Establishing Links between Atmospheric Dynamics and Non-Gaussian Distributions and Quantifying Their Effects on Numerical Weather Prediction

建立大气动力学和非高斯分布之间的联系并量化它们对数值天气预报的影响

• 批准号: 1738206
• 负责人: Steven Fletcher
• 金额: $ 67.49万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738206&HistoricalAwards=false
• 关键词: Establishing; Links; between; Atmospheric; Dynamics

Data assimilation is a method that is used to incorporate observations into a numerical model to improve the representation of the current state of the system. Data assimilation is widely used in atmospheric sciences to assist weather forecasting models, making use of data from satellites, weather stations, weather balloons and many other systems. The focus of the research in this project are the errors that arise between the observations and model state. The errors are assumed to be Gaussian, or evenly distributed, but for some variables that may not be the case. The research in this award will improve our understanding of non-Gaussian errors. The potential impact on society would be improved weather forecasting. Data assimilation is also growing in other areas of science, so the research potentially has cross-cutting applications. An early career scientist will also be trained in this growing area of research.The research team will address several different aspects about how non-Gaussian distributed errors affect data assimilation and retrieval systems, but also how to detect when the Gaussian assumption is not optimal. Research will move forward on five topics: 1) Build conditional probability density functions (PDF) for different atmospheric dynamics from forecast difference fields, as a proxy for the background error fields, and then develop mathematical and stochastical models to link the conditional PDFs to specific atmospheric dynamics, 2) Derive, test in a toy problem, and then implement into the WRF-GSI, a mixed PDF hybrid variational-ensemble system, 3) Investigate the impact of different PDF assumptions for different scales of dynamics in both retrieval and hybrid data assimilation systems, 4) Extend the lognormal detection algorithm to a near real-time capability for educational diagnostics, 5) Create web pages to illustrate the different values that the systems produce combined with the detection algorithm output as an educational tool for researchers to see the effects of the distributions on the performance of the retrievals.

数据同化是一种用于将观测结果纳入数值模型以改进系统当前状态表示的方法。 数据同化广泛应用于大气科学中，利用来自卫星、气象站、气象气球和许多其他系统的数据来协助天气预报模型。 该项目研究的重点是观测值和模型状态之间出现的误差。 假设误差为高斯分布或均匀分布，但对于某些变量可能并非如此。 该奖项的研究将提高我们对非高斯误差的理解。 对社会的潜在影响将是改善天气预报。 数据同化在其他科学领域也在增长，因此该研究可能具有跨领域应用。 早期职业科学家也将在这个不断发展的研究领域接受培训。研究团队将解决非高斯分布误差如何影响数据同化和检索系统的几个不同方面，以及如何检测高斯假设何时不是最优的。 研究将在五个主题上推进：1）根据预测差异场为不同的大气动力学建立条件概率密度函数（PDF），作为背景误差场的代理，然后开发数学和随机模型将条件概率密度函数与特定的概率密度函数联系起来。大气动力学，2) 在玩具问题中进行推导和测试，然后实施到 WRF-GSI（一个混合 PDF 混合变分集成系统）中，3) 研究不同 PDF 假设对不同尺度动力学的影响检索和混合数据同化系统，4) 将对数正态检测算法扩展到教育诊断的近实时能力，5) 创建网页来说明系统与检测算法输出相结合作为教育工具产生的不同值以便研究人员了解分布对检索性能的影响。

项目成果

Detection of Non‐Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model

使用机器学习技术检测非高斯行为：Lorenz 63 模型的案例研究

• DOI: 10.1029/2019jd031551
• 发表时间: 2020
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: Goodliff, Michael;Fletcher, Steven;Kliewer, Anton;Forsythe, John;Jones, Andrew
• 通讯作者: Jones, Andrew

Quantification of Optimal Choices of Parameters in Lognormal Variational Data Assimilation and Their Chaotic Behavior

对数正态变分数据同化中参数最优选择的量化及其混沌行为

• DOI: 10.1007/s11004-018-9765-7
• 发表时间: 2019
• 期刊: Mathematical Geosciences
• 影响因子: 2.6
• 作者: Fletcher, Steven J.;Kliewer, Anton J.;Jones, Andrew S.
• 通讯作者: Jones, Andrew S.