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Radar data assimilation and forecasts of evolving nonlinear wave fields.

机译:不断发展的非线性波场的雷达数据同化和预测。

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摘要

The safety and effectiveness of offshore and marine operations can be greatly enhanced by combining real-time measurements of the wave field surrounding a vessel with short-term forecasts of the sea state. The objectives of this Ph.D. research project are to develop an efficient numerical model to predict nonlinear evolution of multi-directional sea states, and a technique to assimilate real time radar data into the model efficiently in order to provide improved forecasts of the short term evolution of the sea surface.;The wave prediction model is based on a pair of coupled nonlinear evolution equations for the free surface elevation and tangential velocity on the free surface. These equations are solved numerically using a pseudo-spectral method in which both variables are approximated by a truncated Fourier series. All linear operations in the evolution equations are evaluated in Fourier space while the nonlinear operations are computed in physical space and the fourth order Runge-Kutta scheme is used to evolve the variables in time. The numerical model is validated with the exact solutions for one and two-dimensional steady and free waves and experimental data for modulation instability.;Radar returns are inherently mixed with noise while numerical models suffer from the lack of correct initial conditions and discretization errors. Therefore, an efficient assimilation scheme is developed to find the optimal initial conditions that best fit a series of observations over a finite time interval. This is done by minimizing a cost function which is defined as the difference between measured and numerically predicted values of surface elevation. The gradient of the cost function with respect to the initial conditions is calculated using the adjoint technique. The data assimilation scheme is validated using synthetically generated observations for two and three-dimensional flows as well as real radar data collected from field experiments conducted off a ship in Alaska in April 2006.
机译:通过将船舶周围波场的实时测量结果与短期海况预测相结合,可以大大提高海上和海洋作业的安全性和有效性。本博士学位的目标研究项目是开发一种有效的数值模型来预测多方向海况的非线性演变,以及一种将实时雷达数据有效地吸收到模型中的技术,以便提供对海面短期演变的改进预测。波浪预测模型基于一对耦合的非线性演化方程,用于自由表面高程和自由表面上的切线速度。这些方程使用伪谱方法进行数值求解,其中两个变量均通过截短的傅立叶级数近似。演化方程中的所有线性运算都在傅立叶空间中进行评估,而非线性运算是在物理空间中进行计算,并且使用四阶Runge-Kutta方案对变量进行时间演化。用一维和二维稳态和自由波的精确解以及调制不稳定性的实验数据对数值模型进行了验证;雷达回波固有地与噪声混合,而数值模型则缺乏正确的初始条件和离散误差。因此,开发了一种有效的同化方案,以找到最适合在有限时间间隔内进行一系列观测的最佳初始条件。这是通过最小化成本函数来完成的,成本函数定义为表面高程的测量值和数字预测值之间的差。使用伴随技术计算成本函数相对于初始条件的梯度。数据同化方案使用2006年4月在阿拉斯加一艘船上进行的野外实验收集的二维和三维流动合成观测数据以及真实雷达数据进行了验证。

著录项

  • 作者

    Hassanaliaragh, Sina.;

  • 作者单位

    University of Michigan.;

  • 授予单位 University of Michigan.;
  • 学科 Engineering Marine and Ocean.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 195 p.
  • 总页数 195
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 海洋工程;
  • 关键词

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