Accurate reconstruction of atmospheric wind fields is essential for applications such as weather forecasting, hazard prediction, and wind energy assessment, yet conventional instruments leave spatio-temporal gaps within the lower atmospheric boundary layer. Unmanned aircraft systems (UAS) provide flexible in situ measurements, but individual platforms sample wind only along their flight trajectories, limiting full wind-field recovery. This study presents a framework for reconstructing four-dimensional atmospheric wind fields using measurements obtained from a coordinated UAS swarm. A synthetic turbulence environment and high-fidelity multirotor simulation are used to generate training and evaluation data. Local wind components are estimated from UAS dynamics using a bidirectional long short-term memory network (Bi-LSTM) and assimilated into a physics-informed neural network (PINN) to reconstruct a continuous wind field in space and time. For local wind estimation, the bidirectional LSTM achieves root-mean-square errors (RMSE) of 0.064 and 0.062 m/s for the north and east components in low-wind conditions, increasing to 0.122 to 0.129 m/s under moderate winds and 0.271 to 0.273 m/s in high-wind conditions, while the vertical component exhibits higher error, with RMSE values of 0.029 to 0.091 m/s. The physics-informed reconstruction recovers the dominant spatial and temporal structure of the wind field up to 1000 m altitude while preserving mean flow direction and vertical shear. Under moderate wind conditions, the reconstructed mean wind field achieves an overall RMSE between 0.118 and 0.154 m/s across evaluated UAS configurations, with the lowest error obtained using a five-UAS swarm. These results demonstrate that coordinated UAS measurements enable accurate and scalable four-dimensional wind-field reconstruction without dedicated wind sensors or fixed infrastructure.

翻译：大气风场的精确重构对于天气预报、灾害预测和风能评估等应用至关重要，然而传统仪器在低层大气边界层内留下了时空观测空白。无人机系统提供了灵活的原位测量，但单个平台仅能沿其飞行轨迹采样风场，限制了完整风场的恢复。本研究提出了一个利用协调无人机集群测量数据重构四维大气风场的框架。采用合成湍流环境和高保真多旋翼仿真来生成训练与评估数据。首先利用双向长短期记忆网络从无人机动力学数据中估计局部风分量，随后将其同化到一个物理信息神经网络中，以重构时空连续的风场。在局部风估计方面，双向LSTM在低风速条件下对北向和东向分量的均方根误差分别为0.064和0.062 m/s；在中等风速下增至0.122至0.129 m/s；在高风速条件下达到0.271至0.273 m/s；而垂直分量误差较高，RMSE值在0.029至0.091 m/s之间。物理信息重构恢复了高达1000米高度内风场的主要时空结构，同时保持了平均流向和垂直切变。在中等风速条件下，重构的平均风场在所有评估的无人机配置中整体RMSE介于0.118至0.154 m/s之间，其中五架无人机集群配置获得了最低误差。这些结果表明，协调的无人机测量能够在无需专用风传感器或固定基础设施的情况下，实现准确且可扩展的四维风场重构。