DDDAS-SMRP: Data Assimilation by Field Alignment

DDDAS-SMRP：通过场对齐进行数据同化

• 批准号: 0540259
• 负责人: Dennis McLaughlin
• 金额: --
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540259&HistoricalAwards=false
• 关键词: DDDAS; SMRP; Data; Assimilation; Field

An ideal DDDAS will optimally coordinate state estimation and the observation process. This is indispensable for environmental applications, where models are imperfect and measurements are limited and uncertain. A key part of environmental DDDAS is data assimilation, broadly defined as the process of estimating the state of a system using all relevant information. This project will develop a new approach to data assimilation that makes better use of observations to deal with model imperfections. This new approachwill be developed in the context of mesoscale weather, such as thunderstorms, squall-lines, hurricanes, precipitation, and fronts. In these situations, forecast errors occur in both position ("the storm is in the wrong place") and amplitude ("forecast winds are off"). Position errors are particularly important since they degrade our ability to predict storm tracks, issue warnings, and properly target observation platforms such as aircraft. Current assimilation methods have problems dealing with position errors. Instead of correcting these errors directly, they tend to compensate for them by distorting amplitudes. Distorted amplitude estimates can produce poor forecasts. Poor forecasts are a problem in their own right but, in the case of an environmental DDDAS, they can easily make strategies for gathering new observations ineffective. In this new formulation for data assimilation accounts for errors in both position and amplitude. This leads to a minimization algorithm that can be expressed in two steps: a regularized variational alignment problem and an amplitude adjustment problem. Alignment can be formulated with or without feature detection, it maintains dynamical consistency, and it permits the smoothness of the solution to be systematically controlled. Field alignment should significantly advance the state of DDDAS for environmental problems.This work will lead to better analysis of mesoscale weather, especially hurricanes and severe storms. It turns out that expressing errors in terms of position and amplitude is quite general. Thus, from the perspective of DDDAS, this work will provide new ways to deal with model error in applications as diverse as hydrology, ecology, and oceanography. Field alignment also nicely complements existing amplitude-oriented assimilation methods used in operational weather forecasting centers. Finally, the regularization aspects of this work will also advance the state of the art in alignment methods, which will benefit biomedical imaging and object recognition research.

理想的DDDA将最佳地协调状态估计和观察过程。对于环境应用，这是必不可少的，其中模型是不完善的，并且测量值有限且不确定。环境DDDA的关键部分是数据同化，广泛定义为使用所有相关信息估算系统状态的过程。该项目将开发出一种新的数据同化方法，以更好地利用观察值来处理模型缺陷。这种新方法将在中尺度天气的背景下开发，例如雷暴，que脚，飓风，降水和前部。在这些情况下，预测错误在两个位置都出现（“风暴在错误的位置”）和振幅（“预测风不在”）。位置错误尤其重要，因为它们会降低我们预测风暴轨道，发出警告以及适当针对飞机等观察平台的能力。当前的同化方法在处理位置错误方面存在问题。它们没有直接纠正这些错误，而是倾向于通过扭曲幅度来补偿它们。扭曲的振幅估计值会产生较差的预测。较差的预测本身就是一个问题，但是在环境DDDA的情况下，他们可以轻松地制定策略，以无效地收集新的观察结果。 在这个新的数据中，用于数据同化的公式说明了位置和振幅的错误。这导致了一种最小化算法，该算法可以通过两个步骤表示：正规化的变分对准问题和振幅调整问题。可以在有或没有特征检测的情况下制定对齐方式，它可以保持动态的一致性，并且允许系统控制的解决方案的平滑度。现场对齐应大大推进DDDA的状态，以解决环境问题。这项工作将使对中尺度天气，尤其是飓风和严重风暴的更好分析。事实证明，在位置和振幅方面表达错误是相当普遍的。因此，从DDDA的角度来看，这项工作将提供新的方法来处理像水文学，生态学和海洋学等多样化的应用中的模型错误。现场对齐还很好地补充了在操作天气预测中心中使用的现有面向振幅的同化方法。最后，这项工作的正则化方面还将以对齐方式提高最新技术，这将使生物医学成像和对象识别研究受益。