This work introduces a new, distributed implementation of the Ensemble Kalman Filter (EnKF) that allows for non-sequential assimilation of large datasets in high-dimensional problems. The traditional EnKF algorithm is computationally intensive and exhibits difficulties in applications requiring interaction with the background covariance matrix, prompting the use of methods like sequential assimilation which can introduce unwanted consequences, such as dependency on observation ordering. Our implementation leverages recent advancements in distributed computing to enable the construction and use of the full model error covariance matrix in distributed memory, allowing for single-batch assimilation of all observations and eliminating order dependencies. Comparative performance assessments, involving both synthetic and real-world paleoclimatic reconstruction applications, indicate that the new, non-sequential implementation outperforms the traditional, sequential one.

翻译：本文提出了一种新的集合卡尔曼滤波（Ensemble Kalman Filter, EnKF）分布式实现方法，允许在高维问题中对大型数据集进行非序列化同化。传统EnKF算法计算密集，且在需要与背景协方差矩阵交互的应用中面临困难，进而促使采用序列化同化等方法，但此类方法可能引入观测顺序依赖性等不良后果。本实现利用分布式计算的最新进展，在分布式内存中构建并应用完整模型误差协方差矩阵，从而实现所有观测的单批次同化，消除顺序依赖性。通过合成数据与真实古气候重建应用中的性能对比评估表明，新型非序列化实现方案优于传统序列化方案。