The application discloses a method for extracting middle layer top temperature multi-period characteristics and analyzing influencing factors, and belongs to the technical field of atmospheric detection and meteorological
data analysis. The method first extracts
time series data of the middle layer top temperature based on the 90km height and the lowest temperature point standard, then completes
time domain trend and
mutation analysis through seasonal departure and Mann-Kendall test, adopts discrete
wavelet decomposition to obtain four main oscillation periods of 3 years, 7 years, 11 years and 22 years, then uses
continuous wavelet to obtain the length of the fine-calibrated period, and finally performs cross
wavelet analysis on the quasi-biennial oscillation, the El Nino effect and the solar
activity index to obtain the final correlation and
time lag results. The application realizes joint time-
frequency domain analysis of
discrete wavelet transform,
continuous wavelet transform and cross
wavelet transform, effectively improves the cycle recognition accuracy, quantifies the multi-scale correlation and
time sequence hysteresis of each factor, and provides a new research idea for high-altitude atmospheric climate influencing
factor analysis.