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

Abstract

; 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

Analysis of WRF Simulation Skill for Seasonal Rainfall: A Case Study of 2015 September–November Season

“…Eastern Uganda experiences bi-modal rainfall seasons that is the March to May and September to November seasons each year [31][32][33]. The rainfall received in the region just like the other parts of the country, is greatly influenced by the inter-tropical convergence zone [21,33]; the monsoon wind circulations [32]; and the local influences that is to say the land and sea breezes due to the presence of lake Victoria, orographic lifting due to the presence of Mt. Elgon [21].…”

“…The rainfall received in the region just like the other parts of the country, is greatly influenced by the inter-tropical convergence zone [21,33]; the monsoon wind circulations [32]; and the local influences that is to say the land and sea breezes due to the presence of lake Victoria, orographic lifting due to the presence of Mt. Elgon [21]. The geographical details of the study sites are presented using Table 1 Table 1.…”

“…The parent domain was at 110 Km horizontal resolution centered at 0.73 o N and 21.0 E . The parent domain had 85 × 85 grid points sufficient to cover the whole of Africa and capturing the large-scale systems influencing rainfall over the study region such as the sub-tropical high-pressure systems and the inter-tropical convergence zone [21,32]. The nested domain was at 22 Km horizontal resolution spanning 136 × 116 grid points covering most parts of the equatorial regions of Africa so as to capture the systems that affect the moisture influx over Uganda [25] such as Congo air mass from the Congo basin in West and the moist currents from the Mozambique channel in the Indian Ocean [32].…”

“…Among the climate sensitive sectors which are affected by precipitation, the agricultural sector is most prone to the variability in the precipitation amounts received globally [13,17,21]. In most parts of Africa and particularly East Africa, agriculture being predominantly rain-fed, it makes it very vulnerable to the changes in precipitation [21][22][23]. Thus, the uneven seasonal distribution of precipitation may expose crops to severe intra-seasonal dry and wet spells [17,21].…”

“…In most parts of Africa and particularly East Africa, agriculture being predominantly rain-fed, it makes it very vulnerable to the changes in precipitation [21][22][23]. Thus, the uneven seasonal distribution of precipitation may expose crops to severe intra-seasonal dry and wet spells [17,21]. This in return affects the yields at the end of the season [24][25][26][27].…”