从地球科学数据集中提取时空模式

E. Mesrobian, R. Muntz, J. R. Santos, E. C. Shek, C. Mechoso, J. Farrara, P. Stolorz
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引用次数: 24

摘要

当今地球物理科学面临的一个主要挑战是缺乏高级分析工具来研究传感器产生的大量数据或对气候模式的长期模拟。我们开发了一个名为QUEST的原型信息系统,以提供对海量数据集的基于内容的访问。QUEST利用工作站和teraFLOP计算机分析地球科学数据,生成可作为高级索引的时空特征。我们的第一个应用领域是全球气候变化模型。在最初的原型中,提取的第一个特征是从一个环流模式产生的多年气候模拟的输出中提取的气旋轨迹。我们提出了一种气旋提取算法,并举例说明了使用气旋索引访问GCM数据子集以进行进一步分析和可视化
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Extracting spatio-temporal patterns from geoscience datasets
A major challenge facing geophysical science today is the unavailability of high-level analysis tools with which to study the massive amount of data produced by sensors or long simulations of climate models. We have developed a prototype information system called QUEST to provide content-based access to massive datasets. QUEST employs workstations as well as teraFLOP computers to analyze geoscience data to produce spatial-temporal features that can be used as high-level indexes. Our first application area is global change climate modeling. In the initial prototype, the first features extracted are cyclones trajectories from the output of multi-year climate simulations produced by a General Circulation Model. We present an algorithm for cyclone extraction and illustrate the use of cyclone indexes to access subsets of GCM data for further analysis and visualization.<>
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