ENSO dataset & comparison of deep learning models for ENSO forecasting

Abstract

AbstractForecasting the El Nino-Southern Oscillation (ENSO) is a challenging task in climatology. It is one of the main factors responsible for the Earth’s interannual climatic fluctuation and can result in many climatic anomalies. The impacts include natural disasters (floods, droughts), low & high agriculture yields, price fluctuation, energy demand, availability of water resources, animal movement, and many more. This study presents a comprehensive ENSO dataset containing standard indicators and other relevant data to facilitate ENSO analysis and forecasting. To ensure the dataset's validity and reliability, we performed extensive data analysis and trained four basic deep models for time series forecasting (i.e. CNN, RNN, LSTM, and hybrids). The data analysis confirmed the accuracy and suitability of the dataset for ENSO forecasting. The LSTM model achieved the best fit to the data, leading to superior performance in forecasting ENSO events.