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.
Original language | English |
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Number of pages | 15 |
Journal | Methodology and Computing in Applied Probability |
DOIs | |
Publication status | Published - 21 Feb 2020 |
Keywords
- 65C05
- 65C20
- 86A10
- Climate change
- Non-stationary random process
- Stochastic simulation
- Wind chill index
- MORTALITY
- PRECIPITATION
- TEMPERATURE
- CLIMATE