Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets

Abstract

Gravity waves play an essential role in driving and maintaining global circulation. To understand their contribution in the atmosphere, the accurate reproduction of their distribution is important. Thus, a deep learning approach for the estimation of gravity wave momentum fluxes was proposed, and its performance at 100 hPa was tested using data from low-resolution zonal and meridional winds, temperature, and specific humidity at 300, 700, and 850 hPa in the Hokkaido region (Japan). To this end, a deep convolutional neural network was trained on 29-year reanalysis data sets (JRA-55 and DSJRA-55), and the final 5-year data were reserved for evaluation. The results showed that the fine-scale momentum flux distribution of the gravity waves could be estimated at a reasonable computational cost. Particularly, in winter, when gravity waves are stronger, the median root means square errors (RMSEs) of the maximum momentum flux and the characteristic zonal wavenumber were 0.06–0.13 mPa and 1.0 × 10−5, respectively.