Duo‐AttnOPNets: advancing global operational forecasting for atmospheric carbon monoxide with AI‐empowered 4D‐Var
Author(s)
Type
Journal Article
Abstract
The accuracy of global forecasting of atmospheric composition is essential for protecting public health and advancing climate research. Carbon monoxide (CO), a key pollutant with indirect greenhouse effects, requires timely and accurate prediction. We propose Duo-AttnOPNets, an operational framework that combines a deep learning-based forecasting module Duo-AttnForeNet with a training-free data assimilation module Duo-AttnVarNet. Duo-AttnForeNet employs CSLSTM blocks—built from ConvLSTM cells and dual attention mechanisms—to effectively capture spatio-temporal dynamics. Duo-AttnVarNet leverages automatic differentiation and GPU acceleration to enable efficient four-dimensional variational (4D-Var) assimilation without extensive manual adjoint coding. We evaluate Duo-AttnOPNets against the Integrated Forecasting System for Atmospheric Composition (C-IFS). Results show that Duo-AttnOPNets achieves comparable or superior accuracy in both forecasting and assimilation, while generating 5-day forecasts within seconds on a single GPU. These findings demonstrate its potential for real-time, scalable, and accurate CO forecasting, marking a promising advance in integrating deep learning with traditional variational methods for operational atmospheric modeling.
Date Issued
2026-02-01
Date Acceptance
2026-01-11
Citation
Journal of Advances in Modeling Earth Systems, 2026, 18 (2)
ISSN
1942-2466
Publisher
American Geophysical Union (AGU)
Journal / Book Title
Journal of Advances in Modeling Earth Systems
Volume
18
Issue
2
Copyright Statement
© 2026 The Author(s). Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Publication Status
Published
Article Number
e2025MS005319
Date Publish Online
2026-02-06