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A damaging downburst prediction and detection algorithm for the WSR-88D

机译：WSR-88D的破坏性突降预测和检测算法

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摘要

The problem of predicting the onset of damaging downburst winds from high-reflectivity storm cells that develop in an environment of weak vertical shear with Weather Surveillance Radar-1988 Doppler (WSR-88D) is examined. Ninety-one storm cells that produced damaging outflows are analyzed with data from the WSR-88D network, along with 1247 nonsevere storm cells that developed in the same environments. Twenty-six reflectivity and radial velocity-based parameters are calculated for each cell, and a linear discriminant analysis was performed on 65% of the dataset in order to develop prediction equations that would discriminate between severe downburst-producing cells and cells that did not produce a strong outflow. These prediction equations are evaluated on the remaining 35% of the dataset. The datasets were resampled 100 times to determine the range of possible results. The resulting automated algorithm has a median Heidke skill score (HSS) of 0.40 in the 20-45-km range with a median lead time of 5.5 min, and a median HSS of 0.17 in the 45-80-km range with a median lead time of 0 min. As these lead times are medians of the mean lead times calculated from a large, resampled dataset, many of the storm cells in the dataset had longer lead times than the reported median lead times.
机译：检验了使用Weather Surveillance Radar-1988 Doppler（WSR-88D）预测在弱垂直剪切环境中发展的高反射性风暴单元破坏性爆发暴风的开始的问题。使用来自WSR-88D网络的数据分析了产生破坏性流出的91个风暴单元，以及在相同环境中发展的1247个非严重风暴单元。为每个像元计算了26个反射率和基于径向速度的参数，并对65％的数据集进行了线性判别分析，以便开发出预测方程，以区分产生严重下爆发的像元和没有产生爆破的像元大量流出。这些预测方程在数据集的其余35％上进行评估。将数据集重新采样100次以确定可能结果的范围。所得的自动化算法在20-45公里范围内的中位Heidke技能得分（HSS）为0.40，中位提前时间为5.5分钟，在45-80公里范围内的中位HSS为0.17，中位领先时间0分钟的时间由于这些提前期是从大型重新采样的数据集计算得出的平均提前期的中位数，因此数据集中的许多风暴单元的提前期都比所报告的中位数提前期更长。

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