Time series simulation method of meteorological elements based on ARIMA model

With the development of science and technology and economy, environmental monitoring technology and means are constantly enriched and improved, and the information of meteorological big data is gradually diversified, which brings convenience to the study of meteorological trend that thus provides corresponding guidance for industrial and agricultural production. In recent years, the construction of environmental monitoring stations has been accelerated, and they are popular in the study of meteorological trends in various countries because of their real-time characteristics and wide range of data. In this paper, the characteristics of key meteorological elements at different time scales are statistically analyzed firstly based on the measured meteorological data of regional meteorological stations that meteorological elements have significant tendency and seasonality. Secondly, the obtained meteorological big data is pre-processed to eliminate bad data, and then the probability distribution of meteorological elements and their change rules is extracted. Finally, a time series method based on ARIMA model is proposed to simulate the rule of weather change, so as to obtain the time series of temperature and rainfall in the next ten years, which can provide theoretical and data support for various industries, and help meteorological and security departments to make work plans in time and correctly.