• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nonlinear processes in geophysics >Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems
【24h】

Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems

机译：结合使用无充气和迭代集成卡尔曼滤波器，用于强非线性系统

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The finite-size ensemble Kalman filter (EnKF-N) is an ensemble Kalman filter (EnKF) which, in perfect model condition, does not require inflation because it partially accounts for the ensemble sampling errors. For the Lorenz '63 and '95 toy-models, it was so far shown to perform as well or better than the EnKF with an optimally tuned inflation. The iterative ensemble Kalman filter (IEnKF) is an EnKF which was shown to perform much better than the EnKF in strongly nonlinear conditions, such as with the Lorenz '63 and '95 models, at the cost of iteratively updating the trajectories of the ensemble members. This article aims at further exploring the two filters and at combining both into an EnKF that does not require inflation in perfect model condition, and which is as efficient as the IEnKF in very nonlinear conditions. brbr In this study, EnKF-N is first introduced and a new implementation is developed. It decomposes EnKF-N into a cheap two-step algorithm that amounts to computing an optimal inflation factor. This offers a justification of the use of the inflation technique in the traditional EnKF and why it can often be efficient. Secondly, the IEnKF is introduced following a new implementation based on the Levenberg-Marquardt optimisation algorithm. Then, the two approaches are combined to obtain the finite-size iterative ensemble Kalman filter (IEnKF-N). Several numerical experiments are performed on IEnKF-N with the Lorenz '95 model. These experiments demonstrate its numerical efficiency as well as its performance that offer, at least, the best of both filters. We have also selected a demanding case based on the Lorenz '63 model that points to ways to improve the finite-size ensemble Kalman filters. Eventually, IEnKF-N could be seen as the first brick of an efficient ensemble Kalman smoother for strongly nonlinear systems.
机译：有限大小的集合卡尔曼滤波器（EnKF-N）是一个集合卡尔曼滤波器（EnKF），在理想模型条件下，不需要充气，因为它部分解决了集合采样误差。到目前为止，对于Lorenz '63和'95玩具模型，它的表现和EnKF一样好，甚至具有最佳的充气调整效果。迭代集合卡尔曼滤波器（IEnKF）是一个EnKF，在Lorenz '63和'95模型等强非线性条件下，其表现出比EnKF更好的性能，但代价是迭代更新集合成员的轨迹。本文旨在进一步探讨这两个滤波器，并将两者组合为一个EnKF，该EnKF在理想模型条件下不需要膨胀，并且在非常非线性的条件下与IEnKF一样有效。 在此研究中，首先介绍了EnKF-N，并开发了新的实现。它将EnKF-N分解为便宜的两步算法，相当于计算最佳膨胀因子。这提供了在传统的EnKF中使用充气技术的理由，以及为什么它通常可以高效地使用的理由。其次，在基于Levenberg-Marquardt优化算法的新实现之后，引入了IEnKF。然后，将两种方法结合起来以获得有限大小的迭代集成卡尔曼滤波器（IEnKF-N）。使用Lorenz '95模​​型在IEnKF-N上进行了一些数值实验。这些实验证明了其数值效率和性能至少可以提供两种滤波器中的最佳性能。我们还基于Lorenz '63模型选择了一个苛刻的案例，该案例指出了改进有限大小集成Kalman滤波器的方法。最终，IEnKF-N可以看作是用于强非线性系统的高效整体Kalman平滑器的第一块砖。

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