Time Series Analysis and Forecasting Using a Novel Hybrid LSTM Data-Driven Model Based on Empirical Wavelet Transform Applied to Total Column of Ozone at Buenos Aires, Argentina (1966–2017)

Hassan Bencherif,Nkanyiso Mbatha

doi:10.3390/atmos11050457

Abstract

Total column of ozone (TCO) time series analysis and accurate forecasting is of great significance in monitoring the status of the Chapman Mechanism in the stratosphere, which prevents harmful UV radiation from reaching the Earth’s surface. In this study, we performed a detailed time series analysis of the TCO data measured in Buenos Aires, Argentina. Moreover, hybrid data-driven forecasting models, based on long short-term memory networks (LSTM) recurrent neural networks (RNNs), are developed. We extracted the updated trend of the TCO time series by utilizing the singular spectrum analysis (SSA), empirical wavelet transform (EWT), empirical mode decomposition (EMD), and Mann-Kendall. In general, the TCO has been stable since the mid-1990s. The trend analysis shows that there is a recovery of ozone during the period from 2010 to 2017, apart from the decline of ozone observed during 2015, which is presumably associated with the Calbuco volcanic event. The EWT trend method seems to have effective power for trend identification, compared with others. In this study, we developed a robust data-driven hybrid time series-forecasting model (named EWT-LSTM) for the TCO time series forecasting. Our model has the advantage of utilizing the EWT technique in the decomposition stage of the LSTM process. We compared our model with (1) an LSTM model that uses EMD, namely EMD-LSTM; (2) an LSTM model that uses wavelet denoising (WD) (WD-LSTM); (3) a wavelet denoising EWT-LSTM (WD-EWT-LSTM); and (4) a wavelet denoising noise-reducing sequence called EMD-LSTM (WD-EMD-LSTM). The model that uses the EWT decomposition process (EWT-LSTM) outperformed the other five models developed here in terms of various forecasting performance evaluation criteria, such as the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R).

Highlights

Total column of ozone (TCO), defined as the vertical integration of ozone number density profile in the lower and middle atmosphere, is important for regulating the amount of harmful ultraviolet (UV) radiation reaching the surface of the Earth
The decline of global stratospheric ozone column observed throughout the 1980s [1] and the discovery of the Southern Hemisphere polar region ozone hole [2,3] raised the awareness of the need to protect the global ozone layer
Our study proposes the use of a newely developed method (EWT) that is used to decompose any signal into a collection of amplitude modulated-frequency modulated (AM–FM) signals according to the information contained within the Fourier spectrum of the given signal

Summary

Introduction

Total column of ozone (TCO), defined as the vertical integration of ozone number density profile in the lower and middle atmosphere, is important for regulating the amount of harmful ultraviolet (UV) radiation reaching the surface of the Earth. The decline of global stratospheric ozone column observed throughout the 1980s [1] and the discovery of the Southern Hemisphere polar region ozone hole [2,3] raised the awareness of the need to protect the global ozone layer. A number of atmospheric chemistry research groups have been closely monitoring the slow self-recovery of stratospheric ozone for some time. The difficulty of this slow recovery is that the entire process depends solely on the self-recovery aspects of the ozone layer. The recovery of the stratospheric ozone layer is strongly dependent on the continued decline in the atmospheric concentration of ozone-depleting gases such as CFC [4]. It is concerning that a recent study by Rigby et al [4] reported that the recent slowing down of the stratospheric ozone layer recovery is counterbalanced by the continual emission of trichlorofluoromethane (CFC-11) in northeast China

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Results

Conclusion