Assessing the performance of data assimilation algorithms which employ linear error feedback.
Assessing the performance of data assimilation algorithms which employ linear error feedback.
复制标题
DOI：
10.1063/1.4965029
复制
发表时间：
2016-10
期刊：
影响因子：
2.9
通讯作者：
Noeleene Mallia-Parfitt;J. Bröcker
中科院分区：
文献类型：
--
作者：
Noeleene Mallia-Parfitt;J. Bröcker
数据同化是指找到一个动力学模型的（近似）轨迹，该轨迹（近似）匹配给定的观测集。根据现有的观测结果直接评估轨迹可能会对性能产生过于乐观的看法，因为观测结果已经被用来找到解决方案。一个可能的补救措施，简单地包括估计乐观，从而提供了一个更现实的图片的“样本”的性能。我们的方法受到了统计学中用于模型选择和评估目的的统计学习方法的启发。将类似的想法应用于数据同化算法，产生了一种操作上可行的评估方法。该方法可用于改进模型的性能或数据同化本身。这是通过优化的数据同化采用线性反馈的反馈增益。
Data assimilation means to find an (approximate) trajectory of a dynamical model that (approximately) matches a given set of observations. A direct evaluation of the trajectory against the available observations is likely to yield a too optimistic view of performance, since the observations were already used to find the solution. A possible remedy is presented which simply consists of estimating that optimism, thereby giving a more realistic picture of the "out of sample" performance. Our approach is inspired by methods from statistical learning employed for model selection and assessment purposes in statistics. Applying similar ideas to data assimilation algorithms yields an operationally viable means of assessment. The approach can be used to improve the performance of models or the data assimilation itself. This is illustrated by optimising the feedback gain for data assimilation employing linear feedback.