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.
|Journal||Communications in Statistics: Simulation and Computation|
|Publication status||Published - 1 Jan 2019|
- Meteorological time-series
- Non-Gaussian random process
- Non-stationary random process
- Periodically correlated random process
- Stochastic simulation