...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Ocean Dynamics >Variational data assimilation for parameter estimation: application to a simple morphodynamic model
【24h】

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

机译：参数估计的变分数据同化：应用于简单的形态动力学模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error co-variance matrices is crucial for success. A simple ID model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.
机译：数据同化是一种先进的数学技术，用于将观测数据与模型预测相结合，以产生状态和参数估计值，从而最准确地逼近真实系统的当前和未来状态。该技术通常在大气和海洋建模中使用，它将经验观察与模型预测相结合以产生更准确和经过良好校准的预测。在这里，我们考虑了在沿海环境中的一种新颖应用，并描述了该方法还可以如何用于提供不确定形态动力学模型参数的改进估计。这是使用一种称为状态增强的技术来实现的。状态增强的早期应用通常采用4D-Var，卡尔曼滤波器或集成卡尔曼滤波器同化方案。我们的新方法基于计算上便宜的3D-Var方案，其中误差协方差矩阵的规范对于成功至关重要。一个简单的床形传播的ID模型被用来演示该方法。该方案能够恢复接近完美的参数值，因此提高了我们的模型预测未来测深的能力。这些积极的结果表明，有可能应用于更复杂的形态动力学模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号