Ensemble Meritocracy on Madden–Julian Oscillation Predictions: Elites Chosen by AI Beat the Mean

Accurate Madden–Julian oscillation (MJO) prediction is important for estimating the magnitude of and preparing for upcoming extreme climate and weather events. Current methods for MJO prediction exhibit limited predictive skill due to inevitable bias. In this work, we devise an ensemble meritocracy strategy for deep learning–based bias correction for MJO prediction, namely, MJO-Net. MJO-Net consists of two modules: a stochastic MJO prediction (SMP) module, which can generate thousands of stochastic predictions, and a skill evaluation module (SEM), which selects the elite prediction members from the SMP outputs for the final prediction results. MJO-Net demonstrates competitive performances in 46-day MJO prediction compared with the long short-term memory (LSTM) and the original European Centre for Medium-Range Weather Forecasts (ECMWF) models. Our ensemble meritocracy strategy achieves higher skill than the traditional ensemble strategy, which indicates the great potential of our method in improving other ensemble-based weather and climate predictions.