Comment on gmd-2021-420

Abstract

The applications of novel deep learning techniques in atmospheric science are rising quickly. Here we build a hybrid deep learning (DL) model (hyDL-CO), based on convolutional neural networks (CNN) 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 E. China, and −12.5 ppb and 0.96 over grids with independent observations. By contrast, the assimilated CO concentrations by KF exhibit comparable correlation coefficients but larger negative biases. Furthermore, DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 over E. China, and 81.0 ppb and 0.91 over grids with independent observations in 2019–2020, while CO observations are not fed into the DL model as an input variable. Despite these advantages, our analysis indicates 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 in respective to traditional data assimilation, which is helpful for better applications of this novel technique in future studies.