The wind and temperature fields at 20 to 55 km above the Antigua launch site (17°N, 61°W) were analyzed by using sounding rocket data published by the research organization on Stratosphere-Troposphere Processes and their Role in Climate (SPARC). The results showed distinct variations in the wind and temperature fields at different heights from the 1960s to the 1990s. The overall zonal wind speed showed a significant increasing trend with the year, and the overall change in meridional wind speed showed a falling trend from 1976 to 1990, whereas the temperature field showed a significant cooling trend from 1964 to 1990. The times the trends mutated varied at different levels. By taking the altitudes at 20, 35, and 50 km as representatives, we found that the zonal wind speed trend had mutated in 1988, 1986, and 1986, respectively; that the meridional wind speed trend had mutated in 1990, 1986, and 1990, respectively; and that the temperature trend had mutated separately in 1977, 1973, and 1967, respectively. Characteristics of the periodic wind and temperature field variations at different heights were also analyzed, and obvious differences were found in time scale variations across the different layers. The zonal and meridional wind fields were basically characterized as having a significant periodic variation of 5 years across the three layers, and each level was characterized as having a periodic variation of less than 5 years. Temperature field variation at the three levels was basically characterized as occurring in 10-year and 5-year cycles.
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 2030 km from 60°S60°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.