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A robust method to identify cyclone tracks from gridded data

机译:一种从栅格数据中识别旋风径迹的可靠方法

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A system to derive tracks of barometric minima is presented. It isdeliberately using coarse input data in space (order of 2°×2°) and time (6-hourly to daily) as well as information from just onegeopotential level. It is argued that the results are, for one robust in thesense of an assumption of the IMILAST Project that the use of assimple as possible metrics should be strived for and for two tailored to theinput from reanalyses and GCMs. The methodology presented is a necessaryfirst step towards an automated storm track recognition scheme which will beemployed in a second paper to study the future development of atmosphericdynamics in a changing climate. The process towards obtaining storm tracks istwo-fold. In its first step cyclone centers are being identified. Theperformance of this step requires the existence of closed isolines, i.e., atopology in which a grid-point is surrounded by neighbours which all exhibithigher geopotential. The usage of this topology requirement as well as theconstraint of coarse data may lead, though, to limitations in identifyingcenters in geopotential fields with shallow gradients that may occur in thesummer months; moreover, some centers may potentially be missed in case of aconfiguration in which a small scale storm is located at the perimeter of adeep and very large low (a kind of "dent in a crater wall"). The second stepof the process strings the identified cyclone centers together in ameaningful way to form tracks. By way of several examples the capability toidentify known storm tracks is shown.
机译:提出了导出气压极小值的轨迹的系统。它故意使用空间(2°×2°的量级)和时间(从6小时到每天)中的粗略输入数据,以及仅来自一个地势水平的信息。有人认为,对于IMILAST项目的假设而言,结果是有力的,应力求尽可能利用assimple作为度量标准,并针对两个根据重新分析和GCM的输入量身定制的结果。提出的方法是迈向自动风暴轨迹识别方案的必要的第一步,该方法将在第二篇论文中使用,以研究气候变化中大气动力学的未来发展。获取风暴轨迹的过程有两个方面。第一步,确定气旋中心。该步骤的执行需要存在封闭的等值线,即拓扑结构,其中网格点被所有都具有较高地势的邻居包围。但是,这种拓扑要求的使用以及对粗略数据的约束可能会导致识别在夏季可能出现的具有浅梯度的地势场中的中心受限;此外,在小规模风暴位于深处和低谷的外围(一种“陷坑壁”的情况)的配置中,某些中心可能会丢失。处理的第二步以有意义的方式将识别出的旋风串在一起,形成轨道。通过几个示例,示出了识别已知风暴轨迹的能力。

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