Stochastic models of joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure

Abstract

In this paper two numerical stochastic models of the joint non-stationary time-series of air temperature, relative humidity and atmospheric pressure are proposed. The first 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, determined by the day/night alternation. Within the framework of the second model, real weather processes are considered as non-stationary random processes. The input parameters of both models (one-dimensional distributions and correlation structure of the joint time-series) are determined from the data of long-term real observations at weather stations. The results of the models verification are presented.

Keywords:

• Stochastic simulation
• Periodically correlated random process
• Non-stationary random process
• Non-Gaussian random process
• Meteorological time-series

Additional information

Funding

This work was partly financially supported by the Russian Foundation for Basic Research (grant No 18-01-00149-a), Russian Scientific Foundation (grant No 16-17-00063) and the President of the Russian Federation (grant No MK-659.2017.1).