A statistical extended-range tropical forecast model based on the slow evolution of the Madden-Julian oscillation

In this study, a statistical model is developed that exploits the slow evol ution of the Madden-Julian oscillation (MJO) to predict tropical rainfall v ariability at long lead times (i.e., 5-20 days). The model is based on a fi eld-to-field decomposition that uses previous and present pentads of outgoi ng longwave radiation (OLR: predictors) to predict future pentads of OLR (p redictands). The model was developed using 30-70-day bandpassed OLR data fr om 1979 to 1989 and validated on data from 1990 to 1996. Far the validation period, the model exhibits temporal correlations to observed bandpassed da ta of about 0.5-0.9 over a significant region of the Eastern Hemisphere at lend times from 5 to 20 days, after which the correlation drops rapidly wit h increasing lend time. Correlations against observed total anomalies are o n the order of 0.3-0.5 over a smaller region of the Eastern Hemisphere. Comparing the skill values from the above OLR-based model, along with those from an identical statistical model using reanalysis-derived 200-mb zonal wind anomalies, to the skill values of 200-mb zonal wind predictions from t he National Centers for Environmental Prediction's Dynamic Extended Range F orecasts shows that the statistical models appear to perform considerably b etter. These results indicate that considerable advantage might be afforded from the further exploration and eventual implementation of MJO-based stat istical models to augment current operational long-range forecasts in the T ropics. The comparisons also indicate that there is considerably more work to be done in achieving the likely forecast potential that dynamic models m ight offer if they could suitably simulate MJO variability.