Methods for Nonlinear, Non-Gaussian, and Data-Driven Ensemble Data Assimilation in Large-Scale Applications

大规模应用中非线性、非高斯和数据驱动的集合数据同化方法

• 批准号: 2152814
• 负责人: Ian Grooms
• 金额: $ 15万
• 依托单位: University of Colorado at Boulder
• 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: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152814&HistoricalAwards=false
• 关键词: Methods; Nonlinear; Non; Gaussian; Data

A wide range of disciplines, from weather to epidemiology to reservoir management, depend on data assimilation, that is, a set of methods that combine incomplete and imperfect observations with a forecasting model to estimate and predict the state of a complex, evolving system. For large-scale systems such as those in weather forecasting, computational efficiency is essential, so the methods used often rely on a Gaussian approximation - assuming that properties are distributed according to a bell curve - because this approximation unlocks highly efficient algorithms. However, in practice many quantities of interest, from sea ice thickness to rain rates, are not described by a bell curve, and predictions can be inaccurate. This project aims to develop new algorithms that are not based on a bell curve approximation but that can still be used in large-scale applications where computational efficiency is crucial. This inherently interdisciplinary project will provide a multitude of opportunities for training and professional development of the next generation of statisticians and data scientists, with a particular focus on enhancing diversity and inclusion.The new insight on which the research project is built is a novel representation of the Bayesian posterior distribution through the introduction of a new synthetic random variable. The Bayesian posterior can be represented as the expected value of the probability density of the state variable conditioned on the new variable, where the expectation is taken with respect to the posterior on the new variable. This insight enables the use of a two-step approach to sampling from the posterior. The first step is standard Bayesian sampling but with a lower dimensionality, while the second step is based on regression. The project will combine methods from low-dimensional Bayesian computation for the first step with generalized linear regression and/or machine-learning regression for the second step. A skeleton of the two-step approach is already implemented in the Data Assimilation Research Testbed (DART) software suite, and DART enables data assimilation with over 25 geoscientifically-relevant models including the National Water Model and the Community Earth System Model. The research findings will be implemented in a form of new advanced two-step non-Gaussian algorithms in DART, making them available to a wide range of users.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.

从天气到流行病学再到水库管理，许多学科都依赖于数据同化，即一组将不完整和不完善的观测与预测模型结合起来的方法，以估计和预测复杂的、不断发展的系统的状态。对于天气预报等大型系统，计算效率至关重要，因此所使用的方法通常依赖于高斯近似（假设属性根据钟形曲线分布），因为这种近似可以实现高效算法。然而，在实践中，从海冰厚度到降雨率等许多重要的量都不是用钟形曲线描述的，并且预测可能不准确。该项目旨在开发不基于钟形曲线近似但仍可用于计算效率至关重要的大规模应用的新算法。这个本质上是跨学科的项目将为下一代统计学家和数据科学家的培训和专业发展提供大量机会，特别注重增强多样性和包容性。该研究项目所依据的新见解是通过引入新的合成随机变量来得到贝叶斯后验分布。贝叶斯后验可以表示为以新变量为条件的状态变量的概率密度的期望值，其中期望是相对于新变量的后验而取的。这种见解使得能够使用两步方法从后验中采样。第一步是标准贝叶斯采样，但维度较低，而第二步基于回归。该项目将结合第一步的低维贝叶斯计算方法和第二步的广义线性回归和/或机器学习回归。两步法的框架已在数据同化研究测试台 (DART) 软件套件中实施，DART 能够使用超过 25 个地球科学相关模型（包括国家水模型和社区地球系统模型）进行数据同化。研究成果将以新型先进的两步非高斯算法的形式在 DART 中实现，从而可供广大用户使用。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。

项目成果

Two Methods for Data Assimilation of Wind Direction

风向资料同化的两种方法

• DOI: 10.16993/tellusa.2005
• 发表时间: 2023-01
• 期刊: Tellus A: Dynamic Meteorology and Oceanography
• 作者: Grooms; Ian
• 通讯作者: Ian

Analog ensemble data assimilation in a quasigeostrophic coupled model

准地转耦合模型中的模拟系综数据同化

• DOI: 10.1002/qj.4446
• 发表时间: 2023-03-25
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: I. Grooms;Camille Renaud;Z. Stanley;L. Minah Yang
• 通讯作者: L. Minah Yang