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Atmosphere 2018, 9(9), 328;

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda

Department of Geography, Geoinformatics and Climatic Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda
Future Solutions, Håvikbrekka 92, 5440 Mosterhamn, Norway
Uni Research Climate, Bjerknes Centre for Climate Research, Jahnebakken 5, Bergen 5007, Norway
Institute of Atmospheric Physics, Laboratory for Middle Atmosphere and Global Environmental Observation, University of Chinese Academy of Sciences, Beijing 100029, China
Geophysical Institute, University of Bergen, Allegaten 70, 5007 Bergen, Norway
Department of General Studies, Dar es Salaam Institute of Technology, P. O. Box 2958, Dar–es–Salaam, Tanzania
Uganda National Meteorological Authority, P. O. Box 7025, Kampala, Uganda
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 25 December 2017 / Revised: 26 March 2018 / Accepted: 7 April 2018 / Published: 22 August 2018
(This article belongs to the Special Issue Precipitation Variability and Change in Africa)
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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. View Full-Text
Keywords: ensemble mean; analogue ensemble mean; multi–member analogue ensemble mean; quantitative rainfall prediction ensemble mean; analogue ensemble mean; multi–member analogue ensemble mean; quantitative rainfall prediction

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Mugume, I.; Mesquita, M.D.S.; Bamutaze, Y.; Ntwali, D.; Basalirwa, C.; Waiswa, D.; Reuder, J.; Twinomuhangi, R.; Tumwine, F.; Jakob Ngailo, T.; Ogwang, B.A. Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda. Atmosphere 2018, 9, 328.

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