Improved Prediction of June to September Rainfall over Ethiopia

This study explored the predictive skill of June to September rainfall over Ethiopia using the state of the art in climate prediction sciences currently used in a number of global climate prediction centers. In addition, this study attempted to perform wide ranges of diagnostics on local, regional and global systems that strongly influence the climate patterns of Ethiopia’s June to September rainy season.

 The study was conducted by applying multivariate statistical techniques to diagnosis, and fitting appropriate models to predict seasonal rainfall performances. Data pertaining to monthly rainfall was obtained from the National Meteorological Services Agency of Ethiopia, whereas regional and global ocean and atmospheric data sets were extracted from data archives at the IRI of Columbia University.

 The results of the study indicate that Ethiopia’s June to September rainy season is highly influenced by ENSO events. As a result, the seasonal rainfall patterns can be skillfully predictable a few months in advance by using statistical models. However, an ENSO predictability barrier prevailing during the Northern Hemisphere spring imposes a major challenge to providing seasonal rainfall forecasts several months in advance based solely on ENSO parameters.