Abstract
El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.
Highlights
ENSO is a combination of El Niño and Southern Oscillation
We introduce an unsupervised learning domain method to predict ENSO and propose Dense Convolution-Long Short-Term Memory (DC-LSTM) to predict sea surface temperature, the nino3.4 index
The observation at any time can be represented by a tensor X ∈ R P× M× N, where M × N represents the spatial region, and P is the number of meteorological factors
Summary
ENSO is a combination of El Niño and Southern Oscillation. The former is an abnormal warming phenomenon of sea surface temperature in the tropical Pacific, and the latter describes a seesaw fluctuation in sea-level atmospheric pressures over the southernPacific and Indian oceans. ENSO is a combination of El Niño and Southern Oscillation. The former is an abnormal warming phenomenon of sea surface temperature in the tropical Pacific, and the latter describes a seesaw fluctuation in sea-level atmospheric pressures over the southern. Both phenomena have obvious periodicity, occurring once every four years on average. The northern and central parts of Peru were plagued by rainstorms, while South Asia and northern Africa were dry and rainless. This brought huge economic losses to local agriculture and fisheries [2,3]. The international mainstream standard takes the regional sea surface temperature anomaly as the basic monitoring index
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