Articles | Volume 15, issue 10
Geosci. Model Dev., 15, 4225–4237, 2022
Geosci. Model Dev., 15, 4225–4237, 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 et al.

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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,, 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,, 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,, 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,, 2017. 
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.