Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Stochastic models of joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure. / Kargapolova, N. A.; Khlebnikova, E. I.; Ogorodnikov, V. A.
в: Communications in Statistics: Simulation and Computation, Том 50, № 12, 2021, стр. 3972-3983.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
}
TY - JOUR
T1 - Stochastic models of joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure
AU - Kargapolova, N. A.
AU - Khlebnikova, E. I.
AU - Ogorodnikov, V. A.
N1 - Publisher Copyright: © 2019 Taylor & Francis Group, LLC.
PY - 2021
Y1 - 2021
N2 - In this paper two numerical stochastic models of the joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure are proposed. The first model is based on an assumption that real weather processes are periodically correlated random processes with a period equal to 1 day. This assumption takes into account the diurnal variation of real meteorological processes, determined by the day/night alternation. Within the framework of the second model, real weather processes are considered as non-stationary random processes. The input parameters of both models (one-dimensional distributions and correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations. The results of the models verification are presented.
AB - In this paper two numerical stochastic models of the joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure are proposed. The first model is based on an assumption that real weather processes are periodically correlated random processes with a period equal to 1 day. This assumption takes into account the diurnal variation of real meteorological processes, determined by the day/night alternation. Within the framework of the second model, real weather processes are considered as non-stationary random processes. The input parameters of both models (one-dimensional distributions and correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations. The results of the models verification are presented.
KW - Meteorological time-series
KW - Non-Gaussian random process
KW - Non-stationary random process
KW - Periodically correlated random process
KW - Stochastic simulation
UR - http://www.scopus.com/inward/record.url?scp=85068188236&partnerID=8YFLogxK
U2 - 10.1080/03610918.2019.1635157
DO - 10.1080/03610918.2019.1635157
M3 - Article
AN - SCOPUS:85068188236
VL - 50
SP - 3972
EP - 3983
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
SN - 0361-0918
IS - 12
ER -
ID: 20711199