全窗口分支发现与损失选择的EnKF延续用于数据同化
Full-window branch discovery and loss-selected EnKF continuation for data assimilation
- University of California, Irvine（加州大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
我们提出一种结合全窗口分支发现与损失选择EnKF延续的数据同化框架，通过APK微分、优化初始分布和损失加权混合实现高效分支搜索，在Lorenz-96实验中显著降低离线与在线RMSE。
AI中文摘要：
我们开发了一个用于离线全窗口分支发现的框架，并可选地随后使用集合卡尔曼滤波器（EnKF）进行在线延续。分支搜索由三种机制驱动：伴随路径核（APK）微分平衡了核微分与校正稳定的路径扰动，将优化从探索转向利用；优化的高斯初始分布拓宽了对初始状态盆地的搜索；以及跨独立运行的损失加权混合重组了成功的路径组件。然后，我们可以使用局部损失选择一个内部状态，并使用EnKF在线延续。在40维Lorenz-96实验中，APK的平均离线路径RMSE比群体弱4D-Var_x小4.3倍。由此产生的APK-EnKF方法的平均在线RMSE比普通EnKF小64倍。
英文摘要：
We develop a framework for offline full-window branch discovery, optionally followed by online continuation with an ensemble Kalman filter (EnKF). Three mechanisms drive the branch search: adjoint path-kernel (APK) differentiation balances kernel differentiation and correction-stabilized path perturbation, shifting the optimization from exploration to exploitation; an optimized Gaussian initial law broadens the search over initial-state basins; and loss-weighted mixing across independent runs recombines successful path components. We may then select an interior state using a local loss and continue online with an EnKF. In 40-dimensional Lorenz-96 experiments, the mean offline path RMSE of APK is 4.3 times smaller than that of population weak-$\mathrm{4D\text{-}Var}_x$. The resulting APK-EnKF method has a mean online RMSE 64 times smaller than that of ordinary EnKF.
