Standard

Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. / Kargapolova, Nina.

SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. ed. / Floriano De Rango; Tuncer Oren; Mohammad S. Obaidat. SciTePress, 2017. p. 173-179.

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

Harvard

Kargapolova, N 2017, Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. in F De Rango, T Oren & MS Obaidat (eds), SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress, pp. 173-179, 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017, Madrid, Spain, 26.07.2017. https://doi.org/10.5220/0006358801730179

APA

Kargapolova, N. (2017). Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. In F. De Rango, T. Oren, & M. S. Obaidat (Eds.), SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 173-179). SciTePress. https://doi.org/10.5220/0006358801730179

Vancouver

Kargapolova N. Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. In De Rango F, Oren T, Obaidat MS, editors, SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress. 2017. p. 173-179 doi: 10.5220/0006358801730179

Author

Kargapolova, Nina. / Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes. SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. editor / Floriano De Rango ; Tuncer Oren ; Mohammad S. Obaidat. SciTePress, 2017. pp. 173-179

BibTeX

@inproceedings{d9151e3ef7164e56bf6f549bd74e9f99,
title = "Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes",
abstract = "In this paper a stochastic parametric simulation model that provides daily values for precipitation indicators, maximum and minimum temperature at a single site on a yearlong time-interval is presented. The model is constructed on the assumption that these weather elements are non-stationary random processes and their one-dimensional distributions vary from day to day. A latent Gaussian process and its threshold transformation are used for simulation of precipitation indicators. Parameters of the model (parameters of one-dimensional distributions, auto-and cross-correlation functions) are chosen for each location on the basis of real data from a weather station situated in this location. Several examples of model applications are given. It is shown that simulated data may be used for estimation of probability of extreme weather events occurrence (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence).",
keywords = "Air Temperature, Daily Precipitation, Extreme Weather Event, Non-stationary Random Process, Stochastic Simulation",
author = "Nina Kargapolova",
year = "2017",
month = jan,
day = "1",
doi = "10.5220/0006358801730179",
language = "English",
pages = "173--179",
editor = "{De Rango}, Floriano and Tuncer Oren and Obaidat, {Mohammad S.}",
booktitle = "SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications",
publisher = "SciTePress",
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 non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes

AU - Kargapolova, Nina

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this paper a stochastic parametric simulation model that provides daily values for precipitation indicators, maximum and minimum temperature at a single site on a yearlong time-interval is presented. The model is constructed on the assumption that these weather elements are non-stationary random processes and their one-dimensional distributions vary from day to day. A latent Gaussian process and its threshold transformation are used for simulation of precipitation indicators. Parameters of the model (parameters of one-dimensional distributions, auto-and cross-correlation functions) are chosen for each location on the basis of real data from a weather station situated in this location. Several examples of model applications are given. It is shown that simulated data may be used for estimation of probability of extreme weather events occurrence (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence).

AB - In this paper a stochastic parametric simulation model that provides daily values for precipitation indicators, maximum and minimum temperature at a single site on a yearlong time-interval is presented. The model is constructed on the assumption that these weather elements are non-stationary random processes and their one-dimensional distributions vary from day to day. A latent Gaussian process and its threshold transformation are used for simulation of precipitation indicators. Parameters of the model (parameters of one-dimensional distributions, auto-and cross-correlation functions) are chosen for each location on the basis of real data from a weather station situated in this location. Several examples of model applications are given. It is shown that simulated data may be used for estimation of probability of extreme weather events occurrence (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence).

KW - Air Temperature

KW - Daily Precipitation

KW - Extreme Weather Event

KW - Non-stationary Random Process

KW - Stochastic Simulation

UR - http://www.scopus.com/inward/record.url?scp=85029374965&partnerID=8YFLogxK

U2 - 10.5220/0006358801730179

DO - 10.5220/0006358801730179

M3 - Conference contribution

AN - SCOPUS:85029374965

SP - 173

EP - 179

BT - SIMULTECH 2017 - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

A2 - De Rango, Floriano

A2 - Oren, Tuncer

A2 - Obaidat, Mohammad S.

PB - SciTePress

T2 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017

Y2 - 26 July 2017 through 28 July 2017

ER -

ID: 9912947