A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking
A Method for Identifying Midlatitude Mesoscale Convective Systems in Radar Mosaics. Part II: Tracking
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DOI：
10.1175/jamc-d-17-0294.1
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发表时间：
2018-07-01
影响因子：
3
通讯作者：
Ashley, Walker S.
中科院分区：
文献类型：
--
作者：
Haberlie, Alex M.;Ashley, Walker S.
这项研究是一个由两部分组成的研究的第二部分，该研究评估了图像处理和选择机器学习算法的能力，以检测、分类和跟踪连续的美国雷达反射率图像中的中纬度中尺度对流系统(MCS)。本文重点研究了该框架中的跟踪部分。跟踪是通过使用第一部分中生成的切片(瞬时MCS强度的快照)数据的两步过程完成的。第一步是执行时空匹配，将切片与时间上相邻的雷达反射率图像相关联，以生成条带或风暴路径。当发现多个切片匹配时，使用差值最小化过程将最相似的切片与现有条带相关联。一旦这一步完成，第二步就会将时空上接近的区域组合在一起。跟踪性能是通过计算所有可用条带构建扰动的选定度量来评估的，以确定跟踪的最佳方法。从这些条带产生的频率图和时间序列表明，根据以前的工作确定的这些条带的时空发生是合理的。此外，这些事件表现出与邻近美国的整体对流不同的日循环。最后，发现机器学习预测将MCS频率较高的地区限制在大平原的中部和东部。
This research is Part II of a two-part study that evaluates the ability of image-processing and select machine-learning algorithms to detect, classify, and track midlatitude mesoscale convective systems (MCSs) in radar-reflectivity images for the conterminous United States. This paper focuses on the tracking portion of this framework. Tracking is completed through a two-step process using slice (snapshots of instantaneous MCS intensity) data generated in Part I. The first step is to perform spatiotemporal matching, which associates slices through temporally adjacent radar-reflectivity images to generate swaths, or storm tracks. When multiple slices are found to be matches, a difference-minimization procedure is used to associate the most similar slice with the existing swath. Once this step is completed, a second step combines swaths that are spatiotemporally close. Tracking performance is assessed by calculating select metrics for all available swath-building perturbations to determine the optimal approach in tracking. Frequency maps and time series generated from the swaths suggest that the spatiotemporal occurrence of these swaths is reasonable as determined from previous work. Further, these events exhibit a diurnal cycle that is distinct from that of overall convection for the conterminous United States. Last, machine-learning predictions are found to limit areas of high MCS frequency to the central and eastern Great Plains.