Collaborative Research: Data-driven Inverse Sensitivity Analysis for Predictive Coastal Ocean Modeling

合作研究：用于预测沿海海洋建模的数据驱动的逆敏感性分析

• 批准号: 1228243
• 负责人: Clinton Dawson
• 金额: $ 24.94万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1228243&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; driven; Inverse

The goal of this project is to improve the predictive capabilities of computational models of the coastal ocean, by combining a novel measure-theoretic approach for inverse sensitivity with experimental data. Advanced computer models of the coastal ocean, such as the Advanced Circulation (ADCIRC) model, can be used in predictive mode to estimate storm surge as hurricanes approach landfall for the purposes of emergency evacuation and response. However, the accuracy of ADCIRC, and other computer models, relies on the painstaking process of model calibration based on uncertain input parameters. The investigators study the estimation and model sensitivity for certain critical parameters, in particular bathymetry, bottom friction, and wind stress. Applying the solution to the inverse problem for prediction is complicated by two issues. First, the map from the input data and parameter space to the observable space generally reduces the dimension which implies the inverse problem has set-valued solutions. Second, even though the models considered in this project provide deterministic physical descriptions, all of the data available is subject to natural stochastic variability as well as experimental/observational error and uncertainty generally described stochastically. The measure-theoretic algorithm computes a probability measure over the entire parameter space from which an ensemble of model selections may be chosen to deliver reliable predictions of critical quantities of interest such as maximum water elevation along the coast. The PIs study various mathematical issues including estimation of various sources of error inherent in a non-intrusive implementation of the measure-theoretic approach. The use of experimental data and the ADCIRC model creates a unique opportunity for verification and validation of proposed methods.Quantitative predictions of coastal ocean conditions is central to long-range studies of coastal sustainability, the development of priorities and policies for the restoration and maintenance of coastal ecosystems, enhancing the economic vitality of coastal communities, and assessing risk of coastal populations to natural disasters. While coastal predictions of various complexity have been under development and used routinely for decades now, a series of events over the past seven years has driven a revolution. Namely, Hurricane Katrina (2005), in devastating fashion, demonstrated the perils of underestimating the vulnerability of coastal communities to storm surge. Following on the heels of Katrina were hurricanes Rita (2005), Gustav (2008) and Ike (2008), which all caused tremendous damage to communities along the northern Gulf of Mexico, and more recently the Deepwater Horizon Oil Spill, which occurred off the coast of Louisiana and threatened the entire Gulf ecosystem. These events spurred a serious and sustained effort to improve the ability to predict coastal ocean conditions. However, the prediction of coastal conditions beyond what can be observed, e.g. predicting future maximum storm surge from current and near past coastal observation data in real-time, is an exceedingly challenging mathematical, statistical, and computational problem. In this project, the investigators study and apply state-of-the-art techniques in order to improve the predictive capabilities of coastal ocean models used to predict storm surge. The computational methodology and tools developed under this project are applicable to other problems in coastal engineering, marine science, material science and other engineering disciplines. Technology transfer of the mathematical and numerical methodologies developed under this project will occur with the coastal ocean modeling community, and with agencies such as the U.S. Army Corps of Engineers, NOAA, the Department of Homeland Security, state and local agencies, industry, and other universities in the U.S. and abroad.

该项目的目的是通过结合一种新型的测量理论方法与实验数据来提高沿海海洋计算模型的预测能力。 沿海海洋的高级计算机模型，例如高级循环（ADCIRC）模型，可用于预测模式，以估算风暴潮，因为飓风临近登陆，以进行紧急撤离和响应。 但是，ADCIRC和其他计算机模型的准确性依赖于基于不确定输入参数的模型校准的艰苦过程。 研究人员研究了某些关键参数的估计和模型敏感性，特别是测深，底部摩擦和风应力。 将解决方案应用于逆问题进行预测，这两个问题变得复杂。 首先，从输入数据和参数空间到可观察到的空间的地图通常会降低尺寸，这意味着逆问题具有设置值的解决方案。 其次，即使该项目中考虑的模型提供了确定性的物理描述，但所有可用的数据都符合自然的随机变异性以及实验/观察性误差和不确定性，并且通常会随机描述。 测量理论算法计算整个参数空间上的概率度量，可以从中选择模型选择集合来提供对关键兴趣量的可靠预测，例如沿海地区的最大水位高度。 PIS研究了各种数学问题，包括估计量度理论方法的非侵入性实施中固有的各种错误来源。 实验数据和ADCIRC模型的使用为验证和验证提出的方法创造了独特的机会。沿海海洋状况的量词预测是对沿海可持续性的远程研究，恢复和维持沿海生态系统的恢复和维护的优先级和政策的核心，并增强了沿海社区的经济活力，并评估沿海沿海地区的经济活力，并评估自然风险的风险。 尽管沿海关于各种复杂性的预测一直在发展，并且几十年来常规使用，但在过去的七年中，一系列事件引发了一场革命。 也就是说，卡特里娜飓风（2005）以毁灭性的方式证明了低估了沿海社区对风暴潮的脆弱性的危险。 紧随卡特里娜飓风的紧随其后的是飓风丽塔（2005），古斯塔夫（2008）和艾克（2008），这些都对墨西哥北部湾沿岸的社区造成了巨大损害，最近在路易斯安那州沿海地区发生了深水地平线溢油事件，发生在路易斯安那州沿海地区，并威胁着整个Gulf Ecosystem。 这些事件刺激了一项严肃而持续的努力，以提高预测沿海海洋状况的能力。 但是，对沿海条件的预测超出了可以观察到的。从当前和过去的沿海观测数据实时预测未来的最大风暴潮是一个极具挑战性的数学，统计和计算问题。 在该项目中，研究人员研究并采用了最新技术，以提高用于预测风暴潮的沿海海洋模型的预测能力。 该项目下开发的计算方法和工具适用于沿海工程，海洋科学，材料科学和其他工程学科的其他问题。沿海海洋建模社区以及美国陆军工程兵团，NOAA，国土安全部，州和地方机构，州和地方工业，行业以及美国和国外等机构等机构将发生在该项目下开发的数学和数值方法的技术转移。