A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport
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 quant...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-06-01
|
| Series: | Artificial Intelligence in Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000164 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849433436917334016 |
|---|---|
| author | M. Giselle Fernández-Godino Wai Tong Chung Akshay A. Gowardhan Matthias Ihme Qingkai Kong Donald D. Lucas Stephen C. Myers |
| author_facet | M. Giselle Fernández-Godino Wai Tong Chung Akshay A. Gowardhan Matthias Ihme Qingkai Kong Donald D. Lucas Stephen C. Myers |
| author_sort | M. Giselle Fernández-Godino |
| collection | DOAJ |
| description | 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 predictions. 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 DST3D-UNet-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 of three-dimensional (3D) 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. |
| format | Article |
| id | doaj-art-8ca6065bb3f648e8b5f720e6a7e28f43 |
| institution | Kabale University |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-8ca6065bb3f648e8b5f720e6a7e28f432025-08-20T03:27:02ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110012010.1016/j.aiig.2025.100120A staged deep learning approach to spatial refinement in 3D temporal atmospheric transportM. Giselle Fernández-Godino0Wai Tong Chung1Akshay A. Gowardhan2Matthias Ihme3Qingkai Kong4Donald D. Lucas5Stephen C. Myers6Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States of America; Corresponding author.Stanford University, 440 Escondido Hall, Stanford, CA 94305, United States of AmericaLawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States of AmericaStanford University, 440 Escondido Hall, Stanford, CA 94305, United States of AmericaLawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States of AmericaLawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States of AmericaLawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA 94550, United States of AmericaHigh-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 predictions. 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 DST3D-UNet-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 of three-dimensional (3D) 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.http://www.sciencedirect.com/science/article/pii/S2666544125000164Atmospheric sciencesGeosciencesPlume transport3D temporal sequencesArtificial intelligenceCNN |
| spellingShingle | M. Giselle Fernández-Godino Wai Tong Chung Akshay A. Gowardhan Matthias Ihme Qingkai Kong Donald D. Lucas Stephen C. Myers A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport Artificial Intelligence in Geosciences Atmospheric sciences Geosciences Plume transport 3D temporal sequences Artificial intelligence CNN |
| title | A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport |
| title_full | A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport |
| title_fullStr | A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport |
| title_full_unstemmed | A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport |
| title_short | A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport |
| title_sort | staged deep learning approach to spatial refinement in 3d temporal atmospheric transport |
| topic | Atmospheric sciences Geosciences Plume transport 3D temporal sequences Artificial intelligence CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000164 |
| work_keys_str_mv | AT mgisellefernandezgodino astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT waitongchung astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT akshayagowardhan astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT matthiasihme astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT qingkaikong astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT donalddlucas astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT stephencmyers astageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT mgisellefernandezgodino stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT waitongchung stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT akshayagowardhan stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT matthiasihme stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT qingkaikong stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT donalddlucas stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport AT stephencmyers stageddeeplearningapproachtospatialrefinementin3dtemporalatmospherictransport |