On the question of analysis of weather data with an ordered argument in the aspect of solving technosphere security problems
Authors:   Voronezh State University of Engineering Technologies, Kalach Andrey Vladimirovich , Paramonov Alexander Alexandrovich ,   MIREA – Russian Technological University, Krynetsky Boris Alekseevich ,   MIREA – Russian Technological University
Published: November 2024

Introduction. The impact of climate change on meteorological conditions may accompanied by a complex of negative manifestations in the human environment, including sharp temperature fluctuations and changes in atmospheric pressure, which inevitably leads to an increase in the frequency of extreme weather events (floods, drought, and tropical cyclones). It known that approximately 75 % of the planet's population adapts quickly and easily to weather changes. World statistics show that the consequences of climate change threaten the main resources of humanity and dictate the need to develop new and improve existing models and algorithms for analyzing meteorological data in order to solve pressing problems of ensuring the safety of human life in the technosphere.
The aim the study is to improve the forecasting model of atmospheric pressure dynamics by identifying near-periods of oscillations using big data analysis methods with ordered argument.
Research methods. Functional analysis, probability theory, system theory and system analysis.
Results and their discussion. As an object for testing, the implemented big data analysis algorithms, based on the methods described above, we will consider the data of the Federal State Budgetary Institution "Hydrometeorological Center of Russia" on atmospheric pressure from February 2005 to February 2024. The main requirement for computer processing of data with an ordered argument is the absence of influence of processing methods on the measurement results. Classical methods for analyzing periodic components of nonlinear oscillations, based on series expansions, significantly influence the results obtained, since they assume that the original data represented only by series components, whereas autocorrelation functions do not work on large time intervals. A model for predicting the dynamics of atmospheric pressure developed based on computer processing of chronological meteorological data with an ordered argument.
Conclusion. The results obtained when applying the method of separating fast and slow movements of empirical data, with further analysis of the obtained oscillations for the presence of almost-periodic components, showed a complete set of almost-periodic values in the data under study. Big data analysis methods with an ordered argument used to identify and determine almost-periods of atmospheric pressure fluctuations over a 5-year observation period. The proposed method based on distances in metric spaces of functional analysis, the class of shift functions, in principle, does not affect the original measurement results, which makes it possible to determine a structurally complete set of almost-periods contained in the measurement results. The identified almost period of change in atmospheric pressure is a multiple of three lunar cycles.
Keywords: big data analysis methods; big data with ordered argument; trend; nonlinear fluctuations; almost-period.