Abstract Accurately simulating orography‐induced mountain waves over steep terrain, such as the Tibetan Plateau (TP), remains a major challenge for numerical weather prediction (NWP) models due to grid distortions inherent in traditional terrain‐following coordinates. To address this issue, we developed an AI‐driven adaptive mesh refinement (AMR) framework within the Fluidity‐Atmosphere model, which employs a 3D unstructured mesh to mitigate geometric distortions. A Long Short‐Term Memory (LSTM) neural network is integrated to enhance the AMR process, replacing traditional adaptation criteria with data‐driven predictions. A series of idealized 2D and 3D experiments demonstrate that both the traditional AMR and LSTM‐driven approaches reproduce mountain wave dynamics with higher efficiency than fixed mesh. Furthermore, the LSTM model suppresses numerical noise near terrain, preventing spurious over‐refinement. In realistic simulations over the TP, the LSTM‐enhanced model successfully captured the life cycle of mountain waves, reproducing key physical features such as vertical velocity structures, wave amplitude decay, and upstream phase tilt. Comparative tests further revealed efficiency gains of up to 71.4% over fixed meshes and 23.8% over traditional AMR at high resolution, alongside accuracy improvements in vertical velocity, potential temperature, and wave propagation. These findings validate the LSTM‐based AMR framework as a robust and efficient approach for atmospheric simulations over complex terrain. By intelligently allocating computational resources while preserving physical accuracy, this method offers a scalable pathway toward next‐generation atmospheric modeling, with future applications targeted at realistic meteorological conditions over the TP.
Gan et al. (Wed,) studied this question.