A new paper supported with funding from the Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) Program enhances the prediction accuracy of El Niño Southern Oscillation (ENSO) using deep learning techniques. Lead author Chibuike Ibebuchi, a postdoctoral researcher at Kent State University, works with MAPP-funded scientist Cameron Lee on a project aimed at monitoring hazardous temperature conditions in North America. The research, published in Climate Dynamics, demonstrates an innovative approach combining two artificial intelligence methods (Autoencoder neural networks and Long Short-Term Memory deep learning models) to predict a key ENSO indicator up to a year and a half in advance. The method proved capable of predicting extreme ENSO events at about 85% accuracy, which is a big deal for helping us prepare for extreme weather.
Improved ENSO prediction is critical for understanding and mitigating the impacts of this climate phenomenon on global weather patterns, ocean conditions, agriculture, and economies. Traditional methods for forecasting ENSO have limitations, particularly on lead times and during transitional seasons. The deep learning techniques used in this study present a promising approach to enhance prediction accuracy and better capture complex temporal patterns, providing a valuable tool for various forecasting applications beyond ENSO prediction.
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Image credit: NOAA PSL