EGU24-7133, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-7133
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains

Weichao Han1, Tai-Long He2, Zhe Jiang1, Min Wang1, Dylan Jones3, Kazuyuki Miyazaki4, and Yanan Shen1
Weichao Han et al.
  • 1School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • 2Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA.
  • 3Department of Physics, University of Toronto, Toronto, ON, M5S 1A7, Canada.
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.

Machine learning (ML) techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. However, whether ML predictions can be extrapolated to different domains with significant spatial and temporal discrepancies is still unclear. Here we explore the answer to this question by presenting a comparative analysis of surface carbon monoxide (CO) and ozone (O3) predictions by integrating deep learning (DL) and chemical transport model (CTM) methods. The DL model trained with surface CO observations in China in 2015-2018 exhibited good spatial and temporal extrapolation capabilities, i.e., good surface daily CO predictions in China in 2019-2020 and over 10% independent observation stations in China in 2015-2020. The spatial and temporal extrapolation capabilities of DL model are further evaluated by predicting hourly surface O3 concentrations in China, the United States (US) and Europe in 2015-2022 with a DL model trained with surface O3 observations in China and the US in 2015-2018. Compared to baseline O3 simulations using GEOS-Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 µg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 µg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015-2018 and 2019-2022, respectively. This analysis indicates the potential of DL to make reliable atmospheric composition predictions over spatial and temporal domains where a wealth of local observations for training is not available.

How to cite: Han, W., He, T.-L., Jiang, Z., Wang, M., Jones, D., Miyazaki, K., and Shen, Y.: The capability of deep learning model to predict atmospheric compositions across spatial and temporal domains, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7133, https://doi.org/10.5194/egusphere-egu24-7133, 2024.

Corresponding supplementary materials formerly uploaded have been withdrawn.