In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.

中文翻译：

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量化降水临近预报的位置误差

在降水临近预报中，通常会在一系列天气雷达图像中跟踪降水的运动并将这种运动推断到未来。这种预测的总误差由降水特征的预测位置的误差和降水强度随提前时间变化的误差组成。到目前为止，验证措施尚不能隔离位置错误的程度，这使得在位置预测方面难以特别改进临近预报模型。在本文中，我们介绍了一个直接量化位置误差的框架。为此，我们检测并跟踪雷达图像中尺度不变的降水特征（角）。然后，我们将这些观察到的轨迹视为真正的参考，以便评估性能（或者反过来，旨在预测降水特征未来位置的任何模型的误差。因此，任何提前期Δ的预测的位置误差预测时间t之前的t对应于t  + Δt处观察到的特征位置与预测特征位置之间的欧几里得距离。在此框架的基础上，我们使用德国气象局一年的气象雷达复合材料进行了基准案例研究。我们评估了四个外推模型的性能，其中两个基于角运动从t  − 1到t（LK-Lin1）和t  − 4到t的线性外推（LK-Lin4）和其他两个基于密集逆搜索（DIS）方法：从DIS获得的运动矢量用于通过线性（DIS-Lin1）和半拉格朗日外推（DIS-Rot1）预测特征位置。在这四个模型中，DIS-Lin1和LK-Lin4在预测特征位置方面被证明是最熟练的，而我们还发现模型的技能极大地取决于所观察轨迹的正弦度。2016年共有376,125个检测到的特征轨迹的数据集可公开使用，以促进基于外推的临近预报模型中位置预测的改进。