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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HF雷达的海洋电流测量的错误评估:基于子周期测量方差的错误模型

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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型海洋电流传感雷达系统的数据来评估作为可用雷达数据的一部分提供的错误指示符的性能。调查基于来自带过水基线对雷达系统的数据。使用大约长期的时间序列。雷达数据是Codar系统提供的典型每小时径向测量。这些测量实际上是由雷达系统收集的2和7个子小时测量的任何位置的中值(或平均值)。检测下的误差指示符基于亚小时径向的标准偏差(STD),除以子小时径向数量的平方根。这些值被记录在由最近的Codar数据处理软件的版本生产的每小时数据文件中。该模型的检查证明了模型与所检测的所有基线对的基线差异STD之间的正相关性。通过将其用作数据鉴别器和雷达数据的滑动箱样本的时间序列来呈现误差模型的预测能力。基线差异STD基于误差模型的阈值拒绝的数据被认为显着高于所保留的数据。这里提出的结果表明了改善HF雷达电流测量不确定性的评估的潜力。这种改进具有有利于HF雷达数据的所有应用,包括例如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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