Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing

Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Hölger Kantz, Marcelo Barreiro, Cristina Masoller

Earth System Dynamics · 23 August 2022 · ECMWF, Karlsruhe Institute of Technology (KIT)

In one sentence. Machine learning post-processing of ECMWF forecasts improves MJO prediction, outperforming multiple linear regression in location and intensity skill.

Abstract

Abstract. The Madden-Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10- to 90-days) time scale. An improved forecast of the MJO, may have important socioeconomic impacts due to the influence of MJO on both, tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5 weeks prediction skill, there is still room for improving the prediction. In this study we use Multiple Linear Regression (MLR) and a Machine Learning (ML) algorithm as post-processing methods to improve the forecast of the model that currently holds the best MJO forecasting performance, the European Centre for Medium-Range Weather Forecast (ECMWF) model. We find that both MLR and ML improve the MJO prediction and that ML outperforms MLR. The largest improvement is in the prediction of the MJO geographical location and intensity.

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