Evolution of the NMME Rainfall Seasonal Forecasting over Central Africa

Abstract

The North American Multi-Model Ensemble (NMME) has grown into a fully developed scientific database for seasonal and sub-seasonal climate forecasts, progressing prediction from global to regional scales. The NMME has continuously developed, with new models replacing old ones; it is hypothesized that this development will generate more accurate forecasts over time. However, to date, this hypothesis has not been verified in Central Africa (CA). This study investigates the hypothesis that the skill of NMME models will increase as the forecasting system advances, focusing on rainfall in CA. The study is conducted for the four configuration (phases) of NMME models, from the oldest to the most recent. The analyses are performed with Short Lead (SL) time and Long Lead (LL) time hindcasts very coherent with the perspectives of the CA. The results show from configuration 1 (phase 1) to configurations 4 (phase 4), the NMME models reasonably replicate the spatial structures in the seasonal rainfall climatology of the observations with a remarkable bias at LL. The mean absolute error and root mean square difference reveal small but incremental improvements in the prediction skills of NMME models from phase 1 to phase 4. The Pearson coefficient (r) increased in SL by about 1%, i.e., from 0.94 to 0.95 during June–August (JJA) season and about 4% during the September–November (SON), i.e., from [Formula: see text] in phase 1 to [Formula: see text] in phase 4, about 3% from phase 1 to phase 4 during the March–May (MAM). The categorical scores show that the Probability of Detection (POD) and False Alarm (FAR) increased very slightly from phase 1 to phase 4, but is it noted that the different combinations of the NMME forecasting system present difficulties in predicting rainy and dry events. It should be added that by introducing newer models into a multi-model ensemble as they are developed, and by eliminating older models, small skill gains are observed in the NMME forecasting system in CA.