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DDDAS是状态估计和观测过程的最佳协调。这对于环境应用来说是必不可少的，因为模型是不完美的，测量是有限和不确定的。环境DDDAS的一个关键部分是数据同化，广义上定义为使用所有相关信息估计系统状态的过程。该项目将开发一种新的数据同化方法，更好地利用观测数据来处理模型缺陷。这种新方法将在中尺度天气的背景下发展，如雷暴、飑线、飓风、降水和锋面。在这些情况下，预报错误发生在位置（“风暴在错误的地方”）和幅度（“预报风向偏离”）上。位置误差尤其重要，因为它们会降低我们预测风暴轨迹、发出警告和正确瞄准飞机等观测平台的能力。现有的同化方法在处理位置误差方面存在问题。它们不是直接纠正这些误差，而是倾向于通过扭曲振幅来补偿这些误差。扭曲的幅度估计会产生较差的预测。糟糕的预测本身就是一个问题，但在环境DDDAS的情况下，它们很容易使收集新观测结果的策略失效。在这个新的数据同化公式中，考虑了位置和幅度的误差。这导致最小化算法可以表示为两个步骤：一个正则变分对齐问题和幅度调整问题。可以在有或没有特征检测的情况下制定对齐，它保持动态一致性，并且允许系统地控制解决方案的平滑性。野外校准将显著促进DDDAS在环境问题上的发展。这项工作将有助于更好地分析中尺度天气，特别是飓风和强风暴。事实证明，用位置和振幅来表示误差是很普遍的。因此，从DDDAS的角度来看，这项工作将为处理水文学、生态学和海洋学等多种应用中的模型误差提供新的方法。场对准也很好地补充了业务天气预报中心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也将推动对齐方法的最新发展，这将有利于生物医学成像和目标识别研究。