Accurate estimation of the background error covariance matrix denoted as B remains a critical challenge in numerical weather prediction(NWP),directly influencing data assimilation(DA)performance and forecast accuracy....Accurate estimation of the background error covariance matrix denoted as B remains a critical challenge in numerical weather prediction(NWP),directly influencing data assimilation(DA)performance and forecast accuracy.Although hybrid ensemble–variational(En Var)methods combine static and flow-dependent matrices to improve assimilation,their effectiveness is constrained by empirically fixed weights.To address this limitation,we propose DRL-En Var,an adaptive hybrid En Var DA method enhanced with deep reinforcement learning.DRL-En Var integrates deep learning(DL)components,including a novel cyclic convolution module to extract abstract features from data,and employs reinforcement learning(RL)to dynamically optimize hybrid weighting strategies.The system adaptively combines multiple ensemble-based flow-dependent matrices with one or more static matrices to construct a time-varying hybrid matrix B that better reflects real-time background errors.Experimental results demonstrate that DRL-En Var performs better than the traditional ensemble Kalman filter(En KF)and hybrid covariance DA(HCDA)methods,especially under sparse observations or transitional changes in state variables.It achieves competitive or superior assimilation accuracy with lower computational cost,and can be flexibly integrated into both three-dimensional variational assimilation(3DVar)and four-dimensional variational assimilation(4DVar)frameworks.Overall,DRL-En Var offers a novel and efficient approach to adaptive DA,particularly valuable for improving forecast skill during transitional weather regimes.展开更多
基金Project supported by the National Key R&D Program of China(No.2022YFB3207304)the National Natural Science Foundation of China(No.42205161)the Natural Science Foundation of Hunan Province,China(No.2023JJ30630)。
文摘Accurate estimation of the background error covariance matrix denoted as B remains a critical challenge in numerical weather prediction(NWP),directly influencing data assimilation(DA)performance and forecast accuracy.Although hybrid ensemble–variational(En Var)methods combine static and flow-dependent matrices to improve assimilation,their effectiveness is constrained by empirically fixed weights.To address this limitation,we propose DRL-En Var,an adaptive hybrid En Var DA method enhanced with deep reinforcement learning.DRL-En Var integrates deep learning(DL)components,including a novel cyclic convolution module to extract abstract features from data,and employs reinforcement learning(RL)to dynamically optimize hybrid weighting strategies.The system adaptively combines multiple ensemble-based flow-dependent matrices with one or more static matrices to construct a time-varying hybrid matrix B that better reflects real-time background errors.Experimental results demonstrate that DRL-En Var performs better than the traditional ensemble Kalman filter(En KF)and hybrid covariance DA(HCDA)methods,especially under sparse observations or transitional changes in state variables.It achieves competitive or superior assimilation accuracy with lower computational cost,and can be flexibly integrated into both three-dimensional variational assimilation(3DVar)and four-dimensional variational assimilation(4DVar)frameworks.Overall,DRL-En Var offers a novel and efficient approach to adaptive DA,particularly valuable for improving forecast skill during transitional weather regimes.
