Abstract
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.
Highlights
Rainfall is a key climatic element that has consequences on key production sectors including agriculture [1,2], health [3], electricity generation [4] and water resources [5,6] among others.Over Eastern Africa, the rainfall distribution and quantity is influenced by many factors such as theInter–Tropical Convergence Zone, El Niño/La Niña episodes, Indian Ocean Dipole and extra-tropical weather systems [7,8]
The study investigated the potential for improving quantitative rainfall prediction using ensemble methods
It considered 18 ensemble members which were generated from time–lagging by 6–hours (00UTC, 06UTC, 12UTC and 18UTC); perturbing the convective parameterization schemes by varying the entrainment rate by ±25% and combining the simulations from the physical parameterization schemes used
Summary
Rainfall is a key climatic element that has consequences on key production sectors including agriculture [1,2], health [3], electricity generation [4] and water resources [5,6] among others.Over Eastern Africa, the rainfall distribution and quantity is influenced by many factors such as theInter–Tropical Convergence Zone, El Niño/La Niña episodes, Indian Ocean Dipole and extra-tropical weather systems [7,8]. The spatial and temporal variability of rainfall makes its quantitative prediction a challenge [8,9]. According to He et al [5] and Jie et al [10], rainfall can be predicted quantitatively up to 7 days using models but the prediction accuracy degrades with increasing lead time [11]. Some of the scientific ways of predicting rainfall quantitatively have been suggested such as: the use of radar [8] and the use of Numerical Weather Prediction (NWP) models [6]. The use of radar is considered superior at short–range forecasts due to better spatial representation and assimilation of the initial rainfall estimates. An additional scientific method is the use of statistical models such as regression [9]
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