Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4225-2022
https://doi.org/10.5194/gmd-15-4225-2022
Model evaluation paper
 | 
01 Jun 2022
Model evaluation paper |  | 01 Jun 2022

A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China

Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang

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Cited articles

Chen, X., Jiang, Z., Shen, Y., Li, R., Fu, Y., Liu, J., Han, H., Liao, H., Cheng, X., Jones, D. B. A., Worden, H., and Abad, G. G.: Chinese regulations are working – why is surface ozone over industrialized areas still high? Applying lessons from Northeast US air quality evolution, Geophys. Res. Lett., 48, e2021GL092816, https://doi.org/10.1029/2021GL092816, 2021. 
Chen, Y., Cui, S., Chen, P., Yuan, Q., Kang, P., and Zhu, L.: An LSTM-based neural network method of particulate pollution forecast in China, Environ. Res. Lett., 16, 044006, https://doi.org/10.1088/1748-9326/abe1f5, 2021. 
Feng, S., Jiang, F., Wu, Z., Wang, H., Ju, W., and Wang, H.: CO Emissions Inferred From Surface CO Observations Over China in December 2013 and 2017, J. Geophys. Res.-Atmos., 125, 2019JD031808, https://doi.org/10.1029/2019jd031808, 2020. 
Fisher, J. A., Murray, L. T., Jones, D. B. A., and Deutscher, N. M.: Improved method for linear carbon monoxide simulation and source attribution in atmospheric chemistry models illustrated using GEOS-Chem v9, Geosci. Model Dev., 10, 4129–4144, https://doi.org/10.5194/gmd-10-4129-2017, 2017. 
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Short summary
We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.