一种用于改进序列集合卡尔曼滤波器相关性估计的数据驱动方法
A data-driven method for improving the correlation estimation in serial ensemble Kalman filters
AI总结：
本文提出一种数据驱动方法，通过线性映射改进序列集合卡尔曼滤波器中相关性估计，尤其在小规模集合时效果显著。
摘要：
本文介绍了一种用于改进序列集合卡尔曼滤波器中相关性估计的数据驱动方法。该方法在每次同化循环中寻找一个线性映射，将估计不佳的样本相关性转换为改进的相关性。该映射通过离线训练过程获得，作为线性回归问题的解，该问题利用从历史同化产品中获得的适当样本相关性统计信息。在理想化的OSSE中使用洛伦兹-96模型，并针对线性和非线性观测模型的不同情况，所提出的方法提高了滤波估计，尤其是在集合大小相对于状态空间维度较小时效果尤为明显。
英文摘要：
A data-driven method for improving the correlation estimation in serial ensemble Kalman filters is introduced. The method finds a linear map that transforms, at each assimilation cycle, the poorly estimated sample correlation into an improved correlation. This map is obtained from an offline training procedure without any tuning as the solution of a linear regression problem that uses appropriate sample correlation statistics obtained from historical data assimilation products. In an idealized OSSE with the Lorenz-96 model and for a range of cases of linear and nonlinear observation models, the proposed scheme improves the filter estimates, especially when the ensemble size is small relative to the dimension of the state space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
