CAREER: Improving Convective-Scale Weather Prediction through Advanced Bayesian Filtering, Verification, and Uncertainty Quantification

这项研究项目的动机是美国每年严重的对流风暴、飓风和洪水对生命和财产造成的巨大影响。这项研究将解决当前数据同化(DA)和天气模式不确定性量化方面的基本限制。这项研究的结果将对专注于这些天气灾害的多学科努力产生巨大影响，这将导致预测误差的长期持续减少。智力优势：该项目将侧重于：1)开发和测试一个先进的发展议程框架，旨在消除目前在实践中使用的特定假设；2)采用复杂的不确定性可视化计划，探索从总体估计的概率信息；3)为马里兰大学和外部暑期学校的课堂课程开发一个发展议程研究和教育模块。该项目将采用基于“粒子过滤器”的新贝叶斯过滤技术。该方法使用模型模拟样本来表示基于当前和过去观测的模型状态变量的概率属性。除了为天气预报提供最彻底的粒子过滤器调查外，这项研究还将应用这种方法来隔离模型和观测系统中的偏差源。这项工作的另一个独特方面是它使用了由统计学家和计算机科学家开发的新的可视化技术。这些技术形成了一套基于“数据深度”的分析工具，它允许通过等高线和曲线框图对多变量集合输出进行有洞察力的查看。它们还提供了一种验证来自系综的概率量的方法，而不需要假设潜在的误差分布，从而有助于验证非参数DA技术，如粒子过滤器。广泛影响：这项研究直接受到影响美国和世界各地个人福祉的危险天气事件的推动。除了提高数值预报的预报技能外，这项工作的一部分还侧重于集合数据集的不确定性量化和可视化，旨在改善恶劣天气风险与公众的沟通。国家大气研究中心的数据同化研究试验台将致力于在这项工作中取得的进展，这是一个将发展议程研究与地球科学家联系起来的社区软件基础设施。该项目还包括为地球科学学生和研究人员开发教育发展援助模块的详细战略。该单元将随着工作计划的发展而发展，并成为课堂练习和暑期学校活动的宝贵学习工具，用于促进STEM领域的多样性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project is motivated by the large impact severe convective storms, hurricanes, and flooding have on life and property each year in the United States. The research will address fundamental limitations in current data assimilation (DA) and uncertainty quantification for weather models. The outcome from the research will have a tremendous impact on multi-disciplinary efforts that focus on these weather hazards, which will lead to sustained long-term reductions in forecast errors. Intellectual Merit:The project will focus on 1) the development and testing of an advanced DA framework designed to eliminate specific assumptions currently used in practice; 2) the adoption of sophisticated uncertainty visualization schemes for exploring probabilistic information estimated from ensembles; and 3) the development of a DA research and educational module for class curriculum at the University of Maryland and external summer schools.The project will adopt new Bayesian filtering techniques based on "particle filters". The method uses samples of model simulations to represent probabilistic properties of model state variables conditioned on current and past observations. In addition to providing the most thorough investigation of particle filters for weather prediction, the research will apply the method for isolating sources of bias in models and observing systems. Another unique aspect of this work is its use of novel visualization techniques developed by statisticians and computer scientists. These techniques form a set of analysis tools based on "data depth," which allows for an insightful look at multivariate ensemble output via contour and curve boxplots. They also provide a means of verifying probabilistic quantities from ensembles with no assumptions for the underlying error distribution, thus aiding in the verification of non-parametric DA techniques, like particle filters.Broader Impacts:This research is motivated directly by hazardous weather events that affect the well-being of individuals in the United States and around the world. In addition to advancing predictive skill in numerical predictions, a portion of this work focuses on uncertainty quantification and visualization of ensemble datasets, which aims to improve the communication of severe weather risk to the public. DA advancements made during this work will be committed to the National Center for Atmospheric Research's Data Assimilation Research Testbed, a community software infrastructure for linking DA research to geoscientists. The project also includes a detailed strategy for developing an educational DA module for geoscience students and researchers. The module will evolve with the work plan and result in a valuable learning tool for class exercises and summer school activities used to promote diversity in STEM fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.