Spatiotemporal Model Based on Deep Learning for ENSO Forecasts
In one sentence. DC-LSTM, a dense-convolution LSTM, forecasts ENSO Nino3.4 up to 20 months ahead with higher skill than a dynamical model over 1994-2010.
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.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, CNN / U-Net
- Time scale
- Seasonal to annual, 2 weeks–2 months
- Domain
- Global
- Data
- CMIP / climate model output, ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.3390/atmos12070810 OpenAlex:W3177225616
- Added
- 2026-10-22
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