Development of Deep Convolutional Neural Network Ensemble Models for 36-Month ENSO Forecasts

Abstract

The state of the El Niño-Southern Oscillation (ENSO) has chaotic yet deterministic seasonal patterns and inter-annual fluctuations over the equatorial Pacific Ocean. ENSO has impacts and global teleconnections on regional temperature, precipitation, and mid-tropospheric atmospheric circulation and has been used as a predictor of regional weather. Despite being developed over several decades, dynamical and statistical models are still unable to reliably predict seasonal ENSO. This paper presents the unique utilization of several deep convolutional neural networks, identified preferable model parameters, and an optimized ensemble output to extend the ENSO forecast by up to 36 months in advance. While individual models performed differently depending on the forecasting lead month, the ensemble output is the only model that produces a correlation of 0.52 with an index of agreement of 0.60 for the 36th month forecast, a 4% and 7% improvement in the cumulative index of agreement and r score, respectively, over the best single model. The results demonstrate the moderate ENSO forecasting capability of the system and the next step in extending the prediction lead time to previous generations of ENSO forecasting models.

Type
Publication
Asia-Pacific Journal of Atmospheric Science
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Prof. Yunsoo Choi
Prof. Yunsoo Choi
Professor of Atmospheric Chemistry, Air-Quality Modeling, AI (Deep Learning/Machine Learning), Satellite Remote Sensing

My research interests include Atmospheric Chemistry, Air-Quality Modeling and AI (Deep Learning/ Machine Learning).

Ali Mousavinezhad
Ali Mousavinezhad
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Numerical Modeling.

Ahmed Khan Salman
Ahmed Khan Salman
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).

Delaney Nelson
Delaney Nelson
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Climate Sciences, Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).

Deveshwar Singh
Deveshwar Singh
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Climate Sciences, Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).