Rainfall extremes have strong connotations to socioeconomic 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. #

The atmospheric chemistry constituents of nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are associated with air pollution and climate change. In sub-Saharan Africa, a lack of sufficient ground-based and aircraft observations has, for a long time, limited the study of these species. This study thus utilized satellite observations as an alternative source of data to study the abundance of these species over the East African region. The instruments used included the Ozone Monitoring Instrument (OMI), the Atmospheric InfraRed Sounder (AIRS), and the TROPOspheric Monitoring Instrument (TROPOMI). An investigation of trends in the data series from 2005 to 2020 was carried out using the sequential Mann-Kendall test while the Pearson correlation coefficient was used to compare the data records of the instruments. The analysis revealed no trend in NO2 (p > 0.05), a decreasing trend in SO2 (p < 0.05), a decreasing trend (p < 0.05) in CO closer to the surface (850 hPa to 500 hPa) and an increasing trend (p < 0.05) in CO higher up in the atmosphere (400 hPa to 1 hPa). There is likely a vertical ascent of CO. The correlation between the instrument records was 0.54 and 0.77 for NO2 and CO, respectively. Furthermore, seasonal fires in the savanna woodlands were identified as the major source of NO2 and CO over the region, while cities such as Kampala, Nairobi, and Bujumbura and towns such as Dar es Salaam and Mombasa were identified as important NO2 hotspots. Similarly, the active volcano at Mt. Nyiragongo near Goma was identified as the most important SO2 hotspot.

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Climate change and air pollution are two interconnected daunting environmental challenges of the twenty-first century. Globally, stringent public health and environmental policies are set to mitigate the emissions of near-term climate forcers (NTCFs) because they double as air pollutants. While the global climate impact of NTCF mitigation has been investigated using coarse resolution climate models, the fine scale regional climate impacts over East Africa are not fully known. This study presents the first 2021–2055 downscaled model results of two future scenarios which both have increasing greenhouse gas emissions but with weak (SSP3-7.0) versus strong (SSP3-7.0_lowNTCF) levels of air quality control. NTCF mitigation is defined here as SSP3-7.0_lowNTCF–SSP3-7.0. The results reveal that NTCF mitigation could cause an increase in annual mean surface temperature ranging from 0.005 to 0.01 °C decade−1 over parts of Kenya, Ethiopia and Somalia. It could also cause an increase in annual mean precipitation ranging from 0.1 to 1 mm month−1 decade−1 over parts of Uganda, Kenya, Tanzania, South Sudan and Ethiopia. Majority of the precipitation increase is projected to occur during the MAM season. On the other hand, Zambia, Malawi and southern Tanzania could also experience a decrease in annual mean precipitation by up to 0.5 mm month−1 decade−1. Majority of this decrease is projected to occur during the DJF season. These findings suggest that pursuing NTCF mitigation alone while ignoring greenhouse gas emissions will cause additional climate change over East Africa. Mitigating both of them concurrently would be a better policy option.

In East Africa, biomass burning in the savanna region emits nitrogen dioxide (NO2), carbon monoxide (CO), and aerosols among other species. These emissions are dangerous air pollutants which pose a health risk to the population. They also affect the radiation budget. Currently, limited academic research has been done to study their spatial and temporal distribution over this region by means of numerical modeling. This study therefore used the Weather Research and Forecasting model coupled with chemistry (WRF-chem) to simulate, for the first time, the distribution of NO2 during the year 2012 and CO during the period June 2015 to May 2016 over this region. These periods had the highest atmospheric abundances of these species. The model’s performance was evaluated against satellite observations from the Ozone Monitoring Instrument (OMI) and the Measurement of Pollution in the Troposphere (MOPITT). Three evaluation metrics were used, these were, the normalized mean bias (NMB), the root mean square error (RMSE) and Pearson’s correlation coefficient (R). Further, an attempt was made to reduce the bias shown by WRF-chem by applying a deep convolutional autoencoder (WRF-DCA) algorithm and linear scaling (WRF-LS). The results showed that WRF-chem simulated the seasonality of the gases but made below adequate estimates of the gas abundances. It overestimated NO2 and underestimated CO throughout all the seasons. Overall, for NO2, WRF-chem had an average NMB of 3.51, RMSE of 2 × 1015 molecules/cm2 and R of 0.44 while for CO, it had an average NMB of − 0.063, RMSE of 0.65 × 1018 molecules/cm2 and R of 0.13. Furthermore, even though both WRF-DCA and WRF-LS successfully reduced the bias in WRF-chem’s NO2 estimates, WRF-DCA had a superior performance compared to WRF-LS. It reduced the NMB by an average of 3.2 (90.2%). Finally, this study has shown that deep learning has a strong ability to improve the estimates of numerical models, and this can be a cue to incorporate this approach along other stages of the numerical modeling process.