Monte carlo simulation of non-stationary air temperature time-series

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4 Citations (Scopus)

Abstract

Two numerical stochastic models of air temperature time-series are considered in this paper. The first model is constructed under the assumption that time-series are nonstationary. In the second model air temperature time-series are considered as a periodically correlated random processes. Data from real observations on weather stations was used for estimation of models' parameters. On the basis of simulated trajectories, some statistical properties of rare meteorological events, like sharp temperature drops or long-term temperature decreases in summer, are studied.

Original languageEnglish
Title of host publicationSIMULTECH 2018 - Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
EditorsFloriano De Rango, Tuncer Oren, Mohammad S. Obaidat, Mohammad S. Obaidat
PublisherSciTePress
Pages323-329
Number of pages7
ISBN (Electronic)9789897583230
DOIs
Publication statusPublished - 1 Jan 2018
Event8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018 - Porto, Portugal
Duration: 29 Jul 201831 Jul 2018

Publication series

NameSIMULTECH 2018 - Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Conference

Conference8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2018
CountryPortugal
CityPorto
Period29.07.201831.07.2018

Keywords

  • Air temperature
  • Model validation
  • Non-stationary random process
  • Periodically correlated process
  • Stochastic simulation
  • Temperature extremes

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