Statistical Forecasts for the Occurrence of Precipitation Outperform Global Models over Northern Tropical Africa

Short-term global ensemble predictions of rainfall currently have no skill over northern tropical Africa when compared to simple climatology-based forecasts, even after sophisticated statistical postprocessing. Here, we demonstrate that 1-day statistical forecasts for the probability of precipitation occurrence based on a simple logistic regression model have considerable potential for improvement. The new approach we present here relies on gridded rainfall estimates from the Tropical Rainfall Measuring Mission for July-September 1998-2017 and uses rainfall amounts from the pixels that show the highest positive and negative correlations on the previous two days as input. Forecasts using this model are reliable and have a higher resolution and better skill than climatology-based forecasts. The good performance is related to westward propagating African easterly waves and embedded mesoscale convective systems. The statistical model is outmatched by the postprocessed dynamical forecast in the dry outer tropics only, where extratropical influences are important.

Automated summary

Statistical forecasts based on a simple logistic regression model show considerable potential for improvement in short-term global ensemble predictions of rainfall over northern tropical Africa, outperforming climatology-based forecasts. The performance of the model is related to African easterly waves and mesoscale convective systems, with the only exception being the dry outer tropics where extratropical influences dominate.