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Statistical simulation and Monte Carlo method
The paper deals with research of some special characteristics of surface temperature time-series. These characteristics are related to such dangerous weather events as air temperature long-term overshoots above or below different given levels. Modeling algorithms of related processes are developed. Diurnal variation of air temperature characteristics is taken into account during numerical modeling on basis of real data. Two approaches to these processes modeling are considered. The former uses vector Markov chains as basis for temperature overshoots indicators generation. The latter is based on threshold transformation of periodically correlated Gaussian processes. Quality of models is discussed. Both models are applied for estimation of different process’s characteristics. Several properties of correlation function estimations for periodically correlated processes are introduced.
The work was supported by the Russian Foundation for Basic Research under grant 11-01-00641-à.
Note. Abstracts are published in author's edition
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