Application of deep learning to estimate stratospheric gravity wave potential energy

As one of the most important dynamic processes in the middle and upper atmosphere, gravity waves (GWs) play a key role in determining the global atmospheric circulation. Gravity wave potential energy (GW Ep) is an important parameter that characterizes GW intensity, so understanding its global distribution is necessary. In this paper, a deep learning algorithm (DeepLab V3+) is used to estimate the stratospheric GW Ep. The deep learning model inputs are ERA5 reanalysis datasets and GMTED2010 terrain data. The output is the estimated GW Ep averaged over 2030 km from 60°S60°N. The GW Ep averaged over 20~30 km calculated by COSMIC radio occultation (RO) data is used as the measured value corresponding to the model output. The results showed that (1) this method can effectively estimate the zonal trend of GW Ep. However, the errors between the estimated and measured value of Ep are larger in low-latitude regions than in mid-latitude regions. The large number of convolution operations used in the deep learning model may be the main reason. Additionally, the measured Ep has errors associated with interpolation to the grid, the error tends to be amplified in low-latitude regions because the GW Ep is larger and the RO data are relatively sparse, which affects the training accuracy. (2) The estimated Ep shows seasonal variations, which are stronger in the winter hemisphere and weaker in the summer hemisphere. (3) The effect of quasi-biennial oscillation (QBO) can be clearly observed in the monthly variation in the estimated GW Ep, and its QBO amplitude may be less than that of the measured Ep.