Rainfall Anomaly Prediction Using Statistical Downscaling in a Multimodel Superensemble over Tropical South America

Accurate forecasting of seasonal rainfall is important for many areas of commerce, including, but not limited to, agriculture, manufacturing, and power generation. There have been several studies addressing the predictability of rainfall variations over South America and the Amazon basin. The use of coupled atmospheric-ocean numerical models to predict these anomalies has had mixed results. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the initialization phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecasts of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of superensemble member models also allows for increased accuracy. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.