Standard

Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity. / Kargapolova, Nina; Khlebnikova, Elena; Ogorodnikov, Vasily.

In: Statistical Papers, Vol. 59, No. 4, 01.12.2018, p. 1471-1481.

Research output: Contribution to journal › Article › peer-review

Harvard

Kargapolova, N, Khlebnikova, E & Ogorodnikov, V 2018, 'Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity', Statistical Papers, vol. 59, no. 4, pp. 1471-1481. https://doi.org/10.1007/s00362-018-1031-z

APA

Kargapolova, N., Khlebnikova, E., & Ogorodnikov, V. (2018). Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity. Statistical Papers, 59(4), 1471-1481. https://doi.org/10.1007/s00362-018-1031-z

Vancouver

Kargapolova N, Khlebnikova E, Ogorodnikov V. Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity. Statistical Papers. 2018 Dec 1;59(4):1471-1481. doi: 10.1007/s00362-018-1031-z

Author

Kargapolova, Nina ; Khlebnikova, Elena ; Ogorodnikov, Vasily. / Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity. In: Statistical Papers. 2018 ; Vol. 59, No. 4. pp. 1471-1481.

BibTeX

@article{3dfb95d53d184005a87eb2b4f19d7f80,
title = "Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity",
abstract = "In this paper a numerical stochastic model of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity is proposed. The model is based on the 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, defined by the day/night alternation. The input parameters of the model (one-dimensional distributions of air temperature and relative humidity and the correlation structure of the joint time-series) are determined from long-term real observations at weather stations. On the basis of simulated trajectories, some statistical properties of rare combinations of air temperature and relative humidity are studied. In the future, the model will be expanded by the addition of a third component, atmospheric pressure, and with a model of this three-element meteorological complex, properties of enthalpy of moist air time-series will be studied.",
keywords = "Meteorological time-series, Non-Gaussian random process, Periodically correlated random process, Stochastic simulation, STOCHASTIC WEATHER GENERATORS",
author = "Nina Kargapolova and Elena Khlebnikova and Vasily Ogorodnikov",
year = "2018",
month = dec,
day = "1",
doi = "10.1007/s00362-018-1031-z",
language = "English",
volume = "59",
pages = "1471--1481",
journal = "Statistical Papers",
issn = "0932-5026",
publisher = "Springer New York",
number = "4",

}

RIS

TY - JOUR

T1 - Monte Carlo simulation of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity

AU - Kargapolova, Nina

AU - Khlebnikova, Elena

AU - Ogorodnikov, Vasily

PY - 2018/12/1

Y1 - 2018/12/1

N2 - In this paper a numerical stochastic model of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity is proposed. The model is based on the 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, defined by the day/night alternation. The input parameters of the model (one-dimensional distributions of air temperature and relative humidity and the correlation structure of the joint time-series) are determined from long-term real observations at weather stations. On the basis of simulated trajectories, some statistical properties of rare combinations of air temperature and relative humidity are studied. In the future, the model will be expanded by the addition of a third component, atmospheric pressure, and with a model of this three-element meteorological complex, properties of enthalpy of moist air time-series will be studied.

AB - In this paper a numerical stochastic model of the joint non-Gaussian periodically correlated time-series of air temperature and relative humidity is proposed. The model is based on the 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, defined by the day/night alternation. The input parameters of the model (one-dimensional distributions of air temperature and relative humidity and the correlation structure of the joint time-series) are determined from long-term real observations at weather stations. On the basis of simulated trajectories, some statistical properties of rare combinations of air temperature and relative humidity are studied. In the future, the model will be expanded by the addition of a third component, atmospheric pressure, and with a model of this three-element meteorological complex, properties of enthalpy of moist air time-series will be studied.

KW - Meteorological time-series

KW - Non-Gaussian random process

KW - Periodically correlated random process

KW - Stochastic simulation

KW - STOCHASTIC WEATHER GENERATORS

UR - http://www.scopus.com/inward/record.url?scp=85052617686&partnerID=8YFLogxK

U2 - 10.1007/s00362-018-1031-z

DO - 10.1007/s00362-018-1031-z

M3 - Article

AN - SCOPUS:85052617686

VL - 59

SP - 1471

EP - 1481

JO - Statistical Papers

JF - Statistical Papers

SN - 0932-5026

IS - 4

ER -

ID: 16336436