Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикацииSimulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers
ИздательSpringer-Verlag GmbH and Co. KG
Страницы117-127
Число страниц11
ISBN (печатное издание)9783030014698
DOI
СостояниеОпубликовано - 1 янв 2019
Событие7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 - Madrid, Испания
Продолжительность: 26 июл 201728 июл 2017

Серия публикаций

НазваниеAdvances in Intelligent Systems and Computing
Том873
ISSN (печатное издание)2194-5357

Конференция

Конференция7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017
СтранаИспания
ГородMadrid
Период26.07.201728.07.2017

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