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.
Highlights
The North Atlantic Oscillation (NAO) is a large-scale see-saw structure for changing atmospheric mass between the sub-tropical high (Azores high) and the sub-polar low (Iceland low) in the NorthAtlantic
An effective neural network model, Ensemble Empirical Mode Decomposition (EEMD)-ConvLSTM, which is based on ConvLSTM with EEMD, was established aiming at predicting the fluctuations of the NAO index
Index data were preprocessed by EEMD. They were decomposed into several Intrinsic Mode Functions (IMFs), and these IMFs were filtered by the energy threshold, the remaining IMFs were used to reconstruct new NAO
Summary
The North Atlantic Oscillation (NAO) is a large-scale see-saw structure for changing atmospheric mass between the sub-tropical high (Azores high) and the sub-polar low (Iceland low) in the North. It is the most prominent low-frequency dipole mode of atmospheric variability in the mid-to-high latitudes of the Northern Hemisphere, which has been recognized to have a profound effect on regional weather and climate, and the hemispheric-scale circulation [1,2] As such, it has a large impact on precipitation, ecology, and fisheries in the North Atlantic and Europe and can affect monsoons and precipitation in East Asia [3]. The method of predicting the NAO index is mainly based on the numerical models. Kim et al [17] utilized ConvLSTM [18] to predict rainfall, and the experiment results were superior to statistic methods According to these studies, the neural network is a feasible approach to perform climate prediction, and it mostly outperforms the statistic models [19,20,21].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
