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Prediction of North Atlantic Oscillation Index with Convolutional LSTM Based on Ensemble Empirical Mode Decomposition

1,†, 1,†, 1,*,†, 1,† and 2
1
School of Software Engineering, Tongji University, Shanghai 201804, China
2
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Atmosphere 2019, 10(5), 252; https://doi.org/10.3390/atmos10050252
Received: 11 March 2019 / Revised: 23 April 2019 / Accepted: 30 April 2019 / Published: 7 May 2019
(This article belongs to the Section Climatology and Meteorology)
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Abstract

The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is vital to predict NAO events. Since the NAO event can be quantified by the NAO index, an effective neural network model EEMD-ConvLSTM, which is based on Convolutional Long Short-Term Memory (ConvLSTM) with Ensemble Empirical Mode Decomposition (EEMD), is proposed for NAO index prediction in this paper. EEMD is applied to preprocess NAO index data, which are issued by the National Oceanic and Atmospheric Administration (NOAA), and NAO index data are decomposed into several Intrinsic Mode Functions (IMFs). After being filtered by the energy threshold, the remaining IMFs are used to reconstruct new NAO index data as the input of ConvLSTM. In order to evaluate the performance of EEMD-ConvLSTM, six methods were selected as the benchmark, which included traditional models, machine learning algorithms, and other deep neural networks. Furthermore, we forecast the NAO index with EEMD-ConvLSTM and the Rolling Forecast (RF) and compared the results with those of Global Forecast System (GFS) and the averaging of 11 Medium Range Forecast (MRF) model ensemble members (ENSM) provided by the NOAA Climate Prediction Center. The experimental results show that EEMD-ConvLSTM not only has the highest reliability from evaluation metrics, but also can better capture the variation trend of the NAO index data. View Full-Text
Keywords: neural network; LSTM; ConvLSTM; EEMD; NAO; rolling forecast neural network; LSTM; ConvLSTM; EEMD; NAO; rolling forecast
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Yuan, S.; Luo, X.; Mu, B.; Li, J.; Dai, G. Prediction of North Atlantic Oscillation Index with Convolutional LSTM Based on Ensemble Empirical Mode Decomposition. Atmosphere 2019, 10, 252.

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