Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series

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Abstract

A numerical stochastic model of joint non-stationary non-Gaussian time-series of daily precipitation, daily minimum and maximum air temperature is proposed in this paper. The model is constructed on the assumption that these weather elements are non-stationary non-Gaussian random processes with time-dependent one-dimensional distributions. This assumption takes into account the diurnal and seasonal variation of real meteorological processes. The input parameters of the model (one-dimensional distributions and correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations. On the basis of simulated trajectories, some statistical properties of rare and extreme weather events (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence) were studied.

Original languageEnglish
Title of host publicationSimulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers
PublisherSpringer-Verlag GmbH and Co. KG
Pages117-127
Number of pages11
ISBN (Print)9783030014698
DOIs
Publication statusPublished - 1 Jan 2019
Event7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 - Madrid, Spain
Duration: 26 Jul 201728 Jul 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume873
ISSN (Print)2194-5357

Conference

Conference7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017
CountrySpain
CityMadrid
Period26.07.201728.07.2017

Keywords

  • Air temperature
  • Daily precipitation
  • Extreme weather event
  • Non-Gaussian process
  • Non-stationary random process
  • Stochastic simulation

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