A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport

TL;DR

This paper introduces DST3D-UNet-SR, a deep learning model that significantly accelerates 3D atmospheric plume dispersion simulations by combining temporal prediction and spatial super-resolution, enabling rapid and accurate forecasts in complex terrains.

Contribution

The paper presents a novel dual-stage deep learning framework that combines temporal modeling and spatial refinement to efficiently predict 3D plume dispersion from low-resolution data.

Findings

Accelerates LES plume simulations by three orders of magnitude.

Improves prediction accuracy near the source with dynamic data incorporation.

Demonstrates effective spatial and temporal refinement in complex terrains.

Abstract

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.