An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model
Accurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (<b>B</b>), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6399 |
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| author | Lilan Huang Hongze Leng Junqiang Song Dongzi Wang Wuxin Wang Ruisheng Hu Hang Cao |
| author_facet | Lilan Huang Hongze Leng Junqiang Song Dongzi Wang Wuxin Wang Ruisheng Hu Hang Cao |
| author_sort | Lilan Huang |
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| description | Accurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (<b>B</b>), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This study introduces DRL-AST, a deep reinforcement learning-based adaptive variance rescaling strategy that dynamically adjusts the variances of <b>B</b> to optimize forecast skill through improved assimilation performance. By formulating variance rescaling as a Markov Decision Process and employing an actor–critic framework with Proximal Policy Optimization, DRL-AST autonomously selects spatio-temporal rescaling factors, enhancing assimilation and forecast accuracy without additional computational cost. As a new paradigm for adaptive variance tuning, DRL-AST demonstrates competitive improvements in forecast skill in experiments with the Lorenz-96 model by generating initial states that better conform to model dynamical consistency. Given its adaptability and efficiency, DRL-AST holds great potential for application in high-dimensional NWP models, where deep learning-based dimensionality reduction and reinforcement learning techniques could further enhance its feasibility and effectiveness in complex assimilation frameworks. |
| format | Article |
| id | doaj-art-43877a3d4a2f426d8b621e71e085c480 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-43877a3d4a2f426d8b621e71e085c4802025-08-20T03:26:53ZengMDPI AGApplied Sciences2076-34172025-06-011512639910.3390/app15126399An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 ModelLilan Huang0Hongze Leng1Junqiang Song2Dongzi Wang3Wuxin Wang4Ruisheng Hu5Hang Cao6College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Computer Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaAccurate initial conditions are crucial for improving numerical weather prediction (NWP). Variational data assimilation relies on a static background error covariance matrix (<b>B</b>), yet its variance estimation is often inaccurate, affecting assimilation and forecast performance. This study introduces DRL-AST, a deep reinforcement learning-based adaptive variance rescaling strategy that dynamically adjusts the variances of <b>B</b> to optimize forecast skill through improved assimilation performance. By formulating variance rescaling as a Markov Decision Process and employing an actor–critic framework with Proximal Policy Optimization, DRL-AST autonomously selects spatio-temporal rescaling factors, enhancing assimilation and forecast accuracy without additional computational cost. As a new paradigm for adaptive variance tuning, DRL-AST demonstrates competitive improvements in forecast skill in experiments with the Lorenz-96 model by generating initial states that better conform to model dynamical consistency. Given its adaptability and efficiency, DRL-AST holds great potential for application in high-dimensional NWP models, where deep learning-based dimensionality reduction and reinforcement learning techniques could further enhance its feasibility and effectiveness in complex assimilation frameworks.https://www.mdpi.com/2076-3417/15/12/6399variational data assimilationspatio-temporal optimizationdeep reinforcement learningadaptive variance rescalingnumerical weather prediction |
| spellingShingle | Lilan Huang Hongze Leng Junqiang Song Dongzi Wang Wuxin Wang Ruisheng Hu Hang Cao An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model Applied Sciences variational data assimilation spatio-temporal optimization deep reinforcement learning adaptive variance rescaling numerical weather prediction |
| title | An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model |
| title_full | An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model |
| title_fullStr | An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model |
| title_full_unstemmed | An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model |
| title_short | An Adaptive Variance Adjustment Strategy for a Static Background Error Covariance Matrix—Part I: Verification in the Lorenz-96 Model |
| title_sort | adaptive variance adjustment strategy for a static background error covariance matrix part i verification in the lorenz 96 model |
| topic | variational data assimilation spatio-temporal optimization deep reinforcement learning adaptive variance rescaling numerical weather prediction |
| url | https://www.mdpi.com/2076-3417/15/12/6399 |
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