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

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 ca...

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Bibliographic Details
Published in:Atmosphere
Main Authors: Shijin Yuan, Xiaodan Luo, Bin Mu, Jing Li, Guokun Dai
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
neural network
LSTM
ConvLSTM
EEMD
NAO
rolling forecast
Meteorology. Climatology
QC851-999
North Atlantic
North Atlantic oscillation
Online Access:https://doi.org/10.3390/atmos10050252
https://doaj.org/article/7a7ce6ebc4ca402489396a2eb407ffc0
Description
Summary: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.

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