This paper attempts to delineate Uganda into climatological rainfall zones using the method of principal component analysis (PCA). Monthly rainfall records from 102 Uganda rain-gauge stations for the years 1940-1975 inclusive are used in the study.The rotated dominant PCA modes were used to delineate the temporal r a f i l l characteristics into homogeneous spatial regions. Statistical and other methods were then used to determine the physical reality of the spatial patterns of the delineated zones of the rainfall network in describing the climatological rainfall patterns of Uganda.The PCA varimax rotated solutions showed that for the climatological data a maximum of four PCA modes, accounting for about 65 per cent of annual rainfall variance, were significant. However, a maximum of 16 PCA modes, accounting for over 81 per cent of the rainfall variance were obtained with the seasondmonthly records. The PCA regional patterns delineated Uganda into 14 homogeneous climatological zones, which were also indicated by the spatial patterns of the physical features of Uganda and other tests. The derived rainfall zones would be useful in the planning and management of rainfall dependent activities in the country.

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Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March-May season using the Mann-Kendall (MK) trend test and simple linear regression (SLR) over the period 2000-2015. Results showed an increasing trend of both dekadal rainfall amount and rain days (third and seventh dekads). The light rain days (SLR = 0.181; MK = 0.350) and wet days (SLR = 0.092; MK = 0.118) also depict an increasing trend. The rate of increase of light rain days and wet days during the third dekad (light rain days: SLR = 0.020; MK = 0.279 and wet days: SLR = 0.146; MK = 0.376) was slightly greater than during the seventh dekad (light rain days: SLR = 0.014; MK = 0.018 and wet days: SLR = 0.061; MK = 0.315) dekad. Seventy-four percent accounted for 2-4 consecutive dry days, but no significant trend was detected. The extreme rainfall was increasing over the third (MK = 0.363) and seventh (MK = 0.429) dekads. The rainfall amount and rain days were highly correlated (r: 0.43-0.72).

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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.

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Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

; Tel.: +256-701-349477 ‡ These authors contributed equally to this work. Abstract:Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. This study is aimed at improving rainfall prediction using ensemble methods. It first assesses 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 are generated from the three best performing convective schemes (i.e. KF, GF & G3). The performance of three ensemble methods (i.e. ensemble mean (EM); ensemble mean analogue (EMA) and multi-member analogue ensemble (MAEM)) is also analyzed using the RMSE and ME. The EM presented a smaller RMSE compared to individual schemes