Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.

The spatial and temporal cloud coverage derived by CloudSat, CERES, ISCCP satellite observations and their relationship with GPCP and TRMM precipitation in West, East and South of Africa were analyzed in this study. CloudSat, CERES and ISCCP show that the high spatial cloud coverage is more frequent in equatorial regions mainly due to more strong convection than other regions. CloudSat shows a low temporal cloud coverage than CERES and ISCCP which are close. Only ISCCP was used to investigate seasonal and temporal variability of different cloud types. The stratocumulus, altostratus, and cirrus clouds are the low, middle and high cloud types with high cloud coverage during JJA, JJA, MAM in West of Africa, during SON, JJA, MAM in East of Africa, and during SON, DJF, DJF in South of Africa respectively . The correlation between cloud coverage and precipitation dataset generally shows a low positive correlation in East of Africa probably due to GPCP and TRMM observations biases whereas a high positive correlation in West and South of Africa. Only middle clouds level in East of Africa, both low and middle in West of Africa show negative correlation with precipitation, whereas all cloud types level in South of Africa show a positive correlation with precipitation.