A Mamba-Based Hierarchical Partitioning Framework for Upper-Level Wind Field Reconstruction

An accurate perception of upper-level wind fields is essential for improving civil aviation safety and route optimization. However, the sparsity of observational data and the structural complexity of wind fields make reconstruction highly challenging. To address this, we propose QuadMamba-WindNet (QMW-Net), a structure-enhanced deep neural network that integrates a hierarchical state-space modeling framework with a learnable quad-tree-based regional partitioning mechanism, enabling multi-scale adaptive encoding and efficient dynamic modeling. The model is trained end-to-end on ERA5 reanalysis data and validated with simulated flight trajectory observation masks, allowing the reconstruction of complete horizontal wind fields at target altitude levels. Experimental results show that QMW-Net achieves a mean absolute error (MAE) of 1.62 m/s and a mean relative error (MRE) of 6.68% for wind speed reconstruction at 300 hPa, with a mean directional error of 4.85° and an R2 of 0.93, demonstrating high accuracy and stable error convergence. Compared with Physics-Informed Neural Networks (PINNs) and Gaussian Process Regression (GPR), QMW-Net delivers superior predictive performance and generalization across multiple test sets. The proposed model provides refined wind field support for civil aviation forecasting and trajectory planning, and shows potential for broader applications in high-dynamic flight environments and atmospheric sensing.