DDDAS-SMRP: Data Assimilation by Field Alignment

理想的DDDAS将最佳地协调状态估计和观测过程。这对于环境应用来说是必不可少的，因为环境应用的模型是不完善的，测量是有限的和不确定的。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处理模型缺陷。这种新的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，预报误差会出现在位置（“风暴在错误的地方”）和幅度（“预报风关闭”）上。位置误差尤其重要，因为它们降低了我们预测风暴路径、发布警报和正确定位飞机等观测平台的能力。目前的同化方法在处理位置误差方面存在问题。他们倾向于通过扭曲振幅来补偿这些误差，而不是直接校正这些误差。失真的振幅估计可能会产生糟糕的预测。预测不佳本身就是一个问题，但在环境DDDAS的情况下，它们很容易使收集新观测结果的战略无效。 在这个新的公式中，数据同化考虑了位置和振幅的误差。这导致一个最小化算法，可以表示为两个步骤：一个正则化的变分对齐问题和幅度调整问题。对齐可以在有或没有特征检测的情况下制定，它保持动态一致性，并且它允许系统地控制解决方案的平滑度。实地调整应能大大提高DDDAS在环境问题方面的地位，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，用位置和幅度来表达误差是相当普遍的。因此，从DDDAS的角度来看，这项工作将提供新的方法来处理水文学，生态学和海洋学等不同应用中的模型误差。场对齐也很好地补充了业务天气预报中心使用的现有振幅导向同化方法。最后，这项工作的正则化方面也将推进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。

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.