Method for extracting multi-period characteristics of top temperature of intermediate layer and analyzing influencing factors

By employing a joint time-frequency domain analysis method, the problems of identifying multi-period nested features and quantifying the influence of atmospheric phenomena in the mesoapyr temperature sequence were solved. This enabled accurate analysis of the mesoapyr temperature and quantification of influencing factors, improving the precision and comprehensiveness of the analysis, and making it suitable for upper atmospheric physics research.

CN122151253APending Publication Date: 2026-06-05ANHUI UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ANHUI UNIV OF SCI & TECH
Filing Date
2026-01-16
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies struggle to identify the multi-period nesting features in the temperature sequence of the mesotope and cannot effectively quantify the timing and intensity of atmospheric phenomena' influence on temperature, lacking systematic analytical methods.

Method used

A joint time-frequency domain analysis method was adopted. Time-domain analysis was performed through seasonal anomalies and Mann-Kendall significance tests. Discrete wavelet and continuous wavelet transforms were combined, and Morlet cross wavelet analysis was used to analyze the correlation between mesotope temperature and atmospheric phenomena, thus constructing a complete analysis system of influencing factors.

Benefits of technology

It enables accurate identification of multi-period characteristics of mesotope temperature and quantification of influencing factors, improving the accuracy and comprehensiveness of the analysis and providing a scientific research tool for upper atmospheric physical processes.

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Abstract

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.
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