This study looks to examine trends in the annual cycle of Mesoscale Convective System (MCS) activity over Equatorial Africa and the Sahelian region to the north. Convective precipitation features such as these are a vital source of rainfall for this region. Improvements in the understanding and forecasting of these convective systems could lead to improvements in water resources throughout the region.
5 years of data (1998-2003, 2001 exempt) from the University of Utah Precipitation Feature Database are analyzed over Central Africa. Trends of MCC activity are examined to establish a seasonal cycle of activity and to identify signals that may identify wet/dry years. To do this, the overall region of study is split into 3 separate sub-regions based on location and precipitation schemes. Each of these regions is then investigated independently to determine trends and patterns in the seasonal cycle. Aside from number of systems, parameters such as the amount of rainfall per system and lightning data are also utilized to examine the seasonal cycles of activity.
Strong trends emerge in the lightning data across the whole of central Africa, that link convection to topography across the region. As convection is initiated in the early afternoon hours, it is strongly correlated with regions of highlands across central Africa. From which point the cells intensify and concentrate over the lowland forests of the central African Basin. Orgainized convective features also favor certain highland features as well. Maxima in these features are noted in the Cameroon Highlands as well as the highlands to the west of the Great Rift Valley. Seasonal cycles in activity display a pattern that follows the maximum in solar insolation over each sub-region. A maximum in activity in each region is seen with this occurrence, including bi-modal trends in the regions closest to the equator. Also of note is a seemingly strong connection in the southern region to the size/strength of the southern branch of the African Easterly Jet. This relationship could prove to be a strong predictor of monthly rainfall for this region.
