Predictability of Rainfall over Equatorial East Africa in the ECMWF Raw and Post Processed Ensemble Hindcast on Short to Medium-Range Time Scales

Despite the enormous potential of precipitation forecasts to save lives and property in Africa, low skill has limited their uptake. To asses 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 ECMWF over Equatorial East Africa (EEA) against satellite (IMERG) and rain gauge observations for the period 2001–2018. The reforecasts are analysed from short to medium-range time scales and for larger temporal aggregations (48-hours and 120-hours). 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 reduction in time mismatches. However, for some regions of the study domain, the predictive performance is worse than EPC, mainly due to biases. Postprocessing the reforecasts using the new method of isotonic distributional regression (IDR, Henzi et al. 2021) considerably improves skill, increasing the number of grid-points with positive Brier skill score (continuous rank probability score) by 82% (48%) at 1-day lead-time. Overall, the study highlights the potential of the reforecasts, the spatio-temporal variation in skill and benefit of postprocessing in EEA.