CAREER: Developing Dynamic Relational Models to Anticipate Tornado Formation

这项研究的目标是通过开发在空间和时间变化的关系数据中发现统计模式的先进技术来彻底改变预测龙卷风的能力。这些模式被应用于通过资料同化和模拟获得的完整的气象量场。多普勒雷达数据有限，虽然现代数据同化技术可以估计未观测到的数量，但由此产生的四维场过于复杂，无法由人类或现有数据挖掘技术提取有意义的、可重复的模式。通过研究整个变量场，这些模型可以识别高级特征之间的关键交互作用。这些模型是在与领域专家的密切合作下开发和验证的。跨学科研究用于改善计算机科学(CS)的留住和招聘。这是基于最近的证据，即代表不足的群体不会被计算机职业吸引，因为他们不理解如何使用计算机来解决现实世界的问题。在早期CS和气象学课程中引入真实的项目将提高这两个专业受过技术培训的学生的数量。这项研究的主要更广泛的影响是通过减少人的生命、财产和金钱损失的潜力对社会产生的影响。模型将在验证后提供给业务气象学家。另一个更广泛的影响将来自于通过正宗的项目增加面向计算的辅修和专业的数量。所有数据和结果将通过同行评议的出版物和可在项目网站(http://www.cs.ou.edu/~amy/career/).)上查阅的开放源码在线储存库传播

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/).