Influence of topography on the fine structures of stratospheric gravity waves:An analysis using COSMIC-2 temperature data

We derive the potential energy of gravity waves(GWs)in the upper troposphere and stratosphere at 45°S-45°N from December 2019 to November 2022 by using temperature profiles retrieved from the Constellation Observing System for Meteorology,Ionosphere,and Climate-2(COSMIC-2)satellite.Owing to the dense sampling of COSMIC-2,in addition to the strong peaks of gravity wave potential energy(GWPE)above the Andes and Tibetan Plateau,we found weak peaks above the Rocky,Atlas,Caucasus,and Tianshan Mountains.The land-sea contrast is responsible for the longitudinal variations of the GWPE in the lower and upper stratosphere.At 40°N/S,the peaks were mainly above the topographic regions during the winter.At 20°N/S,the peaks were a slight distance away from the topographic regions and might be the combined effect of nontopographic GWs and mountain waves.Near the Equator,the peaks were mainly above the regions with the lowest sea level altitude and may have resulted from convection.Our results indicate that even above the local regions with lower sea level altitudes compared with the Andes and Tibetan Plateau,the GWPE also exhibits fine structures in geographic distributions.We found that dissipation layers above the tropopause jet provide the body force to generate secondary waves in the upper stratosphere,especially during the winter months of each hemisphere and at latitudes of greater than 20°N/S.

Application of deep learning to estimate stratospheric gravity wave potential energy

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.