Ensemble forecast of an index of the Madden Julian Oscillation using a stochastic weather generator based on analogs of Z500

The Madden-Julian Oscillation (MJO) is one of the main sources of sub-seasonal atmospheric predictability in the Tropical region. The MJO affects precipitation over highly populated areas, especially around Southern India. Therefore, predicting its phase and intensity is a scientific challenge of high societal impact.

Indices of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2 indices). The amplitude and phase of the MJO are derived from those indices. Our challenge is to forecast these two indices on a sub-seasonal timescale. This study aims to provide an ensemble forecast of MJO indices from analogs of the atmospheric circulation, computed from the geopotential at 500 hPa (Z500) by using a stochastic weather generator (SWG). The SWG is based on the random sampling of circulation analogs, which is a simple form of machine learning simulation.

We generate an ensemble of 100 members for the MJO amplitude and the RMMs for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using respectively probabilistic (CRPSS) and deterministic skill scores (correlation and RMSE). We found that a reasonable forecast could be achieved for 40-day lead times for the different seasons. We compare our SWG approach with other forecasts of the MJO mainly with the ECMWF forecast and machine learning forecast. We found that the SWG has reliable forecast skills compared to other forecasts in particular for lead times up to 20 days.