Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. / Kargapolova, Nina.
Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers. Springer-Verlag GmbH and Co. KG, 2019. стр. 117-127 (Advances in Intelligent Systems and Computing; Том 873).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 Meteorological Non-Gaussian Joint Time-Series
AU - Kargapolova, Nina
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Air temperature
KW - Daily precipitation
KW - Extreme weather event
KW - Non-Gaussian process
KW - Non-stationary random process
KW - Stochastic simulation
UR - http://www.scopus.com/inward/record.url?scp=85057433606&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01470-4_7
DO - 10.1007/978-3-030-01470-4_7
M3 - Conference contribution
AN - SCOPUS:85057433606
SN - 9783030014698
T3 - Advances in Intelligent Systems and Computing
SP - 117
EP - 127
BT - Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers
PB - Springer-Verlag GmbH and Co. KG
T2 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017
Y2 - 26 July 2017 through 28 July 2017
ER -
ID: 18070244