Time Series Analysis on Satellite Observed Carbon Dioxide Data

Abstract

Carbon dioxide is one of the most important greenhouse gas contributing to global warming [10] and the dramatic increase of carbon dioxide in recent year has been recorded. This paper mainly analyzes the carbon dioxide data from 2011 to 2017 collected by Atmospheric Infrared Sounder (AIRS) on NASA Aqua satellite. We concentrate on the area in Caribbean ocean and northeastern state of Amazonas in Brazil. The statistical models including multiple linear regression, autoregressive–moving-average models, and discrete wavelet transform are employed to study the trends and patterns in the carbon dioxide time series. This results in a partial linear model to find the time dependency, seasonal signals, and significant environmental-factor predictors

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