Abstract
Rainfall extremes have strong connotations to socio-economic activities and human well-being in Uganda’s Lake Victoria Basin (LVB). Reliable prediction and dissemination of extreme rainfall events are therefore of paramount importance to the region’s development agenda. The main objective of this study was to contribute to the prediction of rainfall extremes over this region using a numerical modelling approach. The Weather Research and Forecasting (WRF) model was used to simulate a 20-day period of extremely heavy rainfall that was observed in the March to May season of 2008. The underlying interest was to investigate the performance of different combinations of cumulus and microphysical parameterization along with the model grid resolution and domain size. The model output was validated against rainfall observations from the Tropical Rainfall Measuring Mission (TRMM) using 5 metrics; the rainfall distribution, root mean square error, mean error, probability of detection and false alarm ratio. The results showed that the model was able to simulate extreme rainfall and the most satisfactory skill was obtained with a model setup using the Grell 3D cumulus scheme combined with the SBU_YLin microphysical scheme. This study concludes that the WRF model can be used for simulating extreme rainfall over western LVB. In the other 2 regions, central and eastern LVB, its performance is limited by failure to simulate nocturnal rainfall. Furthermore, increasing the model grid resolution showed good potential for improving the model simulation especially when a large domain is used.
Highlights
Rainfall is one of the important weather elements and its forecasting is crucial to society (Gouda et al, 2018; Mehr et al, 2019)
The Advanced Research WRF (ARW) solver was used with a combination of physical parameterization (Table 2) coupled with static land-use data based on Moderate Resolution Imaging Spectroradiometer (MODIS) with 21 land-use categories (Friedl et al, 2002) and a special lake surface representation
A one-sample t-test was used to test whether the skill scores generated by model runs E1 to E9 significantly differ from those of the control run and the results show that at a 95% level of confidence, changing the microphysical and cumulus parameterization schemes from the default significantly altered the model bias (ME, p < 0.05), detection ability (POD, p < 0.05) and proportion of false alarms (FAR, p < 0.05)
Summary
Rainfall is one of the important weather elements and its forecasting is crucial to society (Gouda et al, 2018; Mehr et al, 2019). Extremely heavy rainfall plays a principal role in soil erosion (Bamutaze et al, 2017), flooding (Lwasa, 2010), landslides (Mugagga et al, 2012) and transmission of waterborne diseases (Cann et al, 2013). The Uganda National Meteorological Authority has integrated NWP modelling in their weather forecasting service to support the generation of short and medium range forecasts and their model of choice is the Weather Research and Forecasting (WRF) model. This model’s user community is on the rise signifying the confidence that scientists have in it (Warner, 2011). It can be set-up for operational use since its outputs can be autonomously analyzed using existing state-of-the-science tools such as the Atmospheric Model Evaluation Tool (Appel et al, 2011)
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