Satellite-Based Nowcasting of West African Mesoscale Storms Has Skill at up to 4-h Lead Time

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 similar to 6-h lead time). In this paper a freely available nowcasting package, pySTEPS, is used to assess the potential to provide nowcasts of satellite-derived convective rain rate for West Africa. By analyzing a large number of nowcasts, we demonstrate that a simple approach of optical flow can have useful skill at 2-h lead time on a 10-km scale and 4-h lead time at larger scales (200 km). A diurnal variation in nowcast skill is observed, with the worst-performing nowcasts being those that are initialized at 1500 UTC. Comparison with existing nowcasts is presented. Such nowcasts, if implemented operationally, would be expected to have significant benefits. Significance StatementA freely available, easy-to-use nowcasting package has been applied to satellite-retrieved rainfall rates for West Africa, and extrapolations have useful skill at up to 4 h of lead time.

Automated summary

The paper investigates the potential of using the freely available nowcasting package pySTEPS to provide short-term nowcasts of satellite-derived convective rain rate in West Africa. The analysis shows that a simple optical flow approach can achieve useful skill in 2-hour lead time forecasts at a 10-km scale and 4-hour lead time forecasts at larger scales (200 km). The study also reveals a diurnal variation in nowcast skill, with the worst-performing forecasts being those initialized at 1500 UTC. Comparison with existing nowcasts is presented, suggesting significant benefits if these nowcasts are implemented operationally.