Ethiopian Seasonal Rainfall Variability and Prediction Using Canonical Correlation Analysis (CCA)

Abstract

Because Ethiopia’s economy is mainly dependent on rain-fed agriculture, the failure or the goodness of seasonal rainfall is incredibly decisive the country’s socio economic functioning- in particular, food production. As a result, the reliable seasonal rainfall prediction would have several advantages for agricultural activities, water management, health (Malaria control) and drought related disaster mitigation. In this paper an attempt is made to study the variability and predictability of two Ethiopian rainy seasons using statistical methods. Canonical Correlation Analysis (CCA) applied to analyze and predict seasonal rainfall over Ethiopia using global sea surface temperature (SST) predictor data and historical monthly total Ethiopian rainfall and merged both satellite and rain gauge rainfall data predictand data. It is found that in general, ENSO is the main source of predictive skill for Ethiopian seasonal rainfall. This is the case for both the Belg (small rainy season) from February to May and Kiremt (main rainy season) from June to September, during which other, more regional SST in the Atlantic and Indian Ocean also contribute. The objective approach provided by the CAA approach resulted in higher mean skill than the more subjective methods used traditionally by the Ethiopian National Meteorological Agency (NMA) since the late 1980’s.