Connell S. Miller, Gregory A. Kopp, D. Sills, Daniel G. Butt
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引用次数: 0
摘要
目前,增强型藤田风级并不考虑各种大型紧凑物体(如车辆、建筑设备、农用设备/干草捆等)随风向移动的情况,而这些物体在事件发生后的损害调查中经常被发现。造成这种情况的原因之一是,龙卷风中的碎片建模具有相当大的不确定性,因为有许多参数需要确定,导致难以使用轨迹来分析龙卷风的风速。本文旨在开发一种使用龙卷风分析模型的取证工具,以便根据大型紧凑物体的轨迹估算出悬浮风速。具体方法是进行蒙特卡罗模拟,随机选择参数,并绘制累积分布函数,显示在每种风速下发生悬浮的可能性。在分析了加拿大几场记录在案的龙卷风的碎片悬浮情况后,结果表明该方法提供的阈值悬浮风速与其他方法给出的估计风速相似。然而,轨迹的引入所产生的估计悬浮风速要高于根据结构损坏情况进行的地面勘测评估所给出的 EF 级。要更好地理解这些差异,还需要进一步的研究。
Estimating wind speeds in tornadoes using debris trajectories of large compact objects
Currently, the Enhanced Fujita scale does not consider the wind-induced movement of various large compact objects such as vehicles, construction equipment, farming equipment / haybales, etc. that are often found in post-event damage surveys. One reason for this is that modelling debris in tornadoes comes with considerable uncertainties since there are many parameters to determine, leading to difficulties in using trajectories to analyze wind speeds of tornadoes. This paper aims to develop a forensic tool using analytical tornado models to estimate lofting wind speeds based on trajectories of large compact objects. This is accomplished by implementing a Monte Carlo simulation to randomly select the parameters and plotting cumulative distribution functions showing the likelihood of lofting at each wind speed. After analyzing the debris lofting from several documented tornadoes in Canada, the results indicate that the method provides threshold lofting wind speeds that are similar to the estimated speeds given by other methods. However, the introduction of trajectories produces estimated lofting wind speeds that are higher than the EF-scale rating given from the ground survey assessment based on structural damage. Further studies will be required to better understand these differences.
期刊介绍:
Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.