useful as a metric for comparing different sensor systems accuracy for directional estimates. It also is frequently used as a quality control test for automated data analysis. Non-acoustic systems generally exhibit high coherence when operating correctly. For acoustic systems the question arises as to how good must coherence be for acceptable data. We present exploratory analyses that examine the relationship between coherence and the quality of directional estimates.
机译:本文研究了来自用于沿海方向波测量的不同类型的底部安装仪器的运动变量之间的相干性。分析来自PPP,PEMCM和PADV的长期现场数据,用于多个位置和波条件的波方向和相干性。从传感器阵列分析的定向波测量假设Wave运动学的​​时间序列是统计上均匀的。传感器时间序列仅在幅度和相位中不同。对于真实世界的现场测量,传感器数据包含噪声分量和系统错误和偏差。传感器对之间的相干性是量化噪声和误差的常用方法。我们的结果表明,短基线压力阵列中的各个压力时间序列之间的相干性非常高。从压力阵列计算的斜率部件用于定向分析。我们发现斜坡连贯性小于标量压力,但通常很高。 U型计量测量的水平速度与V部件之间的相干性应非常高,因为它们是当前向量的笛卡尔部件。我们的数据显示这通常是普通E-M当前仪表。对于声学多普勒(PADV)速度计,相干性显着降低。相干性可能是用于比较不同传感器系统精度以进行定向估计的指标。它还经常用作自动数据分析的质量控制测试。在正确操作时,非声学系统通常表现出高的相干性。对于声学系统,问题出现为可接受数据必须如何良好。我们呈现探索性分析,以研究一致性与定向估计质量之间的关系。

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