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.
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Acknowledgements
This work was supported by the Russian Foundation for Basis Research (grant No 18-01-00149-a), the President of the Russian Federation (grant No MK-659.2017.1).
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Kargapolova, N. (2019). Stochastic Simulation of Meteorological Non-Gaussian Joint Time-Series. In: Obaidat, M., Ören, T., Rango, F. (eds) Simulation and Modeling Methodologies, Technologies and Applications . SIMULTECH 2017. Advances in Intelligent Systems and Computing, vol 873. Springer, Cham. https://doi.org/10.1007/978-3-030-01470-4_7
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DOI: https://doi.org/10.1007/978-3-030-01470-4_7
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