On the next generation (NextGen) seasonal prediction system to enhance climate services over Ethiopia

Abstract In their recent seasonal forecast guidance, the World Meteorological Organization recommended using an objective seasonal forecast system that includes a traceable, reproducible, and well-documented set of steps. Such a forecast system is the backbone of any successful climate service, which should provide advanced warning to government, industry, and communities, and thereby help reduce the impacts of adverse climatic conditions. In this study, we present the Next Generation (NextGen) seasonal forecast system which was recently adopted by the National Meteorological Agency (NMA) of Ethiopia. NextGen is based on a calibrated multi-model ensemble (CMME) approach that uses state-of-the-art general circulation models (GCM) from the North American Multi-Model Ensemble project. A canonical correlation analysis-based regression is used to calibrate the predictions from the GCMs against observations. The calibrated GCMs are then combined with equal weight to make final CMME predictions. A hindcast skill assessment of the CMME predictions has been depicted in this study for three rainy seasons in Ethiopia, namely Belg: Feb to May, Kiremt: Jun to Sep, and Bega: Oct to Jan. Over the region, the resulting forecasts are characterized by moderate skill at lead-1 for all three seasons. NextGen forecasting system shows that Bega rain-benefiting areas demonstrate the highest deterministic and probabilistic skill when compared to Kiremt and Belg rainy areas. The real-time experimental forecast of Kiremt2020 was conducted using NextGen by NMA and proved quite successful in capturing precipitation anomalies comparable to observations.