Abstract. The applications of novel deep learning (DL) techniques in atmospheric science are rising quickly. Here we build a hybrid DL model (hyDL-CO), based
on convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks, to provide a comparative analysis between DL and Kalman
filter (KF) to predict carbon monoxide (CO) concentrations in China in 2015–2020. We find the performance of DL model is better than KF in the
training period (2015–2018): the mean bias and correlation coefficients are 9.6 ppb and 0.98 over eastern China and are −12.5 ppb and
0.96 over grids with independent observations (i.e., grids with CO observations that are not used in DL training and KF assimilation). By
contrast, the assimilated CO concentrations by KF exhibit comparable correlation coefficients but larger negative biases. Furthermore, the DL
model demonstrates good temporal extensibility in the test period (2019–2020): the mean bias and correlation coefficients are 95.7 ppb and
0.93 over eastern China and 81.0 ppb and 0.91 over grids with independent observations, while CO observations are not fed into the DL
model as an input variable. Despite these advantages, we find a weaker prediction capability of the DL model than KF in the test period, and a
noticeable underestimation of CO concentrations at extreme pollution events in the DL model. This work demonstrates the advantages and
disadvantages of DL models to predict atmospheric compositions with respect to traditional data assimilation, which is helpful for better
applications of this novel technique in future studies.
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