Recovering of gaps in the time series of CO2 concentration and air temperature using methods of mathematical statistics

Abstract

The article is devoted to the problem of recovering gaps in the data series of experimental long-term continuous high-frequency observations of carbon dioxide concentration and air temperature. The study was carried out on the example of the results of observations of an automatic ecological and climatic station located in a tropical monsoon forest on the territory of south Vietnam (Dong Nai Nature Reserve). Omissions in the series of observations, as a rule, are random and are caused by technical malfunctions of the instrument base. Correctly recovered series of observations allow us to estimate the temporal variability of the observed parameters on different time scales. Within the framework of this study, options for recovering the continuity of time series based on the methods of mathematical statistics - autoregression (ARIMA) and the method of linear prediction were considered. A comparative analysis of the accuracy of restoring omissions by various methods is given.