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CAREER: Data Assimilation for Massive Spatio-Temporal Systems Using Multi-Resolution Filters

职业：使用多分辨率滤波器对大规模时空系统进行数据同化

基本信息

批准号：
1654083
负责人：
Matthias Katzfuss
金额：
$ 40万
依托单位：
Texas A&M University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-01 至 2023-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1654083&HistoricalAwards=false
关键词：
CAREER Data Assimilation Massive Spatio

项目摘要

The research supported by this award will produce powerful and scalable open-source software for data assimilation in large spatio-temporal systems with varying degrees of nonlinearity. It will lead to improved inference, forecasts, diagnostics, downscaling, and calibration using data assimilation in many fields of science with direct impact on society, including weather forecasting, climate studies, renewable energy, and pollution monitoring. Despite the great importance and highly statistical nature of data assimilation, there is a lack of statisticians involved in this research area. Thus, the educational component of this project revolves around bridging the gap between the statistics and data-assimilation communities, and getting more statisticians involved in the latter.The principal investigator will develop approaches for filtering inference on high-dimensional states that can outperform existing methods in linear and nonlinear settings. The novel approaches are based on the multi-resolution approximation, a state-of-the-art method for spatial covariance approximations that employs many adaptive, compactly supported basis functions at multiple resolutions. Algorithmic implementations of the methods are highly scalable and can take full advantage of massively parallel high-performance computing systems. Validation, testing, and comparison of the methods will be carried out using realistic observations simulated from models of varying complexity.

该奖项支持的研究将为具有不同程度非线性的大型时空系统中的数据同化提供强大且可扩展的开源软件。它将在许多对社会有直接影响的科学领域，包括天气预报、气候研究、可再生能源和污染监测，使用数据同化改进推理、预测、诊断、缩小尺度和校准。尽管数据同化的重要性和高度的统计性质，有缺乏统计学家参与这一研究领域。因此，该项目的教育部分围绕着弥合统计和数据同化社区之间的差距，并使更多的统计学家参与后者。首席研究员将开发在高维状态下过滤推理的方法，该方法可以在线性和非线性设置中优于现有方法。新方法基于多分辨率近似，这是一种最先进的空间协方差近似方法，在多个分辨率下采用了许多自适应、紧凑支持的基函数。这些方法的算法实现具有高度可扩展性，可以充分利用大规模并行高性能计算系统的优势。验证，测试和比较的方法将进行使用现实的观察模拟从不同的复杂性模型。