Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
Development and technical paper
 | 
22 Feb 2022
Development and technical paper |  | 22 Feb 2022

Deep-learning spatial principles from deterministic chemical transport models for chemical reanalysis: an application in China for PM2.5

Baolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu

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The Role of Chemical Boundary Conditions in Simulating Summer Ozone and Cross-Boundary Transport over China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5244,https://doi.org/10.5194/egusphere-2025-5244, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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FastCTM (v1.0): Atmospheric chemical transport modelling with a principle-informed neural network for air quality simulations
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Cited articles

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Beloconi, A., Kamarianakis, Y., and Chrysoulakis, N.: Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data, Remote Sens. Environ., 172, 148–164, https://doi.org/10.1016/j.rse.2015.10.017, 2016. 
Berrocal, V. J., Gelfand, A. E., and Holland, D. M.: Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality, Biometrics, 68, 837–848, 2012. 
Brokamp, C., Jandarov, R., Hossain, M., and Ryan, P.: Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model, Environ. Sci. Technol., 52, 4173–4179, 2018. 
Byun, D. and Schere, K. L.: Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77, 2006. 
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Data fusion is used to estimate spatially completed and smooth reanalysis fields from multiple data sources of observations and model simulations. We developed a well-designed deep-learning model framework to embed spatial correlation principles of atmospheric physics and chemical models. The deep-learning model has very high accuracy to predict reanalysis data fields from isolated observation data points. It is also feasible for operational applications due to computational efficiency.
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