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

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.

Ensemble forecast of an index of the Madden Julian Oscillation using a stochastic weather generator based on circulation analogs

“…In addition, we compared quantitatively the SWG forecast with the ECMWF forecast Figure 10. The ECMWF reforecats were taken from (Silini et al, 2022). We found that the ECMWF forecast has highest correlation until 20 days compared to the SWG forecast.…”

“…However, for lead times of 20 days the RMSE of ECMWF forecast coincides with the RMSE of the SWG, which shows the improvement of the SWG forecast to lead time above 20 days. The skill scores of the ECMWF (COR and RMSE) (Silini et al, 2022) are computed for each lead time, which is different from our way of computing the skill score (considering the average lead time). Of course, this comparison was made to check the performance of our forecast and not to say that the SWG model can replace a numerical prediction.…”