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Numerical Stochastic Model of Non-stationary Time Series of the Wind Chill Index

Author

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  • Nina Kargapolova

    (Institute of Computational Mathematics and Mathematical Geophysics SB RAS
    Novosibirsk State University)

Abstract

A numerical stochastic model of the high-resolution time series of the wind chill index is considered. The model is constructed under the assumption that time series of the wind chill index are non-stationary non-Gaussian random processes with time-dependent one-dimensional distributions. This assumption makes possible to take into account both daily and seasonal variations of real meteorological processes. Data of the long-term real observations at weather stations were used for estimating the model parameters and for the verification of the model. Based on the simulated trajectories, some statistical properties of rare and adverse weather events, like long periods of time with a low wind chill index, are studied. The model is also used to study the dependence of the statistical properties of the wind chill index time series on a climate change.

Suggested Citation

  • Nina Kargapolova, 2021. "Numerical Stochastic Model of Non-stationary Time Series of the Wind Chill Index," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 257-271, March.
    • Handle: RePEc:spr:metcap:v:23:y:2021:i:1:d:10.1007_s11009-020-09778-x
    DOI: 10.1007/s11009-020-09778-x
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    References listed on IDEAS

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    1. Monbet, Valérie & Ailliot, Pierre, 2017. "Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 40-51.
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