Satellite-based nowcasting of West African mesoscale storms has skill at up to four hours lead time

Abstract The ability to predict heavy rain and floods in Africa is urgently needed to reduce the socioeconomic costs of these events, and increase resilience as climate changes. Numerical weather prediction in this region is challenging and attention is being drawn to observationally-based methods of providing short-term nowcasts (up to ∼6 hours lead time). In this paper a freely-available now-casting package, pySTEPS, is used to assess the potential to provide nowcasts of satellite-derived convective rain rate for West Africa. By analysing a large number of nowcasts, we demonstrate that a simple approach of “optical flow” can have useful skill at 2 hours lead time on a 10 km scale, and 4 hours at larger scales (200 km). A diurnal variation in nowcast skill is observed, with the worst-performing nowcasts being those that are initialised at 15Z. Comparison with existing nowcasts is presented. Such nowcasts, if implemented operationally, would be expected to have significant benefits.