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 оригинала | английский |
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Название основной публикации | SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications |
Редакторы | Floriano De Rango, Tuncer Oren, Mohammad S. Obaidat |
Издатель | SciTePress |
Страницы | 173-179 |
Число страниц | 7 |
ISBN (электронное издание) | 9789897582653 |
DOI | |
Состояние | Опубликовано - 1 янв. 2017 |
Событие | 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 - Madrid, Испания Продолжительность: 26 июл. 2017 → 28 июл. 2017 |
Конференция
Конференция | 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 |
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Страна/Tерритория | Испания |
Город | Madrid |
Период | 26.07.2017 → 28.07.2017 |