CAREER: Developing Dynamic Relational Models to Anticipate Tornado Formation

职业：开发动态关系模型来预测龙卷风的形成

• 批准号: 0746816
• 负责人: Amy McGovern
• 金额: $ 50万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746816&HistoricalAwards=false
• 关键词: CAREER; Developing; Dynamic; Relational; Models

The goal of this research is to revolutionize the ability to anticipate tornadoes by developing advanced techniques for statistical pattern discovery in spatially and temporally varying relational data. These models are applied to complete fields of meteorological quantities obtained through data assimilation and simulation. Doppler radar data is limited and, while modern data assimilation techniques allow the unobserved quantities to be estimated, the resulting four- dimensional fields are too complicated for the extraction of meaningful, repeatable patterns by either humans or current data mining techniques. By studying a full field of variables, the models can identify critical interactions among high level features. The models are developed and verified in close collaboration with domain experts.The interdisciplinary research is used to improve retention and recruitment in computer science (CS). This draws on recent evidence that underrepresented groups are not drawn to computing careers because they do not appreciate how computing can be used to solve real world problems. Introducing authentic projects into both early CS and meteorology classes will improve the number of technically trained students in both majors.The primary broader impact of this research is to society, through the potential for reduction in loss of human life, property, and money. Models will be made available to operational meteorologists as they are verified. Another broader impact will come from increasing the number of computing oriented minors and majors through authentic projects. All data and results will be disseminated through peer reviewed publications and via open source online repositories accessible on the project Web site (http://www.cs.ou.edu/~amy/career/).

这项研究的目标是通过开发先进的技术，在空间和时间变化的关系数据的统计模式发现，革命性的预测龙卷风的能力。 这些模式被应用于通过资料同化和模拟获得的气象量的完整场。 多普勒雷达数据是有限的，虽然现代数据同化技术允许估计未观测到的量，但由此产生的四维场太复杂，无法通过人类或当前的数据挖掘技术提取有意义的、可重复的模式。 通过研究整个变量域，模型可以识别高级特征之间的关键相互作用。 这些模型是在与领域专家的密切合作下开发和验证的。跨学科的研究用于提高计算机科学（CS）的保留和招聘。 这是基于最近的证据，即代表性不足的群体不被计算机职业所吸引，因为他们不理解如何使用计算机来解决真实的世界问题。 将真实的项目引入早期的CS和气象学课程将提高这两个专业受过技术培训的学生的数量。这项研究的主要影响是社会，通过减少人类生命，财产和金钱损失的潜力。 模型经核实后将提供给业务气象学家。 另一个更广泛的影响将来自通过真实项目增加面向计算的未成年人和专业人员的数量。 所有数据和结果都将通过同行审查的出版物和可在项目网站（http：//www.cs.ou.edu/career/）上查阅的开放源码在线资料库传播。