Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. / Kargapolova, Nina.
SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. ред. / Floriano De Rango; Tuncer Oren; Mohammad S. Obaidat. SciTePress, 2017. стр. 173-179.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes
AU - Kargapolova, Nina
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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).
AB - 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).
KW - Air Temperature
KW - Daily Precipitation
KW - Extreme Weather Event
KW - Non-stationary Random Process
KW - Stochastic Simulation
UR - http://www.scopus.com/inward/record.url?scp=85029374965&partnerID=8YFLogxK
U2 - 10.5220/0006358801730179
DO - 10.5220/0006358801730179
M3 - Conference contribution
AN - SCOPUS:85029374965
SP - 173
EP - 179
BT - SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
A2 - De Rango, Floriano
A2 - Oren, Tuncer
A2 - Obaidat, Mohammad S.
PB - SciTePress
T2 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017
Y2 - 26 July 2017 through 28 July 2017
ER -
ID: 9912947