Predictability of Rainfall over Equatorial East Africa in the ECMWF Ensemble Reforecasts on short to medium-range time scales

Abstract

Abstract Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To assess the skill and improve the performance of the forecast, validation and postprocessing should continuously be carried out. Here, we evaluate the quality of reforecasts from the European Centre for Medium-Range Weather Forecasts over Equatorial East Africa (EEA) against satellite and rain gauge observations for the period 2001–2018. 24-hour rainfall accumulations are analysed from short to medium-range time scales. Additionally, 48- and 120-hour rainfall accumulations were also assessed. The skill was assessed using an extended probabilistic climatology (EPC) derived from the observations. Results show that the reforecasts overestimate rainfall, especially during the rain seasons and over high-altitude areas. However, there is potential of skill in the raw forecasts up to 14-day lead-time. There is an improvement of up to 30% in Brier score/continuous rank probability score relative to EPC in most areas, especially the higher-altitude regions, decreasing with lead-time. Aggregating the reforecasts enhances the skill further, likely due to a reduction in timing mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using isotonic distributional regression considerably improves skill, increasing the number of grid-points with positive Brier skill score (continuous rank probability score) by an average of 81% (91%) for lead-times 1–14 days ahead. Overall, the study highlights the potential of the reforecasts, the spatio-temporal variation in skill and benefit of postprocessing in EEA.