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Author: Nina Kargapolova

Affiliation: Institute of Computational Mathematics and Mathematical Geophysics and Novosibirsk State University, Russian Federation

Keyword(s): Stochastic Simulation, Non-stationary Random Process, Air Temperature, Daily Precipitation, Extreme Weather Event.

Related Ontology Subjects/Areas/Topics: Complex Systems Modeling and Simulation ; Environmental Modeling ; Formal Methods ; Mathematical Simulation ; Simulation and Modeling ; Stochastic Modeling and Simulation

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).

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Paper citation in several formats:
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 Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-265-3; ISSN 2184-2841, SciTePress, pages 173-179. DOI: 10.5220/0006358801730179

@conference{simultech17,
author={Nina Kargapolova},
title={Stochastic Simulation of Non-stationary Meteorological Time-series - Daily Precipitation Indicators, Maximum and Minimum Air Temperature Simulation using Latent and Transformed Gaussian Processes},
booktitle={Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2017},
pages={173-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006358801730179},
isbn={978-989-758-265-3},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Stochastic Simulation of Non-stationary Meteorological Time-series - Daily Precipitation Indicators, Maximum and Minimum Air Temperature Simulation using Latent and Transformed Gaussian Processes
SN - 978-989-758-265-3
IS - 2184-2841
AU - Kargapolova, N.
PY - 2017
SP - 173
EP - 179
DO - 10.5220/0006358801730179
PB - SciTePress

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