Standard

Stochastic model of the joint time-series of air temperature and atmospheric pressure. / Kargapolova, Nina A.

32nd Annual European Simulation and Modelling Conference 2018, ESM 2018. ed. / Veronique Limere; Dieter Claeys. EUROSIS, 2018. p. 199-204.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Kargapolova, NA 2018, Stochastic model of the joint time-series of air temperature and atmospheric pressure. in V Limere & D Claeys (eds), 32nd Annual European Simulation and Modelling Conference 2018, ESM 2018. EUROSIS, pp. 199-204, 32nd Annual European Simulation and Modelling Conference, ESM 2018, Ghent, Belgium, 24.10.2018.

APA

Kargapolova, N. A. (2018). Stochastic model of the joint time-series of air temperature and atmospheric pressure. In V. Limere, & D. Claeys (Eds.), 32nd Annual European Simulation and Modelling Conference 2018, ESM 2018 (pp. 199-204). EUROSIS.

Vancouver

Kargapolova NA. Stochastic model of the joint time-series of air temperature and atmospheric pressure. In Limere V, Claeys D, editors, 32nd Annual European Simulation and Modelling Conference 2018, ESM 2018. EUROSIS. 2018. p. 199-204

Author

Kargapolova, Nina A. / Stochastic model of the joint time-series of air temperature and atmospheric pressure. 32nd Annual European Simulation and Modelling Conference 2018, ESM 2018. editor / Veronique Limere ; Dieter Claeys. EUROSIS, 2018. pp. 199-204

BibTeX

@inproceedings{ed532b2bb46a452085978ad494c120e0,
title = "Stochastic model of the joint time-series of air temperature and atmospheric pressure",
abstract = "In this paper a numerical stochastic model of the joint nonstationary time-series of the air temperature and atmospheric pressure is proposed. The 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, defined by the day/night alternation. The input parameters of the model (onedimensional distributions of the air temperature and atmospheric pressure and the correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations.",
keywords = "Air temperature, Atmospheric pressure, Model validation., Non-stationary random process, Periodically correlated process, Stochastic simulation, Time-series analysis",
author = "Kargapolova, {Nina A.}",
year = "2018",
month = jan,
day = "1",
language = "English",
pages = "199--204",
editor = "Veronique Limere and Dieter Claeys",
booktitle = "32nd Annual European Simulation and Modelling Conference 2018, ESM 2018",
publisher = "EUROSIS",
note = "32nd Annual European Simulation and Modelling Conference, ESM 2018 ; Conference date: 24-10-2018 Through 26-10-2018",

}

RIS

TY - GEN

T1 - Stochastic model of the joint time-series of air temperature and atmospheric pressure

AU - Kargapolova, Nina A.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In this paper a numerical stochastic model of the joint nonstationary time-series of the air temperature and atmospheric pressure is proposed. The 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, defined by the day/night alternation. The input parameters of the model (onedimensional distributions of the air temperature and atmospheric pressure and the correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations.

AB - In this paper a numerical stochastic model of the joint nonstationary time-series of the air temperature and atmospheric pressure is proposed. The 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, defined by the day/night alternation. The input parameters of the model (onedimensional distributions of the air temperature and atmospheric pressure and the correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations.

KW - Air temperature

KW - Atmospheric pressure

KW - Model validation.

KW - Non-stationary random process

KW - Periodically correlated process

KW - Stochastic simulation

KW - Time-series analysis

UR - http://www.scopus.com/inward/record.url?scp=85058393764&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85058393764

SP - 199

EP - 204

BT - 32nd Annual European Simulation and Modelling Conference 2018, ESM 2018

A2 - Limere, Veronique

A2 - Claeys, Dieter

PB - EUROSIS

T2 - 32nd Annual European Simulation and Modelling Conference, ESM 2018

Y2 - 24 October 2018 through 26 October 2018

ER -

ID: 19262040