High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion prediction. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3DUNet- SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES simulations of three-dimensional plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source. Keywords: Atmospheric sciences, Geosciences, Plume transport,3D temporal sequences, Artificial intelligence, CNN, LSTM, Autoencoder, Autoregressive model, U-Net, Super-resolution, Spatial Refinement.

翻译：高分辨率时空模拟能有效捕捉复杂地形中大气羽流扩散的复杂性。然而，其高昂的计算成本使其在需要快速响应或迭代过程的应用中（如优化、不确定性量化或反演建模）不切实际。为应对这一挑战，本研究提出了双阶段时域三维UNet超分辨率（DST3D-UNet-SR）模型，这是一种用于羽流扩散预测的高效深度学习模型。DST3D-UNet-SR由两个顺序模块组成：时域模块（TM）从低分辨率时域数据预测复杂地形中羽流的瞬态演化；空间细化模块（SRM）随后提升TM预测的空间分辨率。我们使用源自羽流输运高分辨率大涡模拟（LES）的综合数据集对DST3D-UNet-SR进行训练。所提出的DST3D-UNet-SR模型可将三维羽流扩散的LES模拟速度显著提升三个数量级。此外，该模型通过融合新的观测数据，展现出动态适应演变条件的能力，从而大幅提升了源附近高浓度区域的预测精度。关键词：大气科学，地球科学，羽流输运，三维时域序列，人工智能，CNN，LSTM，自编码器，自回归模型，U-Net，超分辨率，空间细化。