Rainfall types over southern West Africa: Objective identification, climatology and synoptic environment

Southern West Africa (SWA) is characterised by a wide range of rainfall types, the relative importance of which have never been quantified on a regional level. Here, we use 16 years of three-dimensional reflectivity data from the Tropical Rainfall Measuring Mission-Precipitation Radar (TRMM-PR) to objectively distinguish between seven different rainfall types in three subregions of SWA. Highly organized Mesoscale Convective System (MCS) events are the dominating rain-bearing systems in SWA. They tend to occur in highly sheared environments as a result of mid-level northeasterlies ahead of a cyclonic vortex. Their contribution to annual rainfall decreases from 71% in the Soudanian to 56% in the coastal zone. MCSs in SWA also propagate slower than their Sahelian counterparts and occur predominantly at the start of the first coastal rainy season. However, in terms of numbers, about 90% of rainfall systems are weakly organized classes, particularly small-sized, highly reflective and moderately deep (40 dBZ at altitude <10 km) systems. Contrary to MCSs, less organized convection typically occurs during and after the passage of a cyclonic vortex within a regime of deep westerly anomalies, low wind shear and low to moderate CAPE (convective available potential energy), bearing some resemblance to what has been termed monsoon or vortex rainfall. Combining TRMM-PR rainfall system identification with infrared-based cloud tracking reveals that organized convection over SWA typically lasts for more than >9 h, whereas less intense rainfall types tend to be short-lived, diurnal phenomena. This novel approach stresses the relevance of mid-level (wave) disturbances on the type and lifetime of convective systems and thereby their regionally, seasonally and diurnally varying contribution to rainfall amount. The present study suggests further investigations into the character of the disturbances as well as possible implications for operational forecasting and the understanding of rainfall variability in SWA.