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地球与行星物理

ISSN  2096-3955

CN  10-1502/P

Citation: Wu, Y., Sheng, Z., and Zuo, X. J. (2022). Application of deep learning to estimate stratospheric gravity wave potential energy. Earth Planet. Phys., 6(1), 1–13. http://doi.org/10.26464/epp2022002

doi: 10.26464/epp2022002

ATMOSPHERIC PHYSICS

Application of deep learning to estimate stratospheric gravity wave potential energy

1. 

College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China

2. 

Northwest Institute of Nuclear Technology, Xi’an 710024, China

3. 

High-tech Institute, Fan Gong-ting South Street on the 12th, Qingzhou 262500, China

Corresponding author: Zheng Sheng, 19994035@sina.com

Received Date: 2021-07-23
Web Publishing Date: 2021-11-04

One of the most important dynamic processes in the middle and upper atmosphere, gravity waves (GWs) play a key role in determining global atmospheric circulation. Gravity wave potential energy (GW Ep) is an important parameter that characterizes GW intensity, so it is critical to understand its global distribution. 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. GW Ep averaged over 20−30 km from 60°S−60°N, calculated by COSMIC radio occultation (RO) data, is used as the measured value corresponding to the model output. The results show 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, possibly due to the large number of convolution operations used in the deep learning model. Additionally, the measured Ep has errors associated with interpolation to the grid; this tends to be amplified in low-latitude regions because the GW Ep is larger and the RO data are relatively sparse, affecting 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 of estimated GW Ep, and its QBO amplitude may be less than that of the measured Ep.

Key words: deep learning; stratospheric gravity wave; potential energy

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Application of deep learning to estimate stratospheric gravity wave potential energy

Yue Wu, Zheng Sheng, XinJie Zuo