Data Mining Storm Attributes from Spatial Grids
Data Mining Storm Attributes from Spatial Grids
复制标题
从空间网格数据挖掘风暴属性
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Travis M. Smith
中科院分区：
文献类型：
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作者：
V. Lakshmanan;Travis M. Smith
摘要 描述了一种识别风暴并捕获已识别风暴的地理和时间范围内的标量特征的技术。该识别技术依赖于对观测场中的网格点进行聚类，以找到自相似且空间相干的簇，以满足对风暴的传统理解。从这些风暴中，可以提取几何、空间和时间特征。然后可以对这些标量特征进行数据挖掘，以客观、数据驱动的方式回答多种类型的研究问题。通过使用该技术回答预报员技能和闪电可预测性问题来说明这一点。
Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability.