Improving the prediction of the Madden-Julian Oscillation of the ECMWF
model by post-processing
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 and an Artificial Neural Network as
post-processing methods to improve one of the currently best dynamical
models developed by the European Centre for Medium-Range Weather
Forecast (ECMWF). We show that the post-processing with the machine
learning algorithm employed leads to an improvement of the MJO
prediction. The largest improvement is in the prediction of the MJO
geographical location and intensity.