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

Nina A. Kargapolova

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

Nina A. Kargapolova

Computational Mathematics Section

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

1 Citation (Scopus)

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.

Original language	English
Title of host publication	32nd Annual European Simulation and Modelling Conference 2018, ESM 2018
Editors	Veronique Limere, Dieter Claeys
Publisher	EUROSIS
Pages	199-204
Number of pages	6
ISBN (Electronic)	9789492859051
Publication status	Published - 1 Jan 2018
Event	32nd Annual European Simulation and Modelling Conference, ESM 2018 - Ghent, Belgium Duration: 24 Oct 2018 → 26 Oct 2018

Conference

Conference	32nd Annual European Simulation and Modelling Conference, ESM 2018
Country	Belgium
City	Ghent
Period	24.10.2018 → 26.10.2018

Keywords

Air temperature
Atmospheric pressure
Model validation.
Non-stationary random process
Periodically correlated process
Stochastic simulation
Time-series analysis

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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",

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note = "32nd Annual European Simulation and Modelling Conference, ESM 2018 ; Conference date: 24-10-2018 Through 26-10-2018",

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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

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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 -