Online Uncertainty Quantification for Novel Atmospheric Measurements

新型大气测量的在线不确定性量化

• 批准号: 2136969
• 负责人: Jonathan Poterjoy
• 金额: $ 41.37万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136969&HistoricalAwards=false
• 关键词: Online; Uncertainty; Quantification; Novel; Atmospheric

In numerical weather modeling, current observations of the state of the atmosphere are assimilated as an initial condition into the model. For the observations to be as useful as possible, the range of potential uncertainty in the measurements needs to be determined. For completely new types of measurements, such as remote sensing from satellites, it is difficult to independently validate the observations and determine the uncertainty characteristics. This work in this project will assess a variety of numerical techniques to determine the uncertainty of these new observations. The main impact of the project will be on weather forecasting, with the potential for the information to be used in various other fields. A graduate student will be involved in the project, ensuring the training of the next generation of data assimilation experts. This project will address the topic of uncertainty quantification (UQ) for atmospheric observations. More specifically, the project will target “novel” measurements, where new observations are unable to be validated against independent observations. The research team will perform an examination of current methodology and develop new methods for advancing the practice of online observation UQ. The first step of the project will be the development of experiments using the two-scale Lorenz (L96) model and the subsequent evaluation of various existing strategies for uncertainty quantification. A new theoretical development based on Kernel density estimates (KDE) will also be tested and matured. The research team will then expand the analysis to general circulation model (GCM) use cases. The result of the project will be: 1) An exhaustive evaluation of assumptions made by leading observation UQ techniques suggested for geoscience, and 2) A new UQ technique for non-Gaussian error estimation.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.

在数值天气模拟中，大气状态的当前观测值作为初始条件被同化到模型中。 为了使观测尽可能有用，需要确定测量中潜在不确定性的范围。 对于全新类型的测量，如卫星遥感，很难独立验证观测结果并确定不确定性特征。 该项目的这项工作将评估各种数值技术，以确定这些新观测的不确定性。 该项目的主要影响将是天气预报，并有可能将信息用于其他各个领域。 一名研究生将参与该项目，确保培训下一代数据同化专家。该项目将讨论大气观测的不确定性量化问题。 更具体地说，该项目将针对“新的”测量，其中新的观测结果无法与独立观测结果进行验证。 研究小组将对当前的方法进行检查，并开发新的方法来推进在线观察UQ的实践。 该项目的第一步将是使用双尺度Lorenz（L96）模型开发实验，并随后评估各种现有的不确定性量化策略。 基于核密度估计（KDE）的新理论发展也将得到测试和成熟。 然后，研究团队将分析扩展到大气循环模型（GCM）用例。 该项目的结果将是：1）对地球科学中建议的领先观测UQ技术所做的假设进行详尽的评估，以及2）一种用于非高斯误差估计的新UQ技术。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。