In supervised data assimilation machine learning emulation, the training data contain targets produced by an existing data assimilation scheme, such as analysis increments. By contrast, data assimilation networks were recently proposed to learn the analysis operator while they are embedded in the forecast–analysis cycle: their only targets are the true trajectory and the observations thereof. They are therefore trained to produce a stable and accurate sequential estimator, rather than to reproduce the output of a prescribed data assimilation algorithm. Conceptually more fundamental, yet computationally more challenging, such learned data assimilation scheme was shown to achieve accuracy comparable to that of the ensemble Kalman filter when applied to low-order chaotic dynamics. Strikingly, the same accuracy can be reached with a single state forecast instead of an ensemble, hence bypassing the need to explicitly represent forecast uncertainty. In this study, we extend the investigation of such learned analysis operators beyond the preliminary experiments reported so far. First, we analyse the emergence of local patterns encoded in the operator, which accounts for the remarkable scalability of the approach to high-dimensional state spaces. Second, we assess the performance of the learned operators in stronger nonlinear regimes of the chaotic dynamics. We show that they can match the efficiency of the iterative ensemble Kalman filter, the baseline in this context, while avoiding the need for nonlinear iterative optimisation. Throughout the paper, we seek underlying reasons for the efficiency of the approach, drawing on insights from both machine learning and nonlinear data assimilation.
原文链接
分享
引用
