Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Isaac Mugume
* ORCID logo ,
Michel d. S. Mesquita
,
Yazidhi Bamutaze
,
Didier Ntwali
,
Charles Basalirwa
,
Daniel Waiswa
,
Joachim Reuder
ORCID logo ,
Revocatus Twinomuhangi
,
Fredrick Tumwine
,
Triphonia Jakob Ngailo
,
Bob Alex Ogwang
Version 1 : Received: 31 October 2017 / Approved: 31 October 2017 / Online: 31 October 2017 (16:28:54 CET)

How to cite: 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. Preprints 2017, 2017100199. https://doi.org/10.20944/preprints201710.0199.v1 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. Preprints 2017, 2017100199. https://doi.org/10.20944/preprints201710.0199.v1

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–Janji´c (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 (EM:10.02; KF:23.96; BMJ:26.04; GF:25.85; G3:24.07; NT:29.13 & GD:26.27) and a better bias (EM:-1.28; KF:-1.62; BMJ:-4.04; GF:-3.90; G3:-3.62; NT:-5.41 & GD:-4.07). The EMA and MAEM presented 13 out of 21 stations & 17 out of 21 stations respectively with smaller RMSE compared to EM thus demonstrating additional improvement in predictive performance. The MAEM is a new approach proposed and described in the study.

Keywords

Ensemble mean; Analogue ensemble mean; Multi–member analogue ensemble mean; Quantitative rainfall prediction

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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