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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. p. 117-127 (Advances in Intelligent Systems and Computing; Vol. 873).

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

Harvard

Kargapolova, N 2019, Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. in Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers. Advances in Intelligent Systems and Computing, vol. 873, Springer-Verlag GmbH and Co. KG, pp. 117-127, 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017, Madrid, Spain, 26.07.2017. https://doi.org/10.1007/978-3-030-01470-4_7

APA

Kargapolova, N. (2019). Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. In Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers (pp. 117-127). (Advances in Intelligent Systems and Computing; Vol. 873). Springer-Verlag GmbH and Co. KG. https://doi.org/10.1007/978-3-030-01470-4_7

Vancouver

Kargapolova N. Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. In Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers. Springer-Verlag GmbH and Co. KG. 2019. p. 117-127. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-01470-4_7

Author

Kargapolova, Nina. / Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers. Springer-Verlag GmbH and Co. KG, 2019. pp. 117-127 (Advances in Intelligent Systems and Computing).

BibTeX

@inproceedings{2a567d895f8544a6ab44a246f576b478,
title = "Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series",
abstract = "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.",
keywords = "Air temperature, Daily precipitation, Extreme weather event, Non-Gaussian process, Non-stationary random process, Stochastic simulation",
author = "Nina Kargapolova",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-01470-4_7",
language = "English",
isbn = "9783030014698",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "117--127",
booktitle = "Simulation and Modeling Methodologies, Technologies and Applications - 7th International Conference, SIMULTECH 2017, Revised Selected Papers",
address = "Germany",
note = "7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017 ; Conference date: 26-07-2017 Through 28-07-2017",

}

RIS

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