Wind lidars present advantages over meteorological masts, including simultaneous multipoint observations, flexibility in measuring geometry, and reduced installation cost. But wind lidars come with the “`cost” of increased complexity in terms of data quality and analysis. Carrier-to-noise ratio (CNR) has been the metric most commonly used to recover reliable observations from lidar measurements but with severely reduced data recovery. In this work we apply a clustering technique to identify unreliable measurements from pulsed lidars scanning a horizontal plane, taking advantage of all data available from the lidars – not only CNR but also line-of-sight wind speed (V_LOS), spatial position, and V_LOS smoothness. The performance of this data filtering technique is evaluated in terms of data recovery and data quality against both a median-like filter and a pure CNR-threshold filter. The results show that the clustering filter is capable of recovering more reliable data in noisy regions of the scans, increasing the data recovery up to 38 % and reducing by at least two-thirds the acceptance of unreliable measurements relative to the commonly used CNR threshold. Along with this, the need for user intervention in the setup of data filtering is reduced considerably, which is a step towards a more automated and robust filter.

中文翻译：

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使用空间信息和聚类算法过滤脉冲激光雷达数据

激光雷达具有优于气象桅杆的优势，包括同时进行多点观测，测量几何形状的灵活性以及降低的安装成本。但是，激光雷达的数据质量和分析方面的复杂性增加了“成本”。载波噪声比（CNR）是最常用来从激光雷达测量中恢复可靠观测结果的度量，但数据恢复却大大减少。在这项工作中，我们利用聚类技术从脉冲激光雷达扫描水平面的过程中识别不可靠的测量结果，同时利用激光雷达提供的所有数据，不仅包括CNR，还包括视线风速（V _LOS），空间位置，和 V _LOS光滑度。针对类似中值的滤波器和纯CNR阈值滤波器，根据数据恢复和数据质量评估了此数据过滤技术的性能。结果表明，聚类过滤器能够在扫描的嘈杂区域中恢复更可靠的数据，将数据恢复率提高到38％，并且相对于常用的CNR阈值，接受不可靠的测量值至少减少了三分之二。随之而来的是，大大减少了用户干预数据过滤设置的需要，这是朝着更加自动化和强大的过滤器迈出的一步。