Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 47, Issue 19, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2020GL089436
Keywords
gravity wave; machine learning; deep learning; convolutional neural network
Categories
Funding
- Japan Science and Technology Agency (JST), PRESTO [JPMJPR1777]
- JST, CREST [JPMJCR1663]
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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 and1.0 x 10(-5), respectively.
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