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Error assessment of HF radar-based ocean current measurements: An error model based on sub-period measurement variance

机译:基于高频雷达的洋流测量的误差评估:基于亚周期测量方差的误差模型

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Data from CODAR-type ocean current sensing radar systems are used here to evaluate the performance of an error indicator provided as part of the available radar data. Investigations are based on data from pairs of radar systems with over-water baselines. Approximately year-long time series are used. The radar data are the typical hourly radial measurements provided by CODAR systems. These measurements are actually the median (or mean) of anywhere between 2 and 7 sub-hourly measurements collected by the radar system. The error indicator under examination is based on the standard deviation (std) of the sub-hourly radials, divided by the square root of the number of sub-hourly radials. These values are recorded in the hourly data files produced by recent versions of the CODAR data processing software. Examination of the model demonstrates a positive correlation between the model and the measured baseline difference std for all baseline pairs examined. The predictive capability of the error model is demonstrated by presenting its use as a data discriminator and by examination of time series of sliding boxcar samples of radar data. Baseline difference std for data rejected by a threshold based on the error model is shown to be significantly higher than for the data retained. The results presented here demonstrate potential to improve assessment of the HF radar current measurement uncertainty. Such improvement has potential to benefit all applications of HF radar data, including for example, Lagrangian particle tracking and surface current assimilation into numerical models.
机译:这里使用来自CODAR型洋流感应雷达系统的数据来评估作为可用雷达数据一部分提供的误差指示器的性能。调查基于成对的雷达系统中具有水上基线的数据。使用大约一年的时间序列。雷达数据是CODAR系统提供的典型的每小时径向测量值。这些测量实际上是雷达系统收集的2到7个亚小时测量值之间的中值(或平均值)。所检查的误差指标是基于次小时半径的标准偏差(std)除以次小时半径的数量的平方根。这些值记录在最新版本的CODAR数据处理软件产生的每小时数据文件中。对模型的检查表明,模型与所检查的所有基线对的实测基线差std之间均呈正相关。通过将误差模型用作数据鉴别器并检查雷达数据的滑动棚车样本的时间序列,证明了误差模型的预测能力。结果表明,基于误差模型的阈值拒绝的数据的基准差std明显高于保留的数据。此处显示的结果表明,有可能改善对HF雷达电流测量不确定度的评估。这种改进有可能使高频雷达数据的所有应用受益,包括例如将拉格朗日粒子跟踪和表面电流同化到数值模型中。

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