Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes

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

3 Цитирования (Scopus)

Аннотация

In this paper a stochastic parametric simulation model that provides daily values for precipitation indicators, maximum and minimum temperature at a single site on a yearlong time-interval is presented. The model is constructed on the assumption that these weather elements are non-stationary random processes and their one-dimensional distributions vary from day to day. A latent Gaussian process and its threshold transformation are used for simulation of precipitation indicators. Parameters of the model (parameters of one-dimensional distributions, auto-and cross-correlation functions) are chosen for each location on the basis of real data from a weather station situated in this location. Several examples of model applications are given. It is shown that simulated data may be used for estimation of probability of extreme weather events occurrence (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence).

Язык оригиналаанглийский
Название основной публикацииSIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
РедакторыFloriano De Rango, Tuncer Oren, Mohammad S. Obaidat
ИздательSciTePress
Страницы173-179
Число страниц7
ISBN (электронное издание)9789897582653
DOI
СостояниеОпубликовано - 1 янв 2017
Событие7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 - Madrid, Испания
Продолжительность: 26 июл 201728 июл 2017

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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